diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..645c8c7772e9dde2525ae45a91e18ae1d464bf15 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+banana_disease_model.keras filter=lfs diff=lfs merge=lfs -text
diff --git a/.gradio/certificate.pem b/.gradio/certificate.pem
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diff --git a/.gradio/flagged/dataset1.csv b/.gradio/flagged/dataset1.csv
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+img,Predicted Disease,timestamp
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+.gradio\flagged\img\58b6968e71c53a81b394\Bract Mosaic Virus Disease 4.jpg,Banana Healthy Leaf,2025-08-21 19:43:18.128853
+.gradio\flagged\img\94019059352474f72530\Banana-Bacterial-Wilt-or-Moko-Disease-Management.jpg,Banana Healthy Leaf,2025-08-21 20:03:36.152510
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diff --git a/README.md b/README.md
index ab1026189d4daa3c13229a3ffce8d7b35a755eb3..3b4239e8f8ae1a5d682ad4d386f0fc4d2cc63f64 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,6 @@
----
-license: unknown
----
+---
+title: Banana-Disease-Classification
+app_file: banana_gradio.py
+sdk: gradio
+sdk_version: 5.43.1
+---
diff --git a/banana-disease-classification.ipynb b/banana-disease-classification.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d67b4324a8752d655b01f2511a5669ad0163a00a
--- /dev/null
+++ b/banana-disease-classification.ipynb
@@ -0,0 +1,1771 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2025-08-21T04:42:52.960698Z",
+     "iopub.status.busy": "2025-08-21T04:42:52.960290Z",
+     "iopub.status.idle": "2025-08-21T04:42:54.851881Z",
+     "shell.execute_reply": "2025-08-21T04:42:54.851164Z",
+     "shell.execute_reply.started": "2025-08-21T04:42:52.960667Z"
+    },
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
+    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
+    "# For example, here's several helpful packages to load\n",
+    "\n",
+    "import numpy as np # linear algebra\n",
+    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
+    "\n",
+    "# Input data files are available in the read-only \"../input/\" directory\n",
+    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
+    "\n",
+    "import os\n",
+    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+    "    for filename in filenames:\n",
+    "        os.path.join(dirname, filename)\n",
+    "\n",
+    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
+    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: tensorflow in c:\\users\\adity\\miniconda3\\lib\\site-packages (2.20.0)\n",
+      "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (2.3.1)\n",
+      "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (1.6.3)\n",
+      "Requirement already satisfied: flatbuffers>=24.3.25 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (25.2.10)\n",
+      "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (0.6.0)\n",
+      "Requirement already satisfied: google_pasta>=0.1.1 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (0.2.0)\n",
+      "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (18.1.1)\n",
+      "Requirement already satisfied: opt_einsum>=2.3.2 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (3.4.0)\n",
+      "Requirement already satisfied: packaging in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (23.1)\n",
+      "Requirement already satisfied: protobuf>=5.28.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (6.32.0)\n",
+      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (2.31.0)\n",
+      "Requirement already satisfied: setuptools in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (68.2.2)\n",
+      "Requirement already satisfied: six>=1.12.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (1.16.0)\n",
+      "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (3.1.0)\n",
+      "Requirement already satisfied: typing_extensions>=3.6.6 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (4.14.1)\n",
+      "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (1.17.3)\n",
+      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (1.74.0)\n",
+      "Requirement already satisfied: tensorboard~=2.20.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (2.20.0)\n",
+      "Requirement already satisfied: keras>=3.10.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (3.11.2)\n",
+      "Requirement already satisfied: numpy>=1.26.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (1.26.4)\n",
+      "Requirement already satisfied: h5py>=3.11.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (3.14.0)\n",
+      "Requirement already satisfied: ml_dtypes<1.0.0,>=0.5.1 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorflow) (0.5.3)\n",
+      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from astunparse>=1.6.0->tensorflow) (0.41.2)\n",
+      "Requirement already satisfied: rich in c:\\users\\adity\\miniconda3\\lib\\site-packages (from keras>=3.10.0->tensorflow) (14.1.0)\n",
+      "Requirement already satisfied: namex in c:\\users\\adity\\miniconda3\\lib\\site-packages (from keras>=3.10.0->tensorflow) (0.1.0)\n",
+      "Requirement already satisfied: optree in c:\\users\\adity\\miniconda3\\lib\\site-packages (from keras>=3.10.0->tensorflow) (0.17.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow) (2.1.0)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow) (2025.8.3)\n",
+      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorboard~=2.20.0->tensorflow) (3.8.2)\n",
+      "Requirement already satisfied: pillow in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorboard~=2.20.0->tensorflow) (11.3.0)\n",
+      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorboard~=2.20.0->tensorflow) (0.7.2)\n",
+      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from tensorboard~=2.20.0->tensorflow) (3.1.3)\n",
+      "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard~=2.20.0->tensorflow) (3.0.2)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from rich->keras>=3.10.0->tensorflow) (4.0.0)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from rich->keras>=3.10.0->tensorflow) (2.19.1)\n",
+      "Requirement already satisfied: mdurl~=0.1 in c:\\users\\adity\\miniconda3\\lib\\site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.10.0->tensorflow) (0.1.2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install tensorflow\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-08T09:30:22.232722Z",
+     "iopub.status.busy": "2025-05-08T09:30:22.232078Z",
+     "iopub.status.idle": "2025-05-08T09:30:25.252680Z",
+     "shell.execute_reply": "2025-05-08T09:30:25.251864Z",
+     "shell.execute_reply.started": "2025-05-08T09:30:22.232698Z"
+    },
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: split-folders in c:\\users\\adity\\miniconda3\\lib\\site-packages (0.5.1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install split-folders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-08-21T05:42:37.259721Z",
+     "iopub.status.busy": "2025-08-21T05:42:37.259423Z",
+     "iopub.status.idle": "2025-08-21T05:42:38.827751Z",
+     "shell.execute_reply": "2025-08-21T05:42:38.826988Z",
+     "shell.execute_reply.started": "2025-08-21T05:42:37.259700Z"
+    },
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Dataset successfully split into train & val folders\n",
+      "Found 390 files belonging to 8 classes.\n",
+      "Found 152 files belonging to 8 classes.\n",
+      "✅ TensorFlow datasets loaded and ready for training.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import shutil\n",
+    "import random\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Dataset paths\n",
+    "data_dir = \"Kaggle/input/Banana Disease Recognition Dataset/Original Images/Original Images\"\n",
+    "base_dir = \"/kaggle/working/banana_split\"\n",
