controller.py

# Import necessary modules
from concurrent.futures import ProcessPoolExecutor
import logging
import os
import asyncio
import threading
import uuid
from fastapi import FastAPI, HTTPException, Header
from fastapi.encoders import jsonable_encoder
from typing import Dict, List
from fastapi.responses import FileResponse
import numpy as np
import pandas as pd
from pandasai import SmartDataframe
from langchain_groq.chat_models import ChatGroq
from dotenv import load_dotenv
from pydantic import BaseModel
from csv_service import clean_data, extract_chart_filenames, generate_csv_data, get_csv_basic_info
from urllib.parse import unquote
from langchain_groq import ChatGroq
import pandas as pd
from langchain_experimental.tools import PythonAstREPLTool
from langchain_experimental.agents import create_pandas_dataframe_agent
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
from gemini_report_generator import generate_csv_report
from intitial_q_handler import if_initial_chart_question, if_initial_chat_question
from orchestrator_agent import csv_orchestrator_chat
from supabase_service import upload_file_to_supabase
from util_service import _prompt_generator, process_answer
from fastapi.middleware.cors import CORSMiddleware
import matplotlib
matplotlib.use('Agg')

# Initialize FastAPI app
app = FastAPI()

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Initialize the ProcessPoolExecutor
max_cpus = os.cpu_count()
logger.info(f"Max CPUs: {max_cpus}")

# Ensure the cache directory exists
os.makedirs("/app/cache", exist_ok=True)

os.makedirs("/app", exist_ok=True)
open("/app/pandasai.log", "a").close()  # Create the file if it doesn't exist

# Ensure the generated_charts directory exists
os.makedirs("/app/generated_charts", exist_ok=True)

load_dotenv()

image_file_path = os.getenv("IMAGE_FILE_PATH")
image_not_found = os.getenv("IMAGE_NOT_FOUND")
allowed_hosts = os.getenv("ALLOWED_HOSTS", "").split(",")
app.add_middleware(
    CORSMiddleware,
    allow_origins=allowed_hosts, 
    allow_credentials=True,
    allow_methods=["*"], 
    allow_headers=["*"],  
)

# Load environment variables
groq_api_keys = os.getenv("GROQ_API_KEYS").split(",")
model_name = os.getenv("GROQ_LLM_MODEL")

class CsvUrlRequest(BaseModel):
    csv_url: str
    
class ImageRequest(BaseModel):
    image_path: str
    chat_id: str
    
class FileProps(BaseModel):
    fileName: str
    filePath: str
    fileType: str  # 'csv' | 'image'

class Files(BaseModel):
    csv_files: List[FileProps]
    image_files: List[FileProps]

class FileBoxProps(BaseModel):
    files: Files
    
dummy_response = FileBoxProps(
            files=Files(
                csv_files=[
                    FileProps(
                        fileName="sales_data.csv",
                        filePath="/downloads/sales_data.csv",
                        fileType="csv"
                    ),
                    FileProps(
                        fileName="customer_data.csv",
                        filePath="/downloads/customer_data.csv",
                        fileType="csv"
                    )
                ],
                image_files=[
                    FileProps(
                        fileName="chart.png",
                        filePath="/downloads/chart.png",
                        fileType="image"
                    ),
                    FileProps(
                        fileName="graph.jpg",
                        filePath="/downloads/graph.jpg",
                        fileType="image"
                    )
                ]
            )
        )


# Thread-safe key management for groq_chat
current_groq_key_index = 0
current_groq_key_lock = threading.Lock()

# Thread-safe key management for langchain_csv_chat
current_langchain_key_index = 0
current_langchain_key_lock = threading.Lock()


# PING CHECK
@app.get("/ping")
async def root():
    return {"message": "Pong !!"}



# BASIC KNOWLEDGE BASED ON CSV

# Remove trailing slash from the URL otherwise it will redirect to GET method
@app.post("/api/basic_csv_data")
async def basic_csv_data(request: CsvUrlRequest):
    try:
        decoded_url = unquote(request.csv_url)
        logger.info(f"Fetching CSV data from URL: {decoded_url}")
        # csv_data = await get_csv_basic_info(decoded_url)    
        # Run the synchronous function in a thread pool executor
        loop = asyncio.get_running_loop()
        csv_data = await loop.run_in_executor(
            process_executor, get_csv_basic_info, decoded_url
        )
        logger.info(f"CSV data fetched successfully: {csv_data}")
        return {"data": csv_data}
    except Exception as e:
        logger.error(f"Error while fetching CSV data: {e}")
        raise HTTPException(status_code=400, detail=f"Failed to retrieve CSV data: {str(e)}")


