Update AI_tool.py

AI_tool.py

@@ -3,20 +3,14 @@ import json
 
 
 def show_message(title, content):
-    """
-    A simple function to simulate showing a message.
-    In a real application, this would be a UI element or logging.
-    """
+    
     print(f"\n--- {title} ---")
     print(content)
     print("-----------------\n")
 
 
 def set_processing_state(is_processing):
-    """
-    Simulates enabling/disabling UI elements during processing.
-    In a real application, this would update a GUI or web interface.
-    """
+    
     if is_processing:
         print("Processing... Please wait.")
     else:
@@ -24,12 +18,7 @@ def set_processing_state(is_processing):
 
 
 async def generate_solution_python(user_query):
-    """
-    Generates a solution using Google Search for context and Gemini LLM.
-
-    Args:
-        user_query (str): The query provided by the user.
-    """
+    
     if not user_query:
         show_message("Input Required", "Please enter your query to get a solution.")
         return
@@ -87,8 +76,7 @@ Please provide a detailed and helpful solution, incorporating the provided infor
         llm_payload = {
             "contents": chat_history
         }
-        # API key is automatically provided by the Canvas environment for gemini-2.0-flash
-        # If running outside Canvas, you would need to provide your API key here.
+       
         gemini_api_url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key="
 
         gemini_response = requests.post(
@@ -124,11 +112,3 @@ Please provide a detailed and helpful solution, incorporating the provided infor
         print(response_text)
         print("----------------\n")
 
-# Example usage (how you would call this function)
-# To run this, you would need an environment that provides the /api/google_search endpoint
-# and handles the Gemini API key. In the Canvas environment, these are typically
-# available when running Python code that interacts with the tools.
-
-# You can test this in a Python environment by calling the function:
-# import asyncio
-# asyncio.run(generate_solution_python("What are the benefits of renewable energy and what are some recent innovations in solar power?"))