Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Load model
+model = tf.keras.models.load_model("oa.keras")
+
+# Define class labels
+class_labels = ['Healthy', 'Moderate', 'Severe']
+
+# Page title
+st.title("Knee Osteoarthritis Severity Classifier")
+
+# File uploader
+uploaded_file = st.file_uploader("Upload a Knee X-ray Image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    # Show uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+
+    # Preprocess the image
+    img = image.resize((224, 224))
+    img_array = np.array(img)
+    if img_array.shape[-1] == 4:  # if RGBA, convert to RGB
+        img_array = img_array[:, :, :3]
+    img_array = img_array / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Predict
+    prediction = model.predict(img_array)
+    predicted_class = class_labels[np.argmax(prediction)]
+    confidence = np.max(prediction)
+
+    # Show result
+    st.markdown(f"### 🩺 Predicted Class: **{predicted_class}**")
+    st.markdown(f"Confidence: `{confidence:.2f}`")