# GET THE CHART FROM A SPECIFIC FILE PATH
@app.post("/api/get-chart")
async def get_image(request: ImageRequest, authorization: str = Header(None)):
    if not authorization:
        raise HTTPException(status_code=401, detail="Authorization header missing")

    if not authorization.startswith("Bearer "):
        raise HTTPException(status_code=401, detail="Invalid authorization header format")

    token = authorization.split(" ")[1]
    if not token:
        raise HTTPException(status_code=401, detail="Token missing")
    if token != os.getenv("AUTH_TOKEN"):
        raise HTTPException(status_code=403, detail="Invalid token")

    try:
        logger.info("Groq Chat created a chat for the user query...")
        image_file_path = request.image_path
        unique_file_name =f'{str(uuid.uuid4())}.png'
        logger.info("Uploading the chart to supabase...")
        image_public_url = await upload_file_to_supabase(f"{image_file_path}", unique_file_name, chat_id=request.chat_id)
        logger.info("Image uploaded to Supabase and Image URL is... ", {image_public_url})
        os.remove(image_file_path)
        return {"image_url": image_public_url}
        # return FileResponse(image_file_path, media_type="image/png")
    except Exception as e:
        logger.error(f"Error: {e}")
        return {"answer": "error"}
        

# GET CSV DATA FOR GENERATING THE TABLE
@app.post("/api/csv_data")
async def get_csv_data(request: CsvUrlRequest):
    try:
        decoded_url = unquote(request.csv_url)
        logger.info(f"Fetching CSV data from URL: {decoded_url}")
        # csv_data = await generate_csv_data(decoded_url)     
        loop = asyncio.get_running_loop()
        csv_data = await loop.run_in_executor(
            process_executor, generate_csv_data, decoded_url
        )   
        return csv_data
    except Exception as e:
        logger.error(f"Error while fetching CSV data: {e}")
        raise HTTPException(status_code=400, detail=f"Failed to retrieve CSV data: {str(e)}")

 



# CHAT CODING STARTS FROM HERE

# Modified groq_chat function with thread-safe key rotation
def groq_chat(csv_url: str, question: str):
    global current_groq_key_index, current_groq_key_lock

    while True:
        with current_groq_key_lock:
            if current_groq_key_index >= len(groq_api_keys):
                return {"error": "All API keys exhausted."}
            current_api_key = groq_api_keys[current_groq_key_index]

        try:
           
            data = clean_data(csv_url)
            llm = ChatGroq(model=model_name, api_key=current_api_key)
            # Generate unique filename using UUID
            chart_filename = f"chart_{uuid.uuid4()}.png"
            chart_path = os.path.join("generated_charts", chart_filename)
            
            # Configure SmartDataframe with chart settings
            df = SmartDataframe(
                data,
                config={
                    'llm': llm,
                    'save_charts': True,  # Enable chart saving
                    'open_charts': False,
                    'save_charts_path': os.path.dirname(chart_path),  # Directory to save
                    'custom_chart_filename': chart_filename, # Unique filename
                    'enable_cache': False
                }
            )
            
            answer = df.chat(question)

            # Process different response types
            if isinstance(answer, pd.DataFrame):
                processed = answer.apply(handle_out_of_range_float).to_dict(orient="records")
            elif isinstance(answer, pd.Series):
                processed = answer.apply(handle_out_of_range_float).to_dict()
            elif isinstance(answer, list):
                processed = [handle_out_of_range_float(item) for item in answer]
            elif isinstance(answer, dict):
                processed = {k: handle_out_of_range_float(v) for k, v in answer.items()}
            else:
                processed = {"answer": str(handle_out_of_range_float(answer))}

            return processed

        except Exception as e:
            error_message = str(e)
            if error_message != "":
                logger.warning("Rate limit exceeded. Switching to next API key.")
                with current_groq_key_lock:
                    current_groq_key_index += 1
                    if current_groq_key_index >= len(groq_api_keys):
                        return {"error": "All API keys exhausted."}
            else:
                logger.error("Error in groq_chat: %s", e)
                return {"error": error_message}

