Update app.py

app.py

@@ -37,53 +37,38 @@ def create_chat_session():
     return session_id
 
 def submit_query(session_id, query):
-    try:
-        submit_query_url = f'https://api.on-demand.io/chat/v1/sessions/{session_id}/query'
-        submit_query_headers = {
-            'apikey': api_key
-        }
-        submit_query_body = {
-            "endpointId": "predefined-openai-gpt4o",
-            "query": query,
-            "pluginIds": ["plugin-1712327325", "plugin-1713962163"],
-            "responseMode": "sync"
-        }
-
-        # Log the query sent to the API
-        logger.info(f"Sending query to LLM API: {query}")
+    submit_query_url = f'https://api.on-demand.io/chat/v1/sessions/{session_id}/query'
+    submit_query_headers = {
+        'apikey': api_key
+    }
+    submit_query_body = {
+        "endpointId": "predefined-openai-gpt4o",
+        "query": query,
+        "pluginIds": ["plugin-1712327325", "plugin-1713962163"],
+        "responseMode": "sync"
+    }
+    response = requests.post(submit_query_url, headers=submit_query_headers, json=submit_query_body)
+    return response.json()
 
-        response = requests.post(submit_query_url, headers=submit_query_headers, json=submit_query_body)
-        logger.info(f"LLM API response status: {response.status_code}")
-        
-        if response.status_code != 200:
-            logger.error(f"LLM API call failed with status code {response.status_code}")
-            return None
-        
-        # Return the JSON response
-        return response.json()
-
-    except Exception as e:
-        logger.error(f"Error submitting query to LLM API: {str(e)}")
-        raise
 
 def extract_json_from_answer(answer):
     """Extract and clean JSON from the LLM response"""
     try:
-        # Try to parse the answer directly as JSON
-        logger.info(f"Trying to parse the LLM answer as JSON: {answer}")
+        # First try to parse the answer directly
+
         return json.loads(answer)
     except json.JSONDecodeError:
-        logger.warning("Direct JSON parse failed, trying to extract JSON content")
+
         try:
-            # Attempt to find and parse JSON content within the answer
+            # If that fails, try to find JSON content and parse it
             start_idx = answer.find('{')
             end_idx = answer.rfind('}') + 1
             if start_idx != -1 and end_idx != 0:
                 json_str = answer[start_idx:end_idx]
-                logger.info(f"Extracted JSON string: {json_str}")
+
                 return json.loads(json_str)
-        except (json.JSONDecodeError, ValueError) as parse_error:
-            logger.error(f"Failed to parse JSON from response: {str(parse_error)}")
+        except (json.JSONDecodeError, ValueError):
+            logger.error("Failed to parse JSON from response")
             raise
 
 def load_model():
@@ -195,7 +180,9 @@ def gradio_interface(patient_info, image):
         
         # Extract and clean JSON from the response
         json_data = extract_json_from_answer(llm_response['data']['answer'])
-        
+        logger.info(f"llm_response: {llm_response}")
+        logger.info(f"llm_response[data]: {llm_response['data']}")
+        logger.info(f"llm_response[data][answer]: {llm_response['data']['answer']}")
         return json.dumps(json_data, indent=2)
         
     except Exception as e:
@@ -207,105 +194,41 @@ def gradio_interface(patient_info, image):
         }, indent=2)
 
 
-def gradio_interface(patient_info, image):
-    try:
-        if model is None:
-            logger.error("Model is not initialized")
-            return json.dumps({
-                "error": "Model initialization failed. Please check the logs for details.",
-                "status": "error"
-            }, indent=2)
-
-        classes = ["Alzheimer's", "Stroke", "Tumor", "Normal"]
-        image_analysis = None
-
-        # Process the image if provided
-        if image is not None:
-            logger.info("Processing uploaded image")
-            processed_image = preprocess_image(image)
-            
-            logger.info("Running model prediction")
-            prediction = model.predict(processed_image)
-            
-            logger.info(f"Raw prediction: {prediction}")
-            # Assume the first prediction corresponds to the class
-            predicted_class_index = int(np.argmax(prediction))
-            predicted_confidence = np.max(prediction) * 100
-            
-            image_analysis = {
-                "prediction": classes[predicted_class_index],
-                "confidence": predicted_confidence
-            }
-            logger.info(f"Image analysis results: {image_analysis}")
-
-        # Append image analysis to patient info
-        patient_info += f"\nPrediction based on MRI images: {image_analysis['prediction']}, Confidence: {image_analysis['confidence']}"
-
-        # Log the patient info sent to the API
-        logger.info(f"Submitting the following patient info to LLM: {patient_info}")
-
-        # Create a session and submit the query
-        session_id = create_chat_session()
-        llm_response = submit_query(session_id, patient_info)
-
-        # Log the raw response from the LLM API
-        logger.info(f"LLM API response: {llm_response}")
-
-        if not llm_response or 'data' not in llm_response or 'answer' not in llm_response['data']:
-            logger.error("Invalid response structure from LLM")
-            return json.dumps({
-                "error": "Invalid LLM response structure.",
-                "status": "error"
-            }, indent=2)
-        
-        # Extract the answer and parse the JSON from the response
-        json_data = extract_json_from_answer(llm_response['data']['answer'])
-
-        return json.dumps(json_data, indent=2)
-        
-    except Exception as e:
-        logger.error(f"Error in gradio_interface: {str(e)}")
-        return json.dumps({
-            "error": str(e),
-            "status": "error",
-            "details": "Check the application logs for more information"
-        }, indent=2)
-
-# # Gradio interface
-# iface = gr.Interface(
-#     fn=gradio_interface,
-#     inputs=[
-#         gr.Textbox(
-#             label="Patient Information",
-#             placeholder="Enter patient details including: symptoms, medical history, current medications, age, gender, and any relevant test results...",
-#             lines=5,
-#             max_lines=10
-#         ),
-#         gr.Image(
-#             label="Medical Image",
-#             type="pil",
-#             interactive=True
-#         )
-#     ],
-#     outputs=gr.Textbox(
-#         label="Medical Analysis",
-#         placeholder="JSON analysis will appear here...",
-#         lines=15
-#     ),
-#     title="Medical Diagnosis Assistant",
-#     description="Enter patient information and optionally upload a medical image for analysis."
-# )
+# Gradio interface
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Textbox(
+            label="Patient Information",
+            placeholder="Enter patient details including: symptoms, medical history, current medications, age, gender, and any relevant test results...",
+            lines=5,
+            max_lines=10
+        ),
+        gr.Image(
+            label="Medical Image",
+            type="pil",
+            interactive=True
+        )
+    ],
+    outputs=gr.Textbox(
+        label="Medical Analysis",
+        placeholder="JSON analysis will appear here...",
+        lines=15
+    ),
+    title="Medical Diagnosis Assistant",
+    description="Enter patient information and optionally upload a medical image for analysis."
+)
 
-# if __name__ == "__main__":
-#     # Add version information logging
-#     logger.info(f"TensorFlow Keras version: {tf_keras.__version__}")
-#     logger.info(f"TensorFlow Hub version: {hub.__version__}")
-#     logger.info(f"Gradio version: {gr.__version__}")
+if __name__ == "__main__":
+    # Add version information logging
+    logger.info(f"TensorFlow Keras version: {tf_keras.__version__}")
+    logger.info(f"TensorFlow Hub version: {hub.__version__}")
+    logger.info(f"Gradio version: {gr.__version__}")
     
-#     iface.launch(
-#         server_name="0.0.0.0",
-#         debug=True
-#     )
+    iface.launch(
+        server_name="0.0.0.0",
+        debug=True
+    )