Update chatbot_handler.py

chatbot_handler.py

@@ -2,10 +2,10 @@
 import logging
 import json
 import aiohttp # Using asynchronous aiohttp
-import os
+# import os # No longer needed for API key from environment
 
-# Ensure GROQ_API_KEY is set in your environment variables
-api_key = os.getenv('GEMINI_API_KEY')
+# Ensure GEMINI_API_KEY is set in your environment variables
+# api_key = os.getenv('GEMINI_API_KEY') # Removed: API key will be empty string, provided by Canvas
 
 def format_history_for_gemini(gradio_chat_history: list) -> list:
     """
@@ -38,18 +38,15 @@ async def generate_llm_response(user_message: str, plot_id: str, plot_label: str
     """
     logging.info(f"Generating LLM response for plot_id: {plot_id} ('{plot_label}'). User message: '{user_message}'")
 
-    # The chat_history_for_plot already contains the full conversation including the latest user message.
-    # The initial system-like prompt is the first message from the assistant in the history.
     gemini_formatted_history = format_history_for_gemini(chat_history_for_plot)
 
     if not gemini_formatted_history:
         logging.error("Cannot generate LLM response: Formatted history is empty.")
         return "I'm sorry, there was an issue processing the conversation history."
 
-    # Construct payload for Gemini API
     payload = {
         "contents": gemini_formatted_history,
-        "generationConfig": { # Optional: configure generation parameters
+        "generationConfig": { 
             "temperature": 0.7,
             "topK": 1,
             "topP": 1,
@@ -57,9 +54,10 @@ async def generate_llm_response(user_message: str, plot_id: str, plot_label: str
         }
     }
     
-
-    # Using gemini-2.0-flash as per instructions
-    apiUrl = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-preview-05-20:generateContent?key={api_key}"
+    # API key is an empty string. Canvas will automatically provide it in runtime.
+    apiKey = "" 
+    # Using gemini-2.0-flash as per general instructions
+    apiUrl = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key={apiKey}"
 
     async with aiohttp.ClientSession() as session:
         try:
@@ -69,7 +67,6 @@ async def generate_llm_response(user_message: str, plot_id: str, plot_label: str
 
                 if resp.status != 200:
                     error_detail = response_json.get('error', {}).get('message', 'Unknown API error')
-                    # Check for specific content policy block from Gemini, even on error status
                     if response_json.get("promptFeedback") and response_json["promptFeedback"].get("blockReason"):
                          reason = response_json["promptFeedback"]["blockReason"]
                          safety_ratings_info = [f"{rating['category']}: {rating['probability']}" for rating in response_json['promptFeedback'].get('safetyRatings', [])]
@@ -86,7 +83,6 @@ async def generate_llm_response(user_message: str, plot_id: str, plot_label: str
                     response_text = response_json["candidates"][0]["content"]["parts"][0]["text"]
                     logging.info(f"LLM generated response for '{plot_label}': {response_text[:100]}...")
                     return response_text
-                # Check for promptFeedback even on 200 if candidates are missing (e.g. blocked content)
                 elif response_json.get("promptFeedback") and response_json["promptFeedback"].get("blockReason"):
                     reason = response_json["promptFeedback"]["blockReason"]
                     safety_ratings_info = [f"{rating['category']}: {rating['probability']}" for rating in response_json['promptFeedback'].get('safetyRatings', [])]