# Modified langchain_csv_chat with thread-safe key rotation
def langchain_csv_chat(csv_url: str, question: str, chart_required: bool):
    global current_langchain_key_index, current_langchain_key_lock

    data = clean_data(csv_url)
    attempts = 0

    while attempts < len(groq_api_keys):
        with current_langchain_key_lock:
            if current_langchain_key_index >= len(groq_api_keys):
                current_langchain_key_index = 0
            api_key = groq_api_keys[current_langchain_key_index]
            current_key = current_langchain_key_index
            current_langchain_key_index += 1
            attempts += 1

        try:
            llm = ChatGroq(model=model_name, api_key=api_key)
            tool = PythonAstREPLTool(locals={
                "df": data,
                "pd": pd,
                "np": np,
                "plt": plt,
                "sns": sns,
                "matplotlib": matplotlib
            })

            agent = create_pandas_dataframe_agent(
                llm,
                data,
                agent_type="tool-calling",
                verbose=True,
                allow_dangerous_code=True,
                extra_tools=[tool],
                return_intermediate_steps=True
            )

            prompt = _prompt_generator(question, chart_required)
            result = agent.invoke({"input": prompt})
            return result.get("output")

        except Exception as e:
            error_message = str(e)
            # if "429" in error_message:
            if error_message != "":
                with current_langchain_chart_lock:
                    current_langchain_chart_key_index = (current_langchain_chart_key_index + 1)
                    logger.warning(f"Rate limit exceeded. Switching to next API key: {groq_api_keys[current_langchain_chart_key_index]}")
            else:
                logger.error(f"Error with API key {api_key}: {error_message}")
                return {"error": error_message}

    return {"error": "All API keys exhausted"}

# Async endpoint with non-blocking execution
@app.post("/api/csv-chat")
async def csv_chat(request: Dict, authorization: str = Header(None)):
    # Authorization checks
    if not authorization or not authorization.startswith("Bearer "):
        raise HTTPException(status_code=401, detail="Invalid authorization")
    
    token = authorization.split(" ")[1]
    if token != os.getenv("AUTH_TOKEN"):
        raise HTTPException(status_code=403, detail="Invalid token")

    try:
        query = request.get("query")
        csv_url = request.get("csv_url")
        decoded_url = unquote(csv_url)
        detailed_answer = request.get("detailed_answer")
        conversation_history = request.get("conversation_history", [])
        generate_report = request.get("generate_report")
        chat_id = request.get("chat_id")
        
        if generate_report is True:
            report_files = await generate_csv_report(csv_url, query, chat_id)
            if report_files is not None:
                return {"answer": jsonable_encoder(report_files)}

        if if_initial_chat_question(query):
            answer = await asyncio.to_thread(
                langchain_csv_chat, decoded_url, query, False
            )
            logger.info("langchain_answer:", answer)
            return {"answer": jsonable_encoder(answer)}
        
        # Orchestrate the execution
        if detailed_answer is True:
           orchestrator_answer = await asyncio.to_thread(
            csv_orchestrator_chat, decoded_url, query, conversation_history, chat_id
           )
           if orchestrator_answer is not None:
              return {"answer": jsonable_encoder(orchestrator_answer)}

        # Process with groq_chat first
        groq_answer = await asyncio.to_thread(groq_chat, decoded_url, query)
        logger.info("groq_answer:", groq_answer)
        
        if process_answer(groq_answer) == "Empty response received.":
            return {"answer": "Sorry, I couldn't find relevant data..."}

        if process_answer(groq_answer):
            lang_answer = await asyncio.to_thread(
                langchain_csv_chat, decoded_url, query, False
            )
            if process_answer(lang_answer):
                return {"answer": "error"}
            return {"answer": jsonable_encoder(lang_answer)}

        return {"answer": jsonable_encoder(groq_answer)}

    except Exception as e:
        logger.error(f"Error processing request: {str(e)}")
        return {"answer": "error"}

def handle_out_of_range_float(value):
    if isinstance(value, float):
        if np.isnan(value):
            return None
        elif np.isinf(value):
            return "Infinity"
    return value







# CHART CODING STARTS FROM HERE

instructions = """

- Please ensure that each value is clearly visible, You may need to adjust the font size, rotate the labels, or use truncation to improve readability (if needed).
- For multiple charts, arrange them in a grid format (2x2, 3x3, etc.)
- Use colorblind-friendly palette
- Read above instructions and follow them.

