Update app.py

app.py

@@ -14,30 +14,49 @@ output_details = interpreter.get_output_details()
 class_names = ['Dyskeratotic', 'Koilocytotic', 'Metaplastic', 'Parabasal', 'Superficial-Intermediat']
 CONFIDENCE_THRESHOLD = 0.7
 
+def preprocess_image(image):
+    """Preprocess input image for model"""
+    image = image.resize((224, 224)).convert("RGB")
+    img_array = np.array(image, dtype=np.float32)
+
+    # 🔑 IMPORTANT: Check model input scale
+    if input_details[0]['dtype'] == np.uint8:
+        img_array = np.expand_dims(img_array, axis=0).astype(np.uint8)
+    else:
+        img_array = img_array / 255.0
+        img_array = np.expand_dims(img_array, axis=0).astype(np.float32)
+
+    return img_array
+
 def predict_image(image):
     try:
-        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)
+        # Preprocess
+        img_array = preprocess_image(image)
 
+        # Inference
         interpreter.set_tensor(input_details[0]['index'], img_array)
         interpreter.invoke()
         output = interpreter.get_tensor(output_details[0]['index'])
 
-        class_idx = int(np.argmax(output))
-        confidence = float(np.max(output))
+        # Ensure output is softmax
+        probabilities = tf.nn.softmax(output[0]).numpy()
+
+        class_idx = int(np.argmax(probabilities))
+        confidence = float(np.max(probabilities))
 
         if confidence < CONFIDENCE_THRESHOLD:
-            return f"⚠️ Low confidence ({confidence:.2f}). The model is unsure. Please try a clearer image."
+            return f"⚠️ Low confidence ({confidence:.2f}). Try another clearer image."
         else:
             return f"✅ Prediction: {class_names[class_idx]} (Confidence: {confidence:.2f})"
+
     except Exception as e:
-        return f"Error: {str(e)}"
+        return f"❌ Error: {str(e)}"
 
+# Gradio app
 gr.Interface(
     fn=predict_image,
-    inputs=gr.Image(type="pil"),
+    inputs=gr.Image(type="pil", label="Upload Cervical Cell Image"),
     outputs="text",
-    title="Cervical Cancer Classification",
-    description="Upload an image to detect cervical cancer."
+    title="Cervical Cancer Cell Classification",
+    description="Upload an image to classify cervical cells using a TFLite model."
 ).launch()