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import gradio as gr
import tensorflow as tf
import numpy as np
# Load the saved model
model = tf.keras.models.load_model('banana_disease_densenet121.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", None
predictions = model.predict(img)[0]
predicted_class = np.argmax(predictions)
confidence_scores = {class_names[i]: float(predictions[i]) for i in range(len(class_names))}
return f"Predicted: {class_names[predicted_class]}", confidence_scores
# 🌿 Custom theme
custom_theme = gr.themes.Soft()
# UI
with gr.Blocks(theme=custom_theme, css="""
#title {
font-size: 32px;
font-weight: bold;
text-align: center;
color: white;
animation: fadeIn 2s ease-in-out;
}
#subtitle {
text-align: center;
font-size: 16px;
color: #ccc;
animation: fadeInUp 2s ease-in-out;
}
#prediction_box {
font-size: 18px;
padding: 12px;
border-radius: 8px;
min-height: 60px; /* ensures readable area */
word-wrap: break-word;
white-space: normal;
}
.gr-button {
transition: 0.3s;
}
.gr-button:hover {
transform: scale(1.05);
box-shadow: 0px 4px 20px rgba(0, 128, 0, 0.4);
}
@keyframes fadeIn {
from {opacity: 0;}
to {opacity: 1;}
}
@keyframes fadeInUp {
from {opacity: 0; transform: translateY(10px);}
to {opacity: 1; transform: translateY(0);}
}
""") as demo:
gr.Markdown("<div id='title'>Banana Leaf Disease Classifier</div>")
gr.Markdown("<div id='subtitle'>Upload a banana leaf image, and our AI will diagnose the disease</div>")
with gr.Row():
with gr.Column(scale=1):
image_input = gr.Image(type="pil", image_mode="RGB", label="Upload Banana Leaf")
predict_btn = gr.Button("🔍 Predict Disease", variant="primary", elem_id="predict_btn")
with gr.Column(scale=1):
# ✅ Changed from Textbox to Text
output_label = gr.Text(label="Prediction", interactive=False, elem_id="prediction_box")
with gr.Accordion("Confidence Scores", open=True):
output_conf = gr.Label(label="Scores")
predict_btn.click(fn=predict_disease, inputs=image_input, outputs=[output_label, output_conf])
# Launch app
demo.launch(share=True)
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