Update app.py

app.py

@@ -10,72 +10,93 @@ interpreter.allocate_tensors()
 input_details = interpreter.get_input_details()
 output_details = interpreter.get_output_details()
 
-# 🏷️ Define your classes
+# 🏷️ Labels of your stool model
 labels = ["bloody", "hard stool", "normal", "parasite", "watery"]
 
-# 🧩 Image preprocessing
+# 💬 Diagnosis summaries for each label
+diagnosis_advice = {
+    "bloody": "Possible hemorrhagic gastroenteritis. ⚠️ Visit a vet immediately as it may indicate internal bleeding or infection.",
+    "hard stool": "Your pet may be constipated. 💧 Encourage hydration and increase dietary fiber intake.",
+    "normal": "✅ Healthy stool detected. Your pet appears to be in good digestive health.",
+    "parasite": "⚠️ Possible tapeworm or other parasite infection. Schedule deworming and a vet check-up.",
+    "watery": "Possible diarrhea. 💧 Ensure hydration and monitor for dehydration or weakness.",
+    "Not stool image": "❌ This image doesn’t appear to be a stool sample. Please upload a clearer photo of stool.",
+    "Uncertain / unclear stool image": "⚠️ The image is unclear or confidence is low. Try retaking the photo in better lighting."
+}
+
+# 🧩 Preprocess image for TFLite
 def preprocess_image(img: Image.Image):
     img = img.convert("RGB").resize((128, 128))
     arr = np.asarray(img).astype(np.float32) / 255.0
     arr = np.expand_dims(arr, axis=0)
     return arr
 
-# 🚫 Detect if the uploaded image is NOT a stool image
+# 🚫 Detection filter for non-stool images
 def is_not_stool_image(image):
     arr = np.asarray(image.convert("RGB")).astype(np.float32)
     brightness = arr.mean()
     contrast = arr.std()
     avg_color = arr.mean(axis=(0, 1))
 
-    # 🧠 Basic heuristic checks
-    # These values are adjustable based on your dataset
     if brightness > 220 or brightness < 25:
-        return True  # too bright or dark
+        return True  # too bright or too dark
     if contrast < 25:
-        return True  # too flat / low texture
+        return True  # too flat / no texture
     if avg_color[0] > 180 and avg_color[1] < 80 and avg_color[2] < 80:
-        return True  # too red
-    if avg_color[0] < 50 and avg_color[1] > 180:
+        return True  # too reddish
+    if avg_color[1] > 200 and avg_color[0] < 100:
         return True  # too greenish
     if avg_color[2] > 200:
-        return True  # too blueish
-
+        return True  # too bluish
     return False
 
-# 🧠 Classification function
+# 🧠 Main classification logic
 def classify_image(image):
     try:
-        # 🚫 Check if this is not stool
+        # Step 1: Validate stool image
         if is_not_stool_image(image):
-            return {"Not stool image": 1.0}
+            return {"Not stool image": 1.0}, diagnosis_advice["Not stool image"]
 
-        # ✅ Proceed with model prediction
+        # Step 2: Run TFLite model
         input_data = preprocess_image(image)
         interpreter.set_tensor(input_details[0]['index'], input_data)
         interpreter.invoke()
         output_data = interpreter.get_tensor(output_details[0]['index'])[0]
 
-        # Sort predictions
+        # Step 3: Sort predictions by confidence
         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))
-
-        # Extra sanity rule: if top score < 0.4, label as uncertain
         top_label, top_score = list(sorted_results.items())[0]
+
+        # Step 4: Handle low confidence
         if top_score < 0.4:
-            return {"Uncertain / unclear stool image": top_score}
+            return {"Uncertain / unclear stool image": top_score}, diagnosis_advice["Uncertain / unclear stool image"]
+
+        # Step 5: Add readable confidence percentages
+        formatted_results = {
+            f"{label} ({score * 100:.2f}%)": score for label, score in sorted_results.items()
+        }
 
-        return sorted_results
+        # Step 6: Return label + human diagnosis
+        advice = diagnosis_advice.get(top_label, "No advice available for this diagnosis.")
+        return formatted_results, advice
 
     except Exception as e:
-        return {"Error": str(e)}
+        return {"Error": str(e)}, f"⚠️ Error encountered: {str(e)}"
 
-# 🎨 Gradio UI
+# 🎨 Gradio Interface
 demo = gr.Interface(
     fn=classify_image,
     inputs=gr.Image(type="pil", label="📸 Upload stool image"),
-    outputs=gr.Label(num_top_classes=3, label="Predicted diagnosis"),
-    title="🐾 Stool Diagnosis AI",
-    description="Upload a stool image for analysis. The model predicts stool type or rejects unrelated photos."
+    outputs=[
+        gr.Label(num_top_classes=3, label="Predicted Diagnosis & Confidence"),
+        gr.Textbox(label="💬 Diagnosis Summary", lines=3),
+    ],
+    title="🐾 Pet Stool Diagnosis AI (with Confidence & Advice)",
+    description=(
+        "Upload a stool image to get an AI-based diagnosis with confidence scores and health advice. "
+        "This tool checks image validity, identifies stool condition, and provides next-step guidance."
+    ),
 )
 
 if __name__ == "__main__":