+    "\n",
+    "train_dir = os.path.join(base_dir, \"train\")\n",
+    "val_dir = os.path.join(base_dir, \"val\")\n",
+    "\n",
+    "# Create train and validation directories\n",
+    "os.makedirs(train_dir, exist_ok=True)\n",
+    "os.makedirs(val_dir, exist_ok=True)\n",
+    "\n",
+    "# Split each class folder into train and val\n",
+    "for cls in os.listdir(data_dir):\n",
+    "    cls_path = os.path.join(data_dir, cls)\n",
+    "    \n",
+    "    if os.path.isdir(cls_path):  # Only process folders\n",
+    "        # Create class subfolders inside train and val\n",
+    "        os.makedirs(os.path.join(train_dir, cls), exist_ok=True)\n",
+    "        os.makedirs(os.path.join(val_dir, cls), exist_ok=True)\n",
+    "\n",
+    "        # ✅ Get only files (exclude subfolders)\n",
+    "        files = [f for f in os.listdir(cls_path) if os.path.isfile(os.path.join(cls_path, f))]\n",
+    "        random.shuffle(files)\n",
+    "\n",
+    "        # 80/20 train-val split\n",
+    "        split_idx = int(0.8 * len(files))\n",
+    "        train_files = files[:split_idx]\n",
+    "        val_files = files[split_idx:]\n",
+    "\n",
+    "        # Copy training images\n",
+    "        for f in train_files:\n",
+    "            shutil.copy(os.path.join(cls_path, f), os.path.join(train_dir, cls, f))\n",
+    "\n",
+    "        # Copy validation images\n",
+    "        for f in val_files:\n",
+    "            shutil.copy(os.path.join(cls_path, f), os.path.join(val_dir, cls, f))\n",
+    "\n",
+    "print(\"✅ Dataset successfully split into train & val folders\")\n",
+    "\n",
+    "# Parameters for dataset loading\n",
+    "img_size = (224, 224)\n",
+    "batch_size = 32\n",
+    "\n",
+    "# Load training dataset\n",
+    "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
+    "    train_dir,\n",
+    "    image_size=img_size,\n",
+    "    batch_size=batch_size\n",
+    ")\n",
+    "\n",
+    "# Load validation dataset\n",
+    "val_ds = tf.keras.utils.image_dataset_from_directory(\n",
+    "    val_dir,\n",
+    "    image_size=img_size,\n",
+    "    batch_size=batch_size\n",
+    ")\n",
+    "\n",
+    "# Prefetch for performance\n",
+    "train_ds = train_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n",
+    "val_ds = val_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n",
+    "\n",
+    "print(\"✅ TensorFlow datasets loaded and ready for training.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-08T09:06:24.296781Z",
+     "iopub.status.busy": "2025-05-08T09:06:24.296461Z",
+     "iopub.status.idle": "2025-05-08T09:06:26.416883Z",
+     "shell.execute_reply": "2025-05-08T09:06:26.416315Z",
+     "shell.execute_reply.started": "2025-05-08T09:06:24.296762Z"
+    },
+    "trusted": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-08T09:06:31.648539Z",
+     "iopub.status.busy": "2025-05-08T09:06:31.647912Z",
+     "iopub.status.idle": "2025-05-08T09:06:32.129772Z",
+     "shell.execute_reply": "2025-05-08T09:06:32.129207Z",
+     "shell.execute_reply.started": "2025-05-08T09:06:31.648513Z"
+    },
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'c:\\\\kaggle\\\\working\\\\banana_split.zip'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output_1 = \"/kaggle/working/banana_split\"\n",
+    "shutil.make_archive(output_1,'zip',output_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-08-21T05:24:10.228232Z",
+     "iopub.status.busy": "2025-08-21T05:24:10.227970Z",
+     "iopub.status.idle": "2025-08-21T05:24:10.300718Z",
+     "shell.execute_reply": "2025-08-21T05:24:10.300191Z",
+     "shell.execute_reply.started": "2025-08-21T05:24:10.228215Z"
+    },
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "\n",
+    "train_datagen=ImageDataGenerator(\n",
+    "    rescale=1./255,\n",
+    "    rotation_range=90,\n",
+    "    horizontal_flip=True,\n",
+    "    vertical_flip=True,\n",
+    "    zoom_range=0.2\n",
+    ")\n",
+    "test_datagen=ImageDataGenerator(\n",
+    "    rescale=1./255\n",
+    ")\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-08-21T05:43:22.530371Z",
+     "iopub.status.busy": "2025-08-21T05:43:22.530073Z",
+     "iopub.status.idle": "2025-08-21T05:43:22.544287Z",
+     "shell.execute_reply": "2025-08-21T05:43:22.543586Z",
+     "shell.execute_reply.started": "2025-08-21T05:43:22.530351Z"
+    },
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 390 images belonging to 8 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_generator = train_datagen.flow_from_directory(\n",
+    "    \"/kaggle/working/banana_split/train\",   # one forward slash\n",
+    "    target_size=(256, 256),\n",
+    "    batch_size=32,\n",
+    "    class_mode=\"categorical\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-08-21T05:34:55.840617Z",
+     "iopub.status.busy": "2025-08-21T05:34:55.840309Z",
+     "iopub.status.idle": "2025-08-21T05:34:55.857579Z",
+     "shell.execute_reply": "2025-08-21T05:34:55.857048Z",
+     "shell.execute_reply.started": "2025-08-21T05:34:55.840594Z"
+    },
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 542 images belonging to 2 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_generator=test_datagen.flow_from_directory(\n",
+    "    '/kaggle/working/banana_split',\n",
+    "    batch_size=32,\n",
+    "    target_size=(256,256),\n",
+    "    class_mode=\"categorical\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-08-21T05:43:43.987451Z",
+     "iopub.status.busy": "2025-08-21T05:43:43.986877Z",
+     "iopub.status.idle": "2025-08-21T05:43:43.998016Z",
+     "shell.execute_reply": "2025-08-21T05:43:43.997332Z",
+     "shell.execute_reply.started": "2025-08-21T05:43:43.987425Z"
+    },
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 152 images belonging to 8 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "val_generator=test_datagen.flow_from_directory(\n",
+    "    '/kaggle/working/banana_split/val',\n",
+    "    batch_size=32,\n",
+    "    target_size=(256,256),\n",
+    "    class_mode=\"categorical\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-08-21T05:43:46.727814Z",
+     "iopub.status.busy": "2025-08-21T05:43:46.727535Z",
+     "iopub.status.idle": "2025-08-21T05:43:46.731618Z",
+     "shell.execute_reply": "2025-08-21T05:43:46.730990Z",
+     "shell.execute_reply.started": "2025-08-21T05:43:46.727792Z"
+    },
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-08-21T05:43:46.961894Z",
+     "iopub.status.busy": "2025-08-21T05:43:46.961689Z",
+     "iopub.status.idle": "2025-08-21T05:43:47.058691Z",
+     "shell.execute_reply": "2025-08-21T05:43:47.058181Z",
+     "shell.execute_reply.started": "2025-08-21T05:43:46.961879Z"
+    },
