Update app.py

app.py

@@ -21,6 +21,17 @@ def preprocess_image(img: Image.Image):
 
 def classify_image(image):
     try:
+        img = image.convert("RGB")
+        arr = np.asarray(img).astype(np.float32)
+        brightness = arr.mean()
+        contrast = arr.std()
+
+        # 🧠 Simple sanity check for stool-like features (brownish tone + moderate contrast)
+        # You can adjust these thresholds depending on your dataset
+        if brightness > 220 or brightness < 20 or contrast < 25:
+            return {"Not stool image": 1.0}
+
+        # Normal prediction process
         input_data = preprocess_image(image)
         interpreter.set_tensor(input_details[0]['index'], input_data)
         interpreter.invoke()
@@ -28,9 +39,11 @@ def classify_image(image):
         results = {labels[i]: float(output_data[i]) for i in range(len(labels))}
         sorted_results = dict(sorted(results.items(), key=lambda x: x[1], reverse=True))
         return sorted_results
+
     except Exception as e:
         return {"error": str(e)}
 
+
 demo = gr.Interface(
     fn=classify_image,
     inputs=gr.Image(type="pil", label="Upload stool image"),