Update app.py

app.py

@@ -27,33 +27,22 @@ def predict_image(image):
         interpreter.invoke()
         output = interpreter.get_tensor(output_details[0]['index'])[0]  # shape (num_classes,)
 
-        # Normalize if needed (sometimes TFLite outputs logits)
+        # Normalize if needed
         probs = tf.nn.softmax(output).numpy()
 
-        # Get predicted class
-        class_idx = int(np.argmax(probs))
-        confidence = float(np.max(probs))
+        # Convert to dict for Gradio Label
+        probs_dict = {class_names[i]: float(probs[i]) for i in range(len(class_names))}
 
-        # Format output (show every class probability)
-        results = []
-        for i, prob in enumerate(probs):
-            results.append(f"{class_names[i]}: {prob*100:.2f}%")
-
-        results_text = "\n".join(results)
-
-        if confidence < CONFIDENCE_THRESHOLD:
-            return f"⚠️ Low confidence ({confidence:.2f}). The model is unsure.\n\nProbabilities:\n{results_text}"
-        else:
-            return f"✅ Prediction: {class_names[class_idx]} ({confidence*100:.2f}%)\n\nProbabilities:\n{results_text}"
+        return probs_dict
 
     except Exception as e:
-        return f"Error: {str(e)}"
+        return {"Error": 1.0}  # dummy error output
 
 # Gradio UI
 gr.Interface(
     fn=predict_image,
     inputs=gr.Image(type="pil"),
-    outputs="text",
-    title="Muscle Disease Detection",
-    description="Upload an MRI image to detect muscle conditions."
+    outputs=gr.Label(num_top_classes=len(class_names)),  # shows all classes with bars
+    title="Cervical Cancer Classification",
+    description="Upload an image. The model shows probabilities for each class."
 ).launch()