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">254</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">254</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">254</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">254</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">127</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">127</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">125</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">125</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">125</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">125</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_3           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50176</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │    <span style=\"color: #00af00; text-decoration-color: #00af00\">12,845,312</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,056</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m254\u001b[0m, \u001b[38;5;34m254\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m896\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m254\u001b[0m, \u001b[38;5;34m254\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m128\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m127\u001b[0m, \u001b[38;5;34m127\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m125\u001b[0m, \u001b[38;5;34m125\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │        \u001b[38;5;34m18,496\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m125\u001b[0m, \u001b[38;5;34m125\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │           \u001b[38;5;34m256\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │           \u001b[38;5;34m512\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m295,168\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_3           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │         \u001b[38;5;34m1,024\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50176\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │    \u001b[38;5;34m12,845,312\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │         \u001b[38;5;34m2,056\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">13,237,704</span> (50.50 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m13,237,704\u001b[0m (50.50 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">13,236,744</span> (50.49 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m13,236,744\u001b[0m (50.49 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">960</span> (3.75 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m960\u001b[0m (3.75 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n",
+    "\n",
+    "# Define number of classes\n",
+    "num_classes = train_generator.num_classes\n",
+    "\n",
+    "# Build CNN model\n",
+    "model = Sequential([\n",
+    "    # Conv Block 1\n",
+    "    Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256,3)),\n",
+    "    BatchNormalization(),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "\n",
+    "    # Conv Block 2\n",
+    "    Conv2D(64, (3, 3), activation='relu'),\n",
+    "    BatchNormalization(),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "\n",
+    "    # Conv Block 3\n",
+    "    Conv2D(128, (3, 3), activation='relu'),\n",
+    "    BatchNormalization(),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "\n",
+    "    # Conv Block 4\n",
+    "    Conv2D(256, (3, 3), activation='relu'),\n",
+    "    BatchNormalization(),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "\n",
+    "    # Fully Connected Layers\n",
+    "    Flatten(),\n",
+    "    Dense(256, activation='relu'),\n",
+    "    Dropout(0.5),\n",
+    "    Dense(num_classes, activation='softmax')\n",
+    "])\n",
+    "\n",
+    "# Compile the model\n",
+    "model.compile(optimizer='adam',\n",
+    "              loss='categorical_crossentropy',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "# Model summary\n",
+    "model.summary()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-08-21T05:43:47.297119Z",
+     "iopub.status.busy": "2025-08-21T05:43:47.296897Z"
+    },
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eager execution enabled: True\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+      "  self._warn_if_super_not_called()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\structured_function.py:258: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4s/step - accuracy: 0.2798 - loss: 19.8470"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+      "  self._warn_if_super_not_called()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 5s/step - accuracy: 0.3769 - loss: 20.5914 - val_accuracy: 0.2171 - val_loss: 9.6354\n",
+      "Epoch 2/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 5s/step - accuracy: 0.4974 - loss: 17.8204 - val_accuracy: 0.2961 - val_loss: 8.4259\n",
+      "Epoch 3/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 5s/step - accuracy: 0.3974 - loss: 23.5594 - val_accuracy: 0.2039 - val_loss: 11.2929\n",
+      "Epoch 4/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 4s/step - accuracy: 0.4897 - loss: 21.5145 - val_accuracy: 0.2171 - val_loss: 19.9768\n",
+      "Epoch 5/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 4s/step - accuracy: 0.5462 - loss: 14.5204 - val_accuracy: 0.2237 - val_loss: 15.2129\n",
+      "Epoch 6/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 4s/step - accuracy: 0.5051 - loss: 13.6900 - val_accuracy: 0.2171 - val_loss: 32.1222\n",
+      "Epoch 7/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 4s/step - accuracy: 0.5359 - loss: 11.8984 - val_accuracy: 0.1118 - val_loss: 18.1529\n",
+      "Epoch 8/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 4s/step - accuracy: 0.5590 - loss: 9.8142 - val_accuracy: 0.2171 - val_loss: 18.6094\n",
+      "Epoch 9/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 4s/step - accuracy: 0.5462 - loss: 8.9134 - val_accuracy: 0.2237 - val_loss: 19.7271\n",
+      "Epoch 10/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 4s/step - accuracy: 0.5000 - loss: 7.0655 - val_accuracy: 0.2500 - val_loss: 24.9700\n",
+      "Epoch 11/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 4s/step - accuracy: 0.5846 - loss: 5.0385 - val_accuracy: 0.2434 - val_loss: 19.9753\n",
+      "Epoch 12/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 5s/step - accuracy: 0.5513 - loss: 3.7617 - val_accuracy: 0.3355 - val_loss: 17.3270\n",
+      "Epoch 13/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 4s/step - accuracy: 0.5436 - loss: 3.1651 - val_accuracy: 0.2237 - val_loss: 16.4889\n",
+      "Epoch 14/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 4s/step - accuracy: 0.5974 - loss: 2.4514 - val_accuracy: 0.2237 - val_loss: 7.9318\n",
+      "Epoch 15/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 5s/step - accuracy: 0.5692 - loss: 1.6729 - val_accuracy: 0.2303 - val_loss: 8.3269\n",
+      "Epoch 16/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 5s/step - accuracy: 0.5769 - loss: 2.3299 - val_accuracy: 0.3618 - val_loss: 5.5341\n",
+      "Epoch 17/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 4s/step - accuracy: 0.5590 - loss: 2.4389 - val_accuracy: 0.2368 - val_loss: 9.1896\n",
+      "Epoch 18/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 4s/step - accuracy: 0.5538 - loss: 1.5745 - val_accuracy: 0.2105 - val_loss: 8.1399\n",
+      "Epoch 19/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 4s/step - accuracy: 0.5667 - loss: 1.3463 - val_accuracy: 0.2171 - val_loss: 11.1081\n",
+      "Epoch 20/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 4s/step - accuracy: 0.5410 - loss: 1.4918 - val_accuracy: 0.2171 - val_loss: 5.7116\n",
+      "Epoch 21/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 4s/step - accuracy: 0.6231 - loss: 1.3336 - val_accuracy: 0.2895 - val_loss: 4.3766\n",
+      "Epoch 22/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 4s/step - accuracy: 0.6103 - loss: 1.1867 - val_accuracy: 0.2171 - val_loss: 7.2286\n",
+      "Epoch 23/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 5s/step - accuracy: 0.6538 - loss: 1.2035 - val_accuracy: 0.2171 - val_loss: 8.6770\n",
+      "Epoch 24/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 4s/step - accuracy: 0.6179 - loss: 0.9628 - val_accuracy: 0.2697 - val_loss: 5.9717\n",
+      "Epoch 25/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 4s/step - accuracy: 0.6179 - loss: 1.1610 - val_accuracy: 0.2237 - val_loss: 5.2485\n",
+      "Epoch 26/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 4s/step - accuracy: 0.6154 - loss: 1.0895 - val_accuracy: 0.3750 - val_loss: 3.5000\n",
+      "Epoch 27/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 4s/step - accuracy: 0.6513 - loss: 0.9564 - val_accuracy: 0.4276 - val_loss: 2.5584\n",
+      "Epoch 28/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 5s/step - accuracy: 0.6821 - loss: 0.9871 - val_accuracy: 0.4145 - val_loss: 3.0618\n",
