Create app.py

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+from PIL import Image
+from app.model import predict, gradcam, CLASS_NAMES
+
+def predict_fn(img):
+    label, confidence, probs = predict(img)
+    probs_sorted = {k: float(v) for k, v in sorted(probs.items(), key=lambda x: x[1], reverse=True)}
+    return {
+        "Predicted label": label,
+        "Confidence": round(confidence, 3),
+        "Class probabilities": probs_sorted
+    }
+
+def gradcam_fn(img, interpolant):
+    heatmap = gradcam(img, interpolant=float(interpolant))
+    return Image.fromarray(heatmap)
+
+with gr.Blocks(title="Brain Tumor MRI Classifier (InceptionV3 + Grad-CAM)") as demo:
+    gr.Markdown("# Brain Tumor MRI Classifier (InceptionV3 + Grad-CAM)")
+    gr.Markdown("Upload an MRI image to classify and visualize Grad-CAM explanation.")
+
+    with gr.Row():
+        with gr.Column():
+            input_img = gr.Image(type="pil", label="Upload MRI Image")
+            interpolant_slider = gr.Slider(0, 1, value=0.5, label="Grad-CAM Intensity (interpolant)")
+            submit_btn = gr.Button("Run Prediction + Grad-CAM")
+
+        with gr.Column():
+            output_json = gr.JSON(label="Prediction Results")
+            output_cam = gr.Image(label="Grad-CAM Overlay")
+
+    submit_btn.click(
+        fn=lambda img, interp: (predict_fn(img), gradcam_fn(img, interp)),
+        inputs=[input_img, interpolant_slider],
+        outputs=[output_json, output_cam]
+    )
+
+demo.launch()