from datetime import datetime
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  # Import the exception for quota exhaustion
from code_exec_service import run_analysis
from csv_service import get_csv_basic_info
from orchestrator_functions import csv_chart, csv_chat
from dotenv import load_dotenv


load_dotenv()


# Load all API keys from the environment variable
GEMINI_API_KEYS = os.getenv("GEMINI_API_KEYS", "").split(",")  # Expecting a comma-separated list of keys

# Function to initialize the model with a specific API key
def initialize_model(api_key: str) -> GeminiModel:
    return GeminiModel(
        'gemini-2.0-flash',
        provider=GoogleGLAProvider(api_key=api_key)
    )

# Define the tools
async def generate_csv_answer(csv_url: str, user_questions: List[str]) -> Any:
    """
    This function generates answers for the given user questions using the CSV URL.
    It uses the csv_chat function to process each question and return the answers.
    
    Args:
        csv_url (str): The URL of the CSV file.
        user_questions (List[str]): A list of user questions.
        
    Returns:
        List[Dict[str, Any]]: A list of dictionaries containing the question and answer for each question.
        
    Example:
        [
            {"question": "What is the average age of the customers?", "answer": "The average age is 35."},
            {"question": "What is the most common gender?", "answer": "The most common gender is Male."}
        ]
    """
    
    print("LLM using the csv chat function....")
    print("CSV URL:", csv_url)
    print("User question:", user_questions)

    # Create an array to accumulate the answers
    answers = []
    # Loop through the user questions and generate answers for each
    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:
    
    """
    This function generates charts for the given user questions using the CSV URL.
    It uses the csv_chart function to process each question and return the chart URLs.
    It returns a list of dictionaries containing the question and chart URL for each question.
    Args:
        csv_url (str): The URL of the CSV file.
        user_questions (List[str]): A list of user questions.

    Returns:
        List[Dict[str, Any]]: A list of dictionaries containing the question and chart URL for each question.

    Example:
        [
            {"question": "What is the average age of the customers?", "chart_url": "https://example.com/chart1.png"},
            {"question": "What is the most common gender?", "chart_url": "https://example.com/chart2.png"}
        ]
    """
    
    print("LLM using the csv chart function....")
    print("CSV URL:", csv_url)
    print("User question:", user_questions)

    # Create an array to accumulate the charts
    charts = []
    # Loop through the user questions and generate charts for each
    for question in user_questions:
        chart = await csv_chart(csv_url, question)
        charts.append(dict(question=question, image_url=chart))
    
    return charts

# Function to create an agent with a specific CSV URL
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:
   - Python Code Executor Tool (To execute Python code, To get date-time, For lightweight data analysis etc.)
   - Data Analysis Tool
   - Chart Generation Tool

## Current Context:
- Working with CSV_URL: {csv_url}
- Dataset overview: {csv_metadata}
- Your conversation history: {conversation_history}
- Output format: Markdown compatible

## Response Template:
1. Confirm understanding of request
2. Outline analysis approach
3. Present results with visualizations (if applicable)
4. Provide interpretation
5. Offer next-step suggestions
"""
    gemini_csv_orchestrator_agent = Agent(
        model=initialize_model(api_key),
        deps_type=str,
        tools=[generate_csv_answer, generate_chart],
        system_prompt=system_prompt
    )
    
    @gemini_csv_orchestrator_agent.tool_plain
    def python_code_executor(analysis_code: str) -> dict:
                """_summary_

                Args:
                    analysis_code (str): _description_
                    Ex: 
                       df = pd.read_csv({csv_url})
                       len(df)

                Returns:
                   dict: _description_
                """
                
                print(f'LLM Passed a code: {analysis_code}')
                result = run_analysis(analysis_code)
                if result['success']:
                    print("Execution successful")
                    print("Execution time:", result['execution_time'], "seconds")
                    print("Output:", result['output'].strip())
                    print("Result:", result['result'])
                    print("Variables:", list(result['variables'].keys()))
                    # convert the result to a string
                    result_str = str(result['output'])
                    return result_str
                else:
                    print("Execution failed")
                    print("Error:", result['error'])
                    error_str = str(result['error'])
                    return error_str
    return gemini_csv_orchestrator_agent

def csv_orchestrator_chat(csv_url: str, user_question: str, conversation_history: List) -> str:
    print("CSV URL:", csv_url)
    print("User questions:", user_question)

    # Iterate through all API keys
    for api_key in GEMINI_API_KEYS:
        try:
            print(f"Attempting with API key: {api_key}")
            agent = create_agent(csv_url, api_key, conversation_history)
            result = agent.run_sync(user_question)
            print("Orchestrator Result:", result.data)
            return result.data
        except ResourceExhausted or Exception as e:
            print(f"Quota exhausted for API key: {api_key}. Switching to the next key.")
            continue  # Move to the next key
        except Exception as e:
            print(f"Error with API key {api_key}: {e}")
            continue  # Move to the next key

    # If all keys are exhausted or fail
    print("All API keys have been exhausted or failed.")
    return None