+      "Epoch 29/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 4s/step - accuracy: 0.6769 - loss: 0.8814 - val_accuracy: 0.4539 - val_loss: 2.3110\n",
+      "Epoch 30/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 5s/step - accuracy: 0.6769 - loss: 0.8749 - val_accuracy: 0.3355 - val_loss: 2.2776\n",
+      "Epoch 31/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 5s/step - accuracy: 0.6590 - loss: 1.0602 - val_accuracy: 0.4013 - val_loss: 1.8672\n",
+      "Epoch 32/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 4s/step - accuracy: 0.6564 - loss: 0.9039 - val_accuracy: 0.4145 - val_loss: 1.8618\n",
+      "Epoch 33/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 4s/step - accuracy: 0.6590 - loss: 0.9887 - val_accuracy: 0.4013 - val_loss: 2.1942\n",
+      "Epoch 34/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 4s/step - accuracy: 0.7051 - loss: 0.9572 - val_accuracy: 0.4539 - val_loss: 2.1114\n",
+      "Epoch 35/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 4s/step - accuracy: 0.6359 - loss: 0.9765 - val_accuracy: 0.5197 - val_loss: 1.7514\n",
+      "Epoch 36/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 4s/step - accuracy: 0.6744 - loss: 1.0620 - val_accuracy: 0.4934 - val_loss: 1.3752\n",
+      "Epoch 37/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 4s/step - accuracy: 0.7256 - loss: 0.7474 - val_accuracy: 0.6382 - val_loss: 0.9238\n",
+      "Epoch 38/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 4s/step - accuracy: 0.7077 - loss: 0.7956 - val_accuracy: 0.6776 - val_loss: 0.8532\n",
+      "Epoch 39/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 4s/step - accuracy: 0.7231 - loss: 1.0127 - val_accuracy: 0.7303 - val_loss: 0.7758\n",
+      "Epoch 40/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 4s/step - accuracy: 0.7000 - loss: 1.1356 - val_accuracy: 0.6250 - val_loss: 1.8973\n",
+      "Epoch 41/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 4s/step - accuracy: 0.6897 - loss: 0.8203 - val_accuracy: 0.6908 - val_loss: 1.1708\n",
+      "Epoch 42/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 4s/step - accuracy: 0.7308 - loss: 0.7932 - val_accuracy: 0.7434 - val_loss: 0.7902\n",
+      "Epoch 43/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 4s/step - accuracy: 0.6923 - loss: 0.8812 - val_accuracy: 0.6447 - val_loss: 1.1985\n",
+      "Epoch 44/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 4s/step - accuracy: 0.7077 - loss: 0.7346 - val_accuracy: 0.6974 - val_loss: 0.9774\n",
+      "Epoch 45/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 4s/step - accuracy: 0.6846 - loss: 0.8489 - val_accuracy: 0.6053 - val_loss: 1.9723\n",
+      "Epoch 46/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 4s/step - accuracy: 0.6846 - loss: 0.7834 - val_accuracy: 0.7368 - val_loss: 1.0558\n",
+      "Epoch 47/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 4s/step - accuracy: 0.6949 - loss: 0.8835 - val_accuracy: 0.7303 - val_loss: 1.4276\n",
+      "Epoch 48/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 4s/step - accuracy: 0.7385 - loss: 0.7021 - val_accuracy: 0.7566 - val_loss: 0.6669\n",
+      "Epoch 49/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 4s/step - accuracy: 0.7154 - loss: 0.7964 - val_accuracy: 0.6974 - val_loss: 1.1310\n",
+      "Epoch 50/50\n",
+      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 4s/step - accuracy: 0.7462 - loss: 0.7340 - val_accuracy: 0.6974 - val_loss: 1.1841\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Training complete. Models saved as 'banana_disease_model.h5' and 'banana_disease_model.keras'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "\n",
+    "# Re-compile model before training\n",
+    "model.compile(\n",
+    "    optimizer=tf.keras.optimizers.Adam(),\n",
+    "    loss=\"categorical_crossentropy\",\n",
+    "    metrics=[\"accuracy\"]\n",
+    ")\n",
+    "\n",
+    "# Make sure eager execution is enabled\n",
+    "tf.config.run_functions_eagerly(True)\n",
+    "print(\"Eager execution enabled:\", tf.executing_eagerly())\n",
+    "\n",
+    "# Train the model\n",
+    "history = model.fit(\n",
+    "    train_generator,\n",
+    "    epochs=50,\n",
+    "    validation_data=val_generator\n",
+    ")\n",
+    "class_names = np.array(list(train_generator.class_indices.keys()))\n",
+    "np.save(\"class_names.npy\", class_names)\n",
+    "\n",
+    "model.save(\"banana_disease_model.h5\")     # Old HDF5 format\n",
+    "model.save(\"banana_disease_model.keras\") \n",
+    "\n",
+    "print(\"✅ Training complete. Models saved as 'banana_disease_model.h5' and 'banana_disease_model.keras'\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = tf.keras.models.load_model('banana_disease_model.h5')\n",
+    "model.save(\"banana_disease.keras\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Running on local URL:  http://127.0.0.1:7860\n",
+      "* Running on public URL: https://ac9b4e0f5a2f6f986d.gradio.live\n",
+      "\n",
+      "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"https://ac9b4e0f5a2f6f986d.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\structured_function.py:258: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 186ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\structured_function.py:258: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\structured_function.py:258: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\structured_function.py:258: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\structured_function.py:258: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\structured_function.py:258: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\structured_function.py:258: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\structured_function.py:258: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\structured_function.py:258: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\structured_function.py:258: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\structured_function.py:258: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "\n",
+    "# Load the saved model\n",
+    "model = tf.keras.models.load_model('banana_disease_model.h5')\n",
+    "\n",
+    "# Load class names\n",
+    "class_names = np.load(\"class_names.npy\", allow_pickle=True)\n",
+    "\n",
+    "# Preprocess the input image\n",
+    "def preprocess_image(img):\n",
+    "    if img is None:\n",
+    "        return None\n",
+    "    img = img.resize((256, 256))  # Resize to match training\n",
+    "    img = np.array(img) / 255.0   # Normalize\n",
+    "    img = np.expand_dims(img, axis=0)  # Add batch dimension\n",
+    "    return img\n",
+    "\n",
+    "# Prediction function\n",
+    "def predict_disease(img):\n",
+    "    img = preprocess_image(img)\n",
+    "    if img is None:\n",
+    "        return \"No image provided\"\n",
+    "    predictions = model.predict(img)\n",
+    "    predicted_class = np.argmax(predictions, axis=1)[0]\n",
+    "    return class_names[predicted_class]\n",
+    "\n",
+    "# Gradio interface\n",
+    "interface = gr.Interface(\n",
+    "    fn=predict_disease,\n",
+    "    inputs=gr.Image(type=\"pil\", image_mode='RGB'),\n",
+    "    outputs=gr.Textbox(label=\"Predicted Disease\"),\n",
+    "    live=False,  # 👈 Turn off live mode for image upload (use True only for webcam/live feed)\n",
+    "    title=\"Banana Disease Classification\",\n",
+    "    description=\"Upload an image of a banana leaf and the model will predict the disease.\"\n",
+    ")\n",
+    "\n",
+    "interface.launch(share=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-08T09:55:35.951810Z",
+     "iopub.status.busy": "2025-05-08T09:55:35.951500Z",
+     "iopub.status.idle": "2025-05-08T09:55:35.988994Z",
+     "shell.execute_reply": "2025-05-08T09:55:35.988015Z",
