Update app.py

app.py

@@ -161,60 +161,109 @@
 
 import requests
 import gradio as gr
+import logging
+import json
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 # API key and user ID for on-demand
 api_key = 'KGSjxB1uptfSk8I8A7ciCuNT9Xa3qWC3'
 external_user_id = 'plugin-1717464304'
 
-# Step 1: Create a chat session with the API
 def create_chat_session():
-    create_session_url = 'https://api.on-demand.io/chat/v1/sessions'
-    create_session_headers = {
-        'apikey': api_key
-    }
-    create_session_body = {
-        "pluginIds": [],
-        "externalUserId": external_user_id
-    }
-    response = requests.post(create_session_url, headers=create_session_headers, json=create_session_body)
-    response_data = response.json()
-    session_id = response_data['data']['id']
-    return session_id
-
-# Step 2: Submit query to the API
-def submit_query(session_id, 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()
-
-# Function to handle patient info, query, and image processing (now focusing on LLM)
-def gradio_interface(patient_info, query_type):
-    # Call LLM with patient info and query
-    session_id = create_chat_session()
-    query = f"Patient Info: {patient_info}\nQuery Type: {query_type}"
-    llm_response = submit_query(session_id, query)
+    try:
+        create_session_url = 'https://api.on-demand.io/chat/v1/sessions'
+        create_session_headers = {
+            'apikey': api_key,
+            'Content-Type': 'application/json'
+        }
+        create_session_body = {
+            "pluginIds": [],
+            "externalUserId": external_user_id
+        }
+        
+        logger.info("Creating chat session...")
+        response = requests.post(create_session_url, headers=create_session_headers, json=create_session_body)
+        response.raise_for_status()  # Raise an exception for bad status codes
+        
+        response_data = response.json()
+        logger.info(f"Session created successfully: {json.dumps(response_data, indent=2)}")
+        
+        session_id = response_data['data']['id']
+        return session_id
     
-    # Debug: Print the full response to inspect it
-    print("LLM Response:", llm_response)  # This will print the full response for inspection
+    except requests.exceptions.RequestException as e:
+        logger.error(f"Error creating chat session: {str(e)}")
+        if hasattr(e.response, 'text'):
+            logger.error(f"Response content: {e.response.text}")
+        raise
 
-    # Safely handle 'message' if it exists
-    message = llm_response.get('data', {}).get('message', 'No message returned from LLM')
-
-    # Check if message is empty and print the complete response if necessary
-    if message == 'No message returned from LLM':
-        print("Full LLM Response Data:", llm_response)  # Inspect the full LLM response for any helpful info
+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,
+            'Content-Type': 'application/json'
+        }
+        submit_query_body = {
+            "endpointId": "predefined-openai-gpt4o",
+            "query": query,
+            "pluginIds": ["plugin-1712327325", "plugin-1713962163"],
+            "responseMode": "sync"
+        }
+        
+        logger.info(f"Submitting query for session {session_id}")
+        logger.info(f"Query content: {query}")
+        
+        response = requests.post(submit_query_url, headers=submit_query_headers, json=submit_query_body)
+        response.raise_for_status()
+        
+        response_data = response.json()
+        logger.info(f"Query response received: {json.dumps(response_data, indent=2)}")
+        return response_data
+    
+    except requests.exceptions.RequestException as e:
+        logger.error(f"Error submitting query: {str(e)}")
+        if hasattr(e.response, 'text'):
+            logger.error(f"Response content: {e.response.text}")
+        raise
 
-    response = f"Patient Info: {patient_info}\nQuery Type: {query_type}\n\nLLM Response:\n{message}"
-    return response
+def gradio_interface(patient_info, query_type):
+    try:
+        # Create session
+        session_id = create_chat_session()
+        
+        # Construct query
+        query = f"Patient Info: {patient_info}\nQuery Type: {query_type}"
+        
+        # Submit query and get response
+        llm_response = submit_query(session_id, query)
+        
+        # Enhanced response handling
+        if not llm_response:
+            logger.error("Empty response received from LLM")
+            return "Error: No response received from the LLM service"
+        
+        # Navigate the response structure with detailed logging
+        logger.info(f"Processing LLM response: {json.dumps(llm_response, indent=2)}")
+        
+        if 'data' not in llm_response:
+            logger.error("Response missing 'data' field")
+            return f"Error: Unexpected response structure\nFull response: {json.dumps(llm_response, indent=2)}"
+        
+        message = llm_response.get('data', {}).get('message')
+        if not message:
+            logger.error("No message found in response data")
+            return f"Error: No message in response\nFull response: {json.dumps(llm_response, indent=2)}"
+        
+        response = f"Patient Info: {patient_info}\nQuery Type: {query_type}\n\nLLM Response:\n{message}"
+        return response
+    
+    except Exception as e:
+        logger.error(f"Error in gradio_interface: {str(e)}", exc_info=True)
+        return f"Error processing request: {str(e)}"
 
 # Gradio interface
 iface = gr.Interface(
@@ -236,4 +285,5 @@ iface = gr.Interface(
     description="Provide patient information and a query type for analysis by the LLM."
 )
 
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()