Update app.py

app.py

@@ -9,25 +9,28 @@ import json
 # Load the model
 try:
     model = load_model('wound_classifier_model_googlenet.h5')
+    print("✅ Model loaded successfully")
 except Exception as e:
-    raise RuntimeError(f"Error loading model: {e}")
+    raise RuntimeError(f"❌ Model loading failed: {e}")
 
 # OpenRouter.ai Configuration
 OPENROUTER_API_KEY = "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"
 OPENROUTER_API_URL = "https://openrouter.ai/api/v1/chat/completions"
-MODEL_NAME = "mistralai/mistral-small-24b-instruct-2501:free"
+MODEL_NAME = "mistralai/mistral-7b-instruct"  # Updated model name
 
 input_shape = (224, 224, 3)
 
 def preprocess_image(image, target_size):
     """Preprocess the input image for the model."""
-    if image is None:
-        raise ValueError("No image provided")
-    image = image.convert("RGB")
-    image = image.resize(target_size)
-    image_array = np.array(image)
-    image_array = image_array / 255.0
-    return image_array
+    try:
+        if image is None:
+            raise ValueError("No image provided")
+        image = image.convert("RGB")
+        image = image.resize(target_size)
+        return np.array(image) / 255.0
+    except Exception as e:
+        print(f"⚠️ Image preprocessing error: {e}")
+        raise
 
 def get_medical_guidelines(wound_type):
     """Fetch medical guidelines using OpenRouter.ai's API."""
@@ -39,20 +42,37 @@ def get_medical_guidelines(wound_type):
     }
     
     prompt = f"""As a medical professional, provide detailed guidelines for treating a {wound_type} wound. 
-    Include first aid steps, precautions, and when to seek professional help."""
+    Include: 
+    1. First aid steps
+    2. Precautions
+    3. When to seek professional help
+    Output in markdown with clear sections."""
     
     data = {
         "model": MODEL_NAME,
         "messages": [{"role": "user", "content": prompt}],
-        "temperature": 0.7
+        "temperature": 0.5
     }
     
     try:
-        response = requests.post(OPENROUTER_API_URL, headers=headers, json=data)
+        print(f"🚀 Sending request to OpenRouter API for {wound_type}...")
+        response = requests.post(OPENROUTER_API_URL, headers=headers, json=data, timeout=10)
         response.raise_for_status()
-        return response.json()["choices"][0]["message"]["content"]
+        
+        response_json = response.json()
+        print("🔧 Raw API response:", json.dumps(response_json, indent=2))
+        
+        if "choices" not in response_json:
+            return "⚠️ API response format unexpected. Please check logs."
+            
+        return response_json["choices"][0]["message"]["content"]
+        
+    except requests.exceptions.HTTPError as e:
+        print(f"❌ HTTP Error: {e.response.status_code} - {e.response.text}")
+        return f"API Error: {e.response.status_code} - Check console for details"
     except Exception as e:
-        return f"Error fetching guidelines: {str(e)}"
+        print(f"⚠️ General API error: {str(e)}")
+        return f"Error: {str(e)}"
 
 def predict(image):
     """Main prediction function."""
@@ -60,38 +80,51 @@ def predict(image):
         # Preprocess image
         input_data = preprocess_image(image, (input_shape[0], input_shape[1]))
         input_data = np.expand_dims(input_data, axis=0)
+        print("🖼️ Image preprocessed successfully")
 
         # Load class labels
-        with open('classes.txt', 'r') as file:
-            class_labels = file.read().splitlines()
+        try:
+            with open('classes.txt', 'r') as file:
+                class_labels = file.read().splitlines()
+            print("📋 Class labels loaded:", class_labels)
+        except Exception as e:
+            raise RuntimeError(f"Class labels loading failed: {e}")
 
+        # Verify model compatibility
         if len(class_labels) != model.output_shape[-1]:
-            raise ValueError("Class labels mismatch with model output")
+            raise ValueError(f"Model expects {model.output_shape[-1]} classes, found {len(class_labels)}")
 
         # Make prediction
         predictions = model.predict(input_data)
-        results = {class_labels[i]: float(predictions[0][i]) for i in range(len(class_labels))}
+        print("📊 Raw predictions:", predictions)
+        
+        results = {class_labels[i]: float(predictions[0][i]) 
+                 for i in range(len(class_labels))}
         predicted_class = max(results, key=results.get)
+        print(f"🏆 Predicted class: {predicted_class}")
 
         # Get medical guidelines
         guidelines = get_medical_guidelines(predicted_class)
+        print("📜 Guidelines generated successfully")
 
-        return results, guidelines  # Return as tuple instead of dict
+        return results, guidelines
 
     except Exception as e:
-        return {f"Error: {str(e)}"}, ""
+        print(f"🔥 Critical error in prediction: {str(e)}")
+        return {"Error": str(e)}, ""
 
 # Gradio Interface
 iface = gr.Interface(
     fn=predict, 
-    inputs=gr.Image(type="pil"), 
+    inputs=gr.Image(type="pil", label="Upload Wound Image"), 
     outputs=[
         gr.Label(num_top_classes=3, label="Classification Results"),
-        gr.Textbox(label="Medical Guidelines", lines=5)
+        gr.Markdown(label="Medical Guidelines")
     ],
-    live=True,
+    live=False,
     title="Wound Classification & Treatment Advisor",
-    description="Upload a wound image for classification and medical guidelines."
+    description="Upload a wound image for AI-powered classification and treatment guidelines.",
+    allow_flagging="never"
 )
 
 iface.launch(server_name="0.0.0.0", server_port=7860)