+     "shell.execute_reply.started": "2025-05-08T09:55:35.951788Z"
+    },
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Running on local URL:  http://127.0.0.1:7861\n",
+      "* Running on public URL: https://aaa4fadea494c10aaf.gradio.live\n",
+      "\n",
+      "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"https://aaa4fadea494c10aaf.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\queueing.py\", line 626, in process_events\n",
+      "    response = await route_utils.call_process_api(\n",
+      "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\route_utils.py\", line 349, in call_process_api\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\blocks.py\", line 2274, in process_api\n",
+      "    result = await self.call_function(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\blocks.py\", line 1781, in call_function\n",
+      "    prediction = await anyio.to_thread.run_sync(  # type: ignore\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
+      "    return await get_async_backend().run_sync_in_worker_thread(\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2476, in run_sync_in_worker_thread\n",
+      "    return await future\n",
+      "           ^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 967, in run\n",
+      "    result = context.run(func, *args)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\utils.py\", line 909, in wrapper\n",
+      "    response = f(*args, **kwargs)\n",
+      "               ^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\adity\\AppData\\Local\\Temp\\ipykernel_5608\\3508234767.py\", line 22, in predict_disease\n",
+      "    predictions = model.predict(img)  # Predict the class\n",
+      "                  ^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 122, in error_handler\n",
+      "    raise e.with_traceback(filtered_tb) from None\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py\", line 59, in quick_execute\n",
+      "    except TypeError as e:\n",
+      "tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:\n",
+      "\n",
+      "Detected at node sequential_3_1/dense_6_1/Relu defined at (most recent call last):\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\threading.py\", line 1030, in _bootstrap\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\threading.py\", line 1073, in _bootstrap_inner\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 967, in run\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\utils.py\", line 909, in wrapper\n",
+      "\n",
+      "  File \"C:\\Users\\adity\\AppData\\Local\\Temp\\ipykernel_5608\\3508234767.py\", line 22, in predict_disease\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 566, in predict\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 260, in one_step_on_data_distributed\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 250, in one_step_on_data\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 105, in predict_step\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 941, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 59, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\models\\sequential.py\", line 220, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 183, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\ops\\function.py\", line 206, in _run_through_graph\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 644, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 941, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 59, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py\", line 155, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\activations\\activations.py\", line 47, in relu\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\activations\\activations.py\", line 101, in static_call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\nn.py\", line 15, in relu\n",
+      "\n",
+      "Matrix size-incompatible: In[0]: [1,12544], In[1]: [50176,256]\n",
+      "\t [[{{node sequential_3_1/dense_6_1/Relu}}]] [Op:__inference_one_step_on_data_distributed_12471]\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\queueing.py\", line 626, in process_events\n",
+      "    response = await route_utils.call_process_api(\n",
+      "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\route_utils.py\", line 349, in call_process_api\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\blocks.py\", line 2274, in process_api\n",
+      "    result = await self.call_function(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\blocks.py\", line 1781, in call_function\n",
+      "    prediction = await anyio.to_thread.run_sync(  # type: ignore\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
+      "    return await get_async_backend().run_sync_in_worker_thread(\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2476, in run_sync_in_worker_thread\n",
+      "    return await future\n",
+      "           ^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 967, in run\n",
+      "    result = context.run(func, *args)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\utils.py\", line 909, in wrapper\n",
+      "    response = f(*args, **kwargs)\n",
+      "               ^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\adity\\AppData\\Local\\Temp\\ipykernel_5608\\3508234767.py\", line 22, in predict_disease\n",
+      "    predictions = model.predict(img)  # Predict the class\n",
+      "                  ^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 122, in error_handler\n",
+      "    raise e.with_traceback(filtered_tb) from None\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py\", line 59, in quick_execute\n",
+      "    except TypeError as e:\n",
+      "tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:\n",
+      "\n",
+      "Detected at node sequential_3_1/dense_6_1/Relu defined at (most recent call last):\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\threading.py\", line 1030, in _bootstrap\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\threading.py\", line 1073, in _bootstrap_inner\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 967, in run\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\utils.py\", line 909, in wrapper\n",
+      "\n",
+      "  File \"C:\\Users\\adity\\AppData\\Local\\Temp\\ipykernel_5608\\3508234767.py\", line 22, in predict_disease\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 566, in predict\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 260, in one_step_on_data_distributed\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 250, in one_step_on_data\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 105, in predict_step\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 941, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 59, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\models\\sequential.py\", line 220, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 183, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\ops\\function.py\", line 206, in _run_through_graph\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 644, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 941, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 59, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py\", line 155, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\activations\\activations.py\", line 47, in relu\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\activations\\activations.py\", line 101, in static_call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\nn.py\", line 15, in relu\n",
+      "\n",
+      "Matrix size-incompatible: In[0]: [1,12544], In[1]: [50176,256]\n",
+      "\t [[{{node sequential_3_1/dense_6_1/Relu}}]] [Op:__inference_one_step_on_data_distributed_12471]\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\queueing.py\", line 626, in process_events\n",