"""

# Thread-safe configuration for chart endpoints
current_groq_chart_key_index = 0
current_groq_chart_lock = threading.Lock()

# current_langchain_chart_key_index = 0
# current_langchain_chart_lock = threading.Lock()

def model():
    global current_groq_chart_key_index, current_groq_chart_lock
    with current_groq_chart_lock:
        if current_groq_chart_key_index >= len(groq_api_keys):
            raise Exception("All API keys exhausted for chart generation")
        api_key = groq_api_keys[current_groq_chart_key_index]
    return ChatGroq(model=model_name, api_key=api_key)

def groq_chart(csv_url: str, question: str):
    global current_groq_chart_key_index, current_groq_chart_lock
    
    for attempt in range(len(groq_api_keys)):
        try:
            # Clean cache before processing
            # cache_db_path = "/workspace/cache/cache_db_0.11.db"
            # if os.path.exists(cache_db_path):
            #     try:
            #         os.remove(cache_db_path)
            #     except Exception as e:
            #         print(f"Cache cleanup error: {e}")

            data = clean_data(csv_url)
            with current_groq_chart_lock:
                current_api_key = groq_api_keys[current_groq_chart_key_index]
            
            llm = ChatGroq(model=model_name, api_key=current_api_key)
            
            # Generate unique filename using UUID
            chart_filename = f"chart_{uuid.uuid4()}.png"
            chart_path = os.path.join("generated_charts", chart_filename)
            
            # Configure SmartDataframe with chart settings
            df = SmartDataframe(
                data,
                config={
                    'llm': llm,
                    'save_charts': True,  # Enable chart saving
                    'open_charts': False,
                    'save_charts_path': os.path.dirname(chart_path),  # Directory to save
                    'custom_chart_filename': chart_filename,  # Unique filename
                    'enable_cache': False
                }
            )
            
            answer = df.chat(question + instructions)
            
            if process_answer(answer):
                return "Chart not generated"
            return answer

        except Exception as e:
            error = str(e)
            # if "429" in error:
            if error != "":
                with current_groq_chart_lock:
                    current_groq_chart_key_index = (current_groq_chart_key_index + 1)
            else:
                logger.error(f"Chart generation error: {error}")
                return {"error": error}
    
    return {"error": "All API keys exhausted for chart generation"}



# def langchain_csv_chart(csv_url: str, question: str, chart_required: bool):
#     global current_langchain_chart_key_index, current_langchain_chart_lock
    
#     data = clean_data(csv_url)

#     for attempt in range(len(groq_api_keys)):
#         try:
#             with current_langchain_chart_lock:
#                 api_key = groq_api_keys[current_langchain_chart_key_index]
#                 current_key = current_langchain_chart_key_index
#                 current_langchain_chart_key_index = (current_langchain_chart_key_index + 1) % len(groq_api_keys)

#             llm = ChatGroq(model=model_name, api_key=api_key)
#             tool = PythonAstREPLTool(locals={
#                 "df": data,
#                 "pd": pd,
#                 "np": np,
#                 "plt": plt,
#                 "sns": sns,
#                 "matplotlib": matplotlib,
#                 "uuid": uuid
#             })

#             agent = create_pandas_dataframe_agent(
#                 llm,
#                 data,
#                 agent_type="openai-tools",
#                 verbose=True,
#                 allow_dangerous_code=True,
#                 extra_tools=[tool],
#                 return_intermediate_steps=True
#             )

#             result = agent.invoke({"input": _prompt_generator(question, True)})
#             output = result.get("output", "")

#             # Verify chart file creation
#             chart_files = extract_chart_filenames(output)
#             if len(chart_files) > 0:
#                 return chart_files

#             if attempt < len(groq_api_keys) - 1:
#                 print(f"Langchain chart error (key {current_key}): {output}")

#         except Exception as e:
#             print(f"Langchain chart error (key {current_key}): {str(e)}")
    
#     return "Chart generation failed after all retries"

# @app.post("/api/csv-chart")
# async def csv_chart(request: dict, authorization: str = Header(None)):
#     # Authorization verification
#     if not authorization or not authorization.startswith("Bearer "):
#         raise HTTPException(status_code=401, detail="Authorization required")
    
#     token = authorization.split(" ")[1]
#     if token != os.getenv("AUTH_TOKEN"):
#         raise HTTPException(status_code=403, detail="Invalid credentials")

