browse url infinite loading fixed with backend check

orchestrator_agent.py

@@ -103,8 +103,8 @@ Capabilities:
 - Available chart libraries are: matplotlib and seaborn
 - If user asks for a specific chart type that is not available (ex: plotly, bokeh etc.), provide a clear explanation.
 - Verify CSV structure; handle missing data by asking clarifications or stating clear assumptions.
+- If user asks a qustion which can be related to the dataset then use the tools before cross-checking with the user.
 - Read the question carefully what the user is asking for.
-- Use the tools before responding.
 - Optimize the questions of the user before sending the questions to the tools.
 - Generate visualizations as: ![Description](direct-url).
 
@@ -154,147 +154,3 @@ def csv_orchestrator_chat(csv_url: str, user_question: str, conversation_history
     return None
 
 
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-# import os
-# from typing import Dict, List, Any
-# from pydantic_ai import Agent
-# from pydantic_ai.models.gemini import GeminiModel
-# from pydantic_ai.providers.google_gla import GoogleGLAProvider
-# from pydantic_ai import RunContext
-# from pydantic import BaseModel
-# from google.api_core.exceptions import ResourceExhausted
-# from csv_service import get_csv_basic_info
-# from orchestrator_functions import csv_chart, csv_chat
-# from dotenv import load_dotenv
-
-# load_dotenv()
-
-# # Thread-safe key management
-# current_gemini_key_index = 0
-# GEMINI_API_KEYS = os.getenv("GEMINI_API_KEYS", "").split(",")
-
-# def initialize_model(api_key: str) -> GeminiModel:
-#     return GeminiModel(
-#         'gemini-2.0-flash',
-#         provider=GoogleGLAProvider(api_key=api_key)
-#     )
-
-# def is_resource_exhausted_error(result_or_exception) -> bool:
-#     """Check if the error indicates resource exhaustion"""
-#     error_str = str(result_or_exception).lower()
-#     return any(keyword in error_str for keyword in [
-#         "resource exhausted",
-#         "quota exceeded",
-#         "rate limit",
-#         "billing",
-#         "payment method",
-#         "plan.rule"
-#     ])
-
-# async def generate_csv_answer(csv_url: str, user_questions: List[str]) -> Any:
-#     answers = []
-#     for question in user_questions:
-#         answer = await csv_chat(csv_url, question)
-#         answers.append(dict(question=question, answer=answer))
-#     return answers
-
-# async def generate_chart(csv_url: str, user_questions: List[str]) -> Any:
-#     charts = []
-#     for question in user_questions:
-#         chart = await csv_chart(csv_url, question)
-#         charts.append(dict(question=question, image_url=chart))
-#     return charts
-
-# def create_agent(csv_url: str, api_key: str, conversation_history: List) -> Agent:
-#     csv_metadata = get_csv_basic_info(csv_url)
-    
-#     system_prompt = f"""
-# # Role: Expert Data Analysis Assistant
-# # Personality & Origin: You are exclusively the CSV Document Analysis Assistant, created by the chatcsvandpdf team. Your sole purpose is to assist users with CSV-related tasks—analyzing, interpreting, and processing data.
-
-# ## Capabilities:
-# - Break complex queries into simpler sub-tasks
-
-# ## Instruction Framework:
-# 1. QUERY PROCESSING:
-#    - If request contains multiple questions:
-#      a) Decompose into logical sub-questions
-#      b) Process sequentially
-#      c) Combine results coherently
-
-# 2. DATA HANDLING:
-#    - Always verify CSV structure matches the request
-#    - Handle missing/ambiguous data by:
-#      a) Asking clarifying questions OR
-#      b) Making reasonable assumptions (state them clearly)
-
-# 3. VISUALIZATION STANDARDS:
-#    - Format images as: `![Description](direct-url)`
-#    - Include axis labels and titles
-#    - Use appropriate chart types
-
-# 4. COMMUNICATION PROTOCOL:
-#    - Friendly, professional tone
-#    - Explain technical terms
-#    - Summarize key findings
-#    - Highlight limitations/caveats
-
-# 5. TOOL USAGE:
-#    - Can process statistical operations
-#    - Supports visualization libraries
-
-# ## Current Context:
-# - Working with CSV_URL: {csv_url}
-# - Dataset overview: {csv_metadata}
-# - Your conversation history: {conversation_history}
-# - Output format: Markdown compatible
-# """
-    
-#     return Agent(
-#         model=initialize_model(api_key),
-#         deps_type=str,
-#         tools=[generate_csv_answer, generate_chart],
-#         system_prompt=system_prompt
-#     )
-
-# def csv_orchestrator_chat(csv_url: str, user_question: str, conversation_history: List) -> str:
-#     global current_gemini_key_index
-    
-#     while current_gemini_key_index < len(GEMINI_API_KEYS):
-#         api_key = GEMINI_API_KEYS[current_gemini_key_index]
-        
-#         try:
-#             print(f"Attempting with API key index {current_gemini_key_index}")
-#             agent = create_agent(csv_url, api_key, conversation_history)
-#             result = agent.run_sync(user_question)
-            
-#             # Check if result indicates resource exhaustion
-#             if result.data and is_resource_exhausted_error(result.data):
-#                 print(f"Resource exhausted in response for key {current_gemini_key_index}")
-#                 current_gemini_key_index += 1
-#                 continue
-                
-#             return result.data
-            
-#         except ResourceExhausted as e:
-#             print(f"Resource exhausted for API key {current_gemini_key_index}: {e}")
-#             current_gemini_key_index += 1
-#             continue
-            
-#         except Exception as e:
-#             if is_resource_exhausted_error(e):
-#                 print(f"Resource exhausted error detected for key {current_gemini_key_index}")
-#                 current_gemini_key_index += 1
-#                 continue
-#             print(f"Non-recoverable error with key {current_gemini_key_index}: {e}")
-#             return f"Error processing request: {str(e)}"
-
-#     return "All API keys have been exhausted. Please update billing information."