+      "    response = await route_utils.call_process_api(\n",
+      "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\route_utils.py\", line 349, in call_process_api\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\blocks.py\", line 2274, in process_api\n",
+      "    result = await self.call_function(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\blocks.py\", line 1781, in call_function\n",
+      "    prediction = await anyio.to_thread.run_sync(  # type: ignore\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
+      "    return await get_async_backend().run_sync_in_worker_thread(\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2476, in run_sync_in_worker_thread\n",
+      "    return await future\n",
+      "           ^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 967, in run\n",
+      "    result = context.run(func, *args)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\utils.py\", line 909, in wrapper\n",
+      "    response = f(*args, **kwargs)\n",
+      "               ^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\adity\\AppData\\Local\\Temp\\ipykernel_5608\\3508234767.py\", line 21, in predict_disease\n",
+      "    img = preprocess_image(img)\n",
+      "          ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\adity\\AppData\\Local\\Temp\\ipykernel_5608\\3508234767.py\", line 14, in preprocess_image\n",
+      "    img = img.resize((150, 150))  # Resize to the same size used during training\n",
+      "          ^^^^^^^^^^\n",
+      "AttributeError: 'NoneType' object has no attribute 'resize'\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\queueing.py\", line 626, in process_events\n",
+      "    response = await route_utils.call_process_api(\n",
+      "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\route_utils.py\", line 349, in call_process_api\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\blocks.py\", line 2274, in process_api\n",
+      "    result = await self.call_function(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\blocks.py\", line 1781, in call_function\n",
+      "    prediction = await anyio.to_thread.run_sync(  # type: ignore\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
+      "    return await get_async_backend().run_sync_in_worker_thread(\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2476, in run_sync_in_worker_thread\n",
+      "    return await future\n",
+      "           ^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 967, in run\n",
+      "    result = context.run(func, *args)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\utils.py\", line 909, in wrapper\n",
+      "    response = f(*args, **kwargs)\n",
+      "               ^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\adity\\AppData\\Local\\Temp\\ipykernel_5608\\3508234767.py\", line 22, in predict_disease\n",
+      "    predictions = model.predict(img)  # Predict the class\n",
+      "                  ^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 122, in error_handler\n",
+      "    raise e.with_traceback(filtered_tb) from None\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py\", line 59, in quick_execute\n",
+      "    except TypeError as e:\n",
+      "tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:\n",
+      "\n",
+      "Detected at node sequential_3_1/dense_6_1/Relu defined at (most recent call last):\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\threading.py\", line 1030, in _bootstrap\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\threading.py\", line 1073, in _bootstrap_inner\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 967, in run\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\utils.py\", line 909, in wrapper\n",
+      "\n",
+      "  File \"C:\\Users\\adity\\AppData\\Local\\Temp\\ipykernel_5608\\3508234767.py\", line 22, in predict_disease\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 566, in predict\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 260, in one_step_on_data_distributed\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 250, in one_step_on_data\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 105, in predict_step\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 941, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 59, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\models\\sequential.py\", line 220, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 183, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\ops\\function.py\", line 206, in _run_through_graph\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 644, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 941, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 59, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py\", line 155, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\activations\\activations.py\", line 47, in relu\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\activations\\activations.py\", line 101, in static_call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\nn.py\", line 15, in relu\n",
+      "\n",
+      "Matrix size-incompatible: In[0]: [1,12544], In[1]: [50176,256]\n",
+      "\t [[{{node sequential_3_1/dense_6_1/Relu}}]] [Op:__inference_one_step_on_data_distributed_12471]\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\queueing.py\", line 626, in process_events\n",
+      "    response = await route_utils.call_process_api(\n",
+      "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\route_utils.py\", line 349, in call_process_api\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\blocks.py\", line 2274, in process_api\n",
+      "    result = await self.call_function(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\blocks.py\", line 1781, in call_function\n",
+      "    prediction = await anyio.to_thread.run_sync(  # type: ignore\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
+      "    return await get_async_backend().run_sync_in_worker_thread(\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2476, in run_sync_in_worker_thread\n",
+      "    return await future\n",
+      "           ^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 967, in run\n",
+      "    result = context.run(func, *args)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\utils.py\", line 909, in wrapper\n",
+      "    response = f(*args, **kwargs)\n",
+      "               ^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\adity\\AppData\\Local\\Temp\\ipykernel_5608\\3508234767.py\", line 22, in predict_disease\n",
+      "    predictions = model.predict(img)  # Predict the class\n",
+      "                  ^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 122, in error_handler\n",
+      "    raise e.with_traceback(filtered_tb) from None\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py\", line 59, in quick_execute\n",
+      "    except TypeError as e:\n",
+      "tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:\n",
+      "\n",
+      "Detected at node sequential_3_1/dense_6_1/Relu defined at (most recent call last):\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\threading.py\", line 1030, in _bootstrap\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\threading.py\", line 1073, in _bootstrap_inner\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 967, in run\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\gradio\\utils.py\", line 909, in wrapper\n",
+      "\n",
