Update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import numpy as np
-from PIL import Image, ImageFilter, ImageStat
+from PIL import Image
 import tensorflow as tf
 
 # Load TFLite model
@@ -15,38 +15,8 @@ output_details = interpreter.get_output_details()
 class_names = ['Dyskeratotic', 'Koilocytotic', 'Metaplastic', 'Parabasal', 'Superficial-Intermediat']
 CONFIDENCE_THRESHOLD = 0.25
 
-def is_valid_cervical_image(image):
-    """Basic smart validation: checks variance, edges, brightness"""
-    # Convert to grayscale
-    gray = image.convert("L")
-    stat = ImageStat.Stat(gray)
-    
-    # Variance check (texture)
-    variance = stat.var[0]
-    if variance < 500:  # threshold, tweak as needed
-        return False, "Image lacks texture. Upload a proper cervical cell image."
-
-    # Edge detection check
-    edges = gray.filter(ImageFilter.FIND_EDGES)
-    edge_data = np.array(edges)
-    edge_pixels = np.sum(edge_data > 50)
-    if edge_pixels < 1000:  # too few edges
-        return False, "Image has too few edges. Upload a clear cervical cell image."
-
-    # Brightness check
-    brightness = stat.mean[0]
-    if brightness < 30 or brightness > 220:
-        return False, "Image brightness/contrast is not suitable. Adjust and upload again."
-
-    return True, ""
-
 def predict_image(image):
     try:
-        # Validate input
-        valid, message = is_valid_cervical_image(image)
-        if not valid:
-            return {"Error": 1.0}, f"⚠️ {message}"
-
         # Preprocess
         image = image.resize((224, 224)).convert("RGB")
         img_array = np.array(image, dtype=np.float32) / 255.0
@@ -55,27 +25,35 @@ def predict_image(image):
         # Run inference
         interpreter.set_tensor(input_details[0]['index'], img_array)
         interpreter.invoke()
-        output = interpreter.get_tensor(output_details[0]['index'])[0]
+        output = interpreter.get_tensor(output_details[0]['index'])[0]  # shape (num_classes,)
 
-        # Normalize
+        # Normalize if needed (sometimes TFLite outputs logits)
         probs = tf.nn.softmax(output).numpy()
 
-        # Confidence check
-        if np.max(probs) < CONFIDENCE_THRESHOLD:
-            return {"Error": 1.0}, "⚠️ The model is unsure. Please upload a clearer cervical cell image."
+        # Get predicted class
+        class_idx = int(np.argmax(probs))
+        confidence = float(np.max(probs))
+
+        # 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)
 
-        # Convert to dict for Gradio Label
-        probs_dict = {class_names[i]: float(probs[i]) for i in range(len(class_names))}
-        return probs_dict, f"✅ Prediction: {class_names[np.argmax(probs)]} ({np.max(probs)*100:.2f}%)"
+        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}"
 
     except Exception as e:
-        return {"Error": 1.0}, f"⚠️ Something went wrong. Please upload a correct cervical cell image. ({str(e)})"
+        return f"Error: {str(e)}"
 
 # Gradio UI
 gr.Interface(
     fn=predict_image,
     inputs=gr.Image(type="pil"),
-    outputs=[gr.Label(num_top_classes=len(class_names)), gr.Textbox()],
-    title="Cervical Cancer Classification",
-    description="Upload a cervical cell image. The model shows probabilities for each class and warns if the image is incorrect."
+    outputs="text",
+    title="Muscle Disease Detection",
+    description="Upload an MRI image to detect muscle conditions."
 ).launch()