Update app.py

app.py

@@ -15,42 +15,30 @@ output_details = interpreter.get_output_details()
 class_names = ['Dyskeratotic', 'Koilocytotic', 'Metaplastic', 'Parabasal', 'Superficial-Intermediat']
 CONFIDENCE_THRESHOLD = 0.7
 
-def preprocess_image(image):
-    image = image.resize((224, 224)).convert("RGB")
-    img_array = np.array(image, dtype=np.float32) / 255.0
-    return np.expand_dims(img_array, axis=0)
-
 def predict_image(image):
     try:
-        img_array = preprocess_image(image)
+        image = image.resize((128, 128)).convert("RGB")
+        img_array = np.array(image, dtype=np.float32) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
 
-        # Inference
         interpreter.set_tensor(input_details[0]['index'], img_array)
         interpreter.invoke()
         output = interpreter.get_tensor(output_details[0]['index'])
 
-        # Normal softmax
-        probabilities = tf.nn.softmax(output[0]).numpy()
-        class_idx = int(np.argmax(probabilities))
-        confidence = float(np.max(probabilities))
+        class_idx = int(np.argmax(output))
+        confidence = float(np.max(output))
 
-        if confidence >= CONFIDENCE_THRESHOLD:
-            return f"✅ Prediction: {class_names[class_idx]} (Confidence: {confidence:.2f})"
+        if confidence < CONFIDENCE_THRESHOLD:
+            return f"⚠️ Low confidence ({confidence:.2f}). The model is unsure. Please try a clearer image."
         else:
-            # Show top-2 if low confidence
-            top2_idx = np.argsort(probabilities)[-2:][::-1]
-            suggestion = ", ".join(
-                [f"{class_names[i]} ({probabilities[i]:.2f})" for i in top2_idx]
-            )
-            return f"⚠️ Low confidence ({confidence:.2f}).\nPossible classes: {suggestion}"
-
+            return f"✅ Prediction: {class_names[class_idx]} (Confidence: {confidence:.2f})"
     except Exception as e:
-        return f"❌ Error: {str(e)}"
+        return f"Error: {str(e)}"
 
 gr.Interface(
     fn=predict_image,
-    inputs=gr.Image(type="pil", label="Upload Cervical Cell Image"),
+    inputs=gr.Image(type="pil"),
     outputs="text",
-    title="Cervical Cancer Cell Classification",
-    description="Upload a cervical cell image. The model predicts the cell type with confidence scores."
+    title="Muscle Disease Detection",
+    description="Upload an MRI image to detect muscle conditions."
 ).launch()