+      "  File \"C:\\Users\\adity\\AppData\\Local\\Temp\\ipykernel_5608\\3508234767.py\", line 22, in predict_disease\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 566, in predict\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 260, in one_step_on_data_distributed\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 250, in one_step_on_data\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 105, in predict_step\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 941, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 59, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\models\\sequential.py\", line 220, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 183, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\ops\\function.py\", line 206, in _run_through_graph\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 644, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 941, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 59, in __call__\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py\", line 155, in call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\activations\\activations.py\", line 47, in relu\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\activations\\activations.py\", line 101, in static_call\n",
+      "\n",
+      "  File \"c:\\Users\\adity\\miniconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\nn.py\", line 15, in relu\n",
+      "\n",
+      "Matrix size-incompatible: In[0]: [1,12544], In[1]: [50176,256]\n",
+      "\t [[{{node sequential_3_1/dense_6_1/Relu}}]] [Op:__inference_one_step_on_data_distributed_12471]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "\n",
+    "# Load the saved model\n",
+    "#model = tf.keras.models.load_model('banana_disease_model.h5')\n",
+    "\n",
+    "# Define the class names (modify these based on your dataset)\n",
+    "class_names = ['disease_1', 'disease_2', 'healthy']\n",
+    "\n",
+    "# Preprocess the input image before making predictions\n",
+    "def preprocess_image(img):\n",
+    "    img = img.resize((150, 150))  # Resize to the same size used during training\n",
+    "    img = np.array(img) / 255.0  # Normalize to [0, 1]\n",
+    "    img = np.expand_dims(img, axis=0)  # Add batch dimension\n",
+    "    return img\n",
+    "\n",
+    "# Define the prediction function\n",
+    "def predict_disease(img):\n",
+    "    img = preprocess_image(img)\n",
+    "    predictions = model.predict(img)  # Predict the class\n",
+    "    predicted_class = np.argmax(predictions, axis=1)[0]  # Get the class with the highest probability\n",
+    "    return class_names[predicted_class]\n",
+    "\n",
+    "# Create the Gradio interface\n",
+    "interface = gr.Interface(\n",
+    "    fn=predict_disease,  # Function to call when the user submits an image\n",
+    "    inputs=gr.Image(type=\"pil\", image_mode='RGB'),  # Input type (image upload)\n",
+    "    outputs=gr.Textbox(label=\"Predicted Disease\"),  # Output type (predicted label)\n",
+    "    live=True,  # Enable live predictions\n",
+    "    title=\"Banana Disease Classification\",  # Title for the interface\n",
+    "    description=\"Upload an image of a banana leaf and the model will predict the disease.\"  # Description\n",
+    ")\n",
+    "\n",
+    "# Launch the Gradio interface\n",
+    "interface.launch(share=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-08T09:31:44.449369Z",
+     "iopub.status.busy": "2025-05-08T09:31:44.448579Z",
+     "iopub.status.idle": "2025-05-08T09:31:44.454584Z",
+     "shell.execute_reply": "2025-05-08T09:31:44.453759Z",
+     "shell.execute_reply.started": "2025-05-08T09:31:44.449333Z"
+    },
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TensorFlow version: 2.20.0\n",
+      "Keras version: 3.11.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import keras\n",
+    "\n",
+    "print(\"TensorFlow version:\", tf.__version__)\n",
+    "print(\"Keras version:\", keras.__version__)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model=tf.keras.models.load_model('banana_disease_model.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "class_labels=[\"black_sigatoka\",\"bract_Mosaic_virus\",\"Healthy Leaf\",\"insect_pest\",\"moko\",\"panama\",\"sigatoka\"]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 197ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from tensorflow.keras.preprocessing import image\n",
+    "path = \"C:/kaggle/working/banana_split/train/Banana Bract Mosaic Virus Disease/Bract Mosaic Virus Disease (1).jpg\"\n",
+    "img_size=(256,256)\n",
+    "img=image.load_img(path,target_size=img_size)\n",
+    "img_array=image.img_to_array(img)\n",
+    "img_array=np.expand_dims(img_array,axis=0)\n",
+    "img_array=img_array/255\n",
+    "prediction=model.predict(img_array)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "pred=np.argmax(prediction)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Healthy Leaf'"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class_labels[pred]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "nvidiaTeslaT4",
+   "dataSources": [
+    {
+     "datasetId": 4038537,
+     "sourceId": 7022960,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "dockerImageVersionId": 31011,
+   "isGpuEnabled": true,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/banana_disease.keras b/banana_disease.keras
new file mode 100644
index 0000000000000000000000000000000000000000..8943d8bbdd702cdeb108b8f4cda38433f78bf50f
--- /dev/null
+++ b/banana_disease.keras
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2a235a8bce90d585817c095857f0ae408facabef071ac930586f7b7aa558fe8
+size 53014372
diff --git a/banana_disease_classification.py b/banana_disease_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..efa5f1b027753b69f932a2c02501cb0b979cca00
--- /dev/null
+++ b/banana_disease_classification.py
@@ -0,0 +1,179 @@
+import os
+import shutil
+import random
+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization
+
+
+for dirname, _, filenames in os.walk('/kaggle/input'):
+    for filename in filenames:
+        os.path.join(dirname, filename)
+
+# Dataset paths
+data_dir = "Kaggle/input/Banana Disease Recognition Dataset/Original Images/Original Images"
+base_dir = "/kaggle/working/banana_split"
+
+train_dir = os.path.join(base_dir, "train")
+val_dir = os.path.join(base_dir, "val")
+
+# Create train and validation directories
+os.makedirs(train_dir, exist_ok=True)
+os.makedirs(val_dir, exist_ok=True)
+
+# Split each class folder into train and val
+for cls in os.listdir(data_dir):
+    cls_path = os.path.join(data_dir, cls)
+    
+    if os.path.isdir(cls_path):  # Only process folders
+        # Create class subfolders inside train and val
+        os.makedirs(os.path.join(train_dir, cls), exist_ok=True)
+        os.makedirs(os.path.join(val_dir, cls), exist_ok=True)
+
+        # ✅ Get only files (exclude subfolders)
+        files = [f for f in os.listdir(cls_path) if os.path.isfile(os.path.join(cls_path, f))]
+        random.shuffle(files)
+
+        # 80/20 train-val split
+        split_idx = int(0.8 * len(files))
+        train_files = files[:split_idx]
+        val_files = files[split_idx:]
+
+        # Copy training images
+        for f in train_files:
+            shutil.copy(os.path.join(cls_path, f), os.path.join(train_dir, cls, f))
+
+        # Copy validation images
+        for f in val_files:
+            shutil.copy(os.path.join(cls_path, f), os.path.join(val_dir, cls, f))
+
+print("✅ Dataset successfully split into train & val folders")
+
+# Parameters for dataset loading
+img_size = (224, 224)
+batch_size = 32
+
+# Load training dataset
+train_ds = tf.keras.utils.image_dataset_from_directory(
+    train_dir,
+    image_size=img_size,
+    batch_size=batch_size
+)
+
+# Load validation dataset
+val_ds = tf.keras.utils.image_dataset_from_directory(
+    val_dir,
+    image_size=img_size,
+    batch_size=batch_size
+)
+
+# Prefetch for performance
+train_ds = train_ds.prefetch(buffer_size=tf.data.AUTOTUNE)
+val_ds = val_ds.prefetch(buffer_size=tf.data.AUTOTUNE)
+
+print("✅ TensorFlow datasets loaded and ready for training.")
