Create app.py

app.py

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+import gradio as gr
+from tensorflow import keras
+import numpy as np
+from PIL import Image
+import cv2
+
+# Load model
+model = keras.models.load_model('my_model (2).h5')
+
+# Class labels
+CLASS_NAMES = ['Necrotic Tumor', 'Non-Tumor', 'Viable Tumor']
+
+def make_gradcam_heatmap(img_array, model, last_conv_layer_name="last_conv_layer"):
+    grad_model = keras.models.Model(
+        model.inputs,
+        [model.get_layer(last_conv_layer_name).output, model.output]
+    )
+    
+    with keras.backend.GradientTape() as tape:
+        conv_outputs, predictions = grad_model(img_array)
+        pred_index = np.argmax(predictions[0])
+        class_channel = predictions[:, pred_index]
+    
+    grads = tape.gradient(class_channel, conv_outputs)
+    pooled_grads = np.mean(grads, axis=(0, 1, 2))
+    
+    conv_outputs = conv_outputs[0]
+    heatmap = conv_outputs @ pooled_grads[..., np.newaxis]
+    heatmap = np.squeeze(heatmap)
+    heatmap = np.maximum(heatmap, 0) / (np.max(heatmap) + 1e-8)
+    
+    return heatmap
+
+def predict(input_image):
+    # Preprocess
+    img = Image.fromarray(input_image).convert('RGB')
+    img = img.resize((224, 224))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    # Predict
+    predictions = model.predict(img_array)
+    pred_class = CLASS_NAMES[np.argmax(predictions[0])]
+    confidence = float(np.max(predictions[0])) * 100
+    
+    # Create result text
+    result_text = f"Prediction: {pred_class}\nConfidence: {confidence:.2f}%\n\n"
+    result_text += "All Probabilities:\n"
+    for i, name in enumerate(CLASS_NAMES):
+        result_text += f"  {name}: {predictions[0][i]*100:.2f}%\n"
+    
+    # Generate Grad-CAM
+    try:
+        heatmap = make_gradcam_heatmap(img_array, model)
+        heatmap = cv2.resize(heatmap, (224, 224))
+        heatmap = np.uint8(255 * heatmap)
+        heatmap_colored = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
+        
+        original = np.array(img)
+        superimposed = cv2.addWeighted(original, 0.6, heatmap_colored, 0.4, 0)
+        output_image = superimposed
+    except Exception as e:
+        output_image = np.array(img)
+        result_text += f"\n(Grad-CAM unavailable: {str(e)})"
+    
+    return output_image, result_text
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(label="Upload Histopathology Image"),
+    outputs=[
+        gr.Image(label="Grad-CAM Visualization"),
+        gr.Textbox(label="Classification Result")
+    ],
+    title="Bone Cancer Detection (Osteosarcoma)",
+    description="Upload an H&E stained histopathology image to classify as Non-Tumor, Viable Tumor, or Necrotic Tumor."
+)
+
+demo.launch()
+```
+
+4. Click **Commit new file to main**
+
+---
+
+## Also update `requirements.txt`
+
+You need to add `opencv-python`. Edit your `requirements.txt` to:
+```
+tensorflow
+gradio
+numpy
+opencv-python-headless
+pillow