#     try:
#         query = request.get("query", "")
#         csv_url = unquote(request.get("csv_url", ""))
        
#         # Parallel processing with thread pool
#         if if_initial_chart_question(query):
#             chart_paths = await asyncio.to_thread(
#                 langchain_csv_chart, csv_url, query, True
#             )
#             print(chart_paths)
        
#             if len(chart_paths) > 0:
#                return FileResponse(f"{image_file_path}/{chart_paths[0]}", media_type="image/png")
        
#         # Groq-based chart generation
#         groq_result = await asyncio.to_thread(groq_chart, csv_url, query)
#         print(f"Generated Chart: {groq_result}")
#         if groq_result != 'Chart not generated':
#             return FileResponse(groq_result, media_type="image/png")
          
        
#         # Fallback to Langchain
#         langchain_paths = await asyncio.to_thread(
#             langchain_csv_chart, csv_url, query, True
#         )
        
#         print (langchain_paths)
       
#         if len(langchain_paths) > 0:
#            return FileResponse(f"{image_file_path}/{langchain_paths[0]}", media_type="image/png")
#         else:
#            return {"error": "All chart generation methods failed"}

#     except Exception as e:
#         print(f"Critical chart error: {str(e)}")
#         return {"error": "Internal system error"}
    
    
    



# MERGED CALL

# class CSVData(BaseModel):
#     csv_url: str
#     query: str
#     chart_required: bool

# @app.post("/api/v1/csv_chat")
# async def csv_chat(csv_data: CSVData, authorization: str = Header(None)):
#     # Authorization verification
#     if not authorization or not authorization.startswith("Bearer "):
#         raise HTTPException(status_code=401, detail="Authorization required")

#     token = authorization.split(" ")[1]
#     if token != os.getenv("AUTH_TOKEN"):
#         raise HTTPException(status_code=403, detail="Invalid credentials")

#     csv_url = csv_data.csv_url
#     query = csv_data.query
#     chart_required = csv_data.chart_required
    
#     if(chart_required == True):
#         try:
#             # Parallel processing with thread pool
#             if if_initial_chart_question(query):
#                 chart_path = await asyncio.to_thread(
#                     langchain_csv_chart, csv_url, query, True
#                 )
#                 if "temp" in chart_path:
#                    print("langchain chart Generated")
#                    return FileResponse('temp.png', media_type="image/png")
#                 return {"error": "Chart generation failed"}
            
#             # Groq-based chart generation
#             groq_result = await asyncio.to_thread(groq_chart, csv_url, query)
#             if groq_result == "Chart Generated":
#                 return FileResponse("exports/charts/temp_chart.png")
#             # Fallback to Langchain
#             langchain_path = await asyncio.to_thread(
#                 langchain_csv_chart, csv_url, query, True
#             )
#             if "temp" in langchain_path:
#                 print("langchain chart Generated")
#                 return FileResponse('temp.png', media_type="image/png")
#             return {"error": "All chart generation methods failed"}

#         except Exception as e:
#             print(f"Critical chart error: {str(e)}")
#             raise HTTPException(status_code=500, detail="Internal server error")
#     else:
#         try:            
#             if if_initial_chat_question(query):
#                 answer = await asyncio.to_thread(
#                  langchain_csv_chat, csv_url, query, False
#                 )
#                 print("langchain_answer:", answer)
#                 return {"answer": jsonable_encoder(answer)}

#             # Process with groq_chat first
#             groq_answer = await asyncio.to_thread(groq_chat, csv_url, query)
#             print("groq_answer:", groq_answer)
        
#             if process_answer(groq_answer) == "Empty response received.":
#               return {"answer": "Sorry, I couldn't find relevant data..."}

#             if process_answer(groq_answer):
#               lang_answer = await asyncio.to_thread(
#                 langchain_csv_chat, csv_url, query, False
#               )
#               if process_answer(lang_answer):
#                 return {"answer": "error"}
#               return {"answer": jsonable_encoder(lang_answer)}

#             return {"answer": jsonable_encoder(groq_answer)}

#         except Exception as e:
#             print(f"Error processing request: {str(e)}")
#             raise HTTPException(status_code=500, detail="Internal server error")
    
        



# Global locks for key rotation (chart endpoints)
# current_groq_chart_key_index = 0
# current_groq_chart_lock = threading.Lock()
current_langchain_chart_key_index = 0
current_langchain_chart_lock = threading.Lock()


# Use a process pool to run CPU-bound charts generation
process_executor = ProcessPoolExecutor(max_workers