+
+output_1 = "/kaggle/working/banana_split"
+shutil.make_archive(output_1,'zip',output_1)
+
+
+train_datagen=ImageDataGenerator(
+    rescale=1./255,
+    rotation_range=90,
+    horizontal_flip=True,
+    vertical_flip=True,
+    zoom_range=0.2
+)
+test_datagen=ImageDataGenerator(
+    rescale=1./255
+)
+
+train_generator = train_datagen.flow_from_directory(
+    "/kaggle/working/banana_split/train",   # one forward slash
+    target_size=(256, 256),
+    batch_size=32,
+    class_mode="categorical"
+)
+
+test_generator=test_datagen.flow_from_directory(
+    '/kaggle/working/banana_split',
+    batch_size=32,
+    target_size=(256,256),
+    class_mode="categorical"
+)
+
+val_generator=test_datagen.flow_from_directory(
+    '/kaggle/working/banana_split/val',
+    batch_size=32,
+    target_size=(256,256),
+    class_mode="categorical"
+)
+
+# Define number of classes
+num_classes = train_generator.num_classes
+
+# Build CNN model
+model = Sequential([
+    # Conv Block 1
+    Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256,3)),
+    BatchNormalization(),
+    MaxPooling2D(pool_size=(2, 2)),
+
+    # Conv Block 2
+    Conv2D(64, (3, 3), activation='relu'),
+    BatchNormalization(),
+    MaxPooling2D(pool_size=(2, 2)),
+
+    # Conv Block 3
+    Conv2D(128, (3, 3), activation='relu'),
+    BatchNormalization(),
+    MaxPooling2D(pool_size=(2, 2)),
+
+    # Conv Block 4
+    Conv2D(256, (3, 3), activation='relu'),
+    BatchNormalization(),
+    MaxPooling2D(pool_size=(2, 2)),
+
+    # Fully Connected Layers
+    Flatten(),
+    Dense(256, activation='relu'),
+    Dropout(0.5),
+    Dense(num_classes, activation='softmax')
+])
+
+# Compile the model
+model.compile(optimizer='adam',
+              loss='categorical_crossentropy',
+              metrics=['accuracy'])
+
+# Model summary
+model.summary()
+
+# Re-compile model before training
+model.compile(
+    optimizer=tf.keras.optimizers.Adam(),
+    loss="categorical_crossentropy",
+    metrics=["accuracy"]
+)
+
+# Make sure eager execution is enabled
+tf.config.run_functions_eagerly(True)
+print("Eager execution enabled:", tf.executing_eagerly())
+
+# Train the model
+history = model.fit(
+    train_generator,
+    epochs=50,
+    validation_data=val_generator
+)
+class_names = np.array(list(train_generator.class_indices.keys()))
+np.save("class_names.npy", class_names)
+
+model.save("banana_disease_model.h5")     # Old HDF5 format
+model.save("banana_disease.keras") 
+
+print("✅ Training complete. Models saved as 'banana_disease_model.h5' and 'banana_disease_model.keras'")
+
diff --git a/banana_disease_model.h5 b/banana_disease_model.h5
new file mode 100644
index 0000000000000000000000000000000000000000..a3636ec9e8404abd14564df4f773ad2703d5b619
--- /dev/null
+++ b/banana_disease_model.h5
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f47150c7a0d8358a95903c294b13531acb3eea8f6ab7af9072c4855e90792b4
+size 158936240
diff --git a/banana_disease_model.keras b/banana_disease_model.keras
new file mode 100644
index 0000000000000000000000000000000000000000..d8e1d679dd64e42ea72a08545f0738de81c1142f
--- /dev/null
+++ b/banana_disease_model.keras
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c5d9ca8c6c9af283f5d7ff3ff6af2c1dc33833122c3904b4bf70e39d004d66c
+size 158923080
diff --git a/banana_gradio.py b/banana_gradio.py
new file mode 100644
index 0000000000000000000000000000000000000000..63ad00aeef7e2aa26be5725ad396e2808f88102f
--- /dev/null
+++ b/banana_gradio.py
@@ -0,0 +1,39 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+# Load the saved model
+model = tf.keras.models.load_model('banana_disease.keras')
+
+# Load class names
+class_names = np.load("class_names.npy", allow_pickle=True)
+
+# Preprocess the input image
+def preprocess_image(img):
+    if img is None:
+        return None
+    img = img.resize((256, 256))  # Resize to match training
+    img = np.array(img) / 255.0   # Normalize
+    img = np.expand_dims(img, axis=0)  # Add batch dimension
+    return img
+
+# Prediction function
+def predict_disease(img):
+    img = preprocess_image(img)
+    if img is None:
+        return "No image provided"
+    predictions = model.predict(img)
+    predicted_class = np.argmax(predictions, axis=1)[0]
+    return class_names[predicted_class]
+
+# Gradio interface
+interface = gr.Interface(
+    fn=predict_disease,
+    inputs=gr.Image(type="pil", image_mode='RGB'),
+    outputs=gr.Textbox(label="Predicted Disease"),
+    live=False,  # 👈 Turn off live mode for image upload (use True only for webcam/live feed)
+    title="Banana Disease Classification",
+    description="Upload an image of a banana leaf and the model will predict the disease."
+)
+
+interface.launch(share=True)
diff --git a/class_names.npy b/class_names.npy
new file mode 100644
index 0000000000000000000000000000000000000000..420c5824083b303583964148171837fefcbbb7a6
--- /dev/null
+++ b/class_names.npy
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+version https://git-lfs.github.com/spec/v1
+oid sha256:6ea3fcec75879323ee299b3ea24c146e614187564ff7a053f53663e5810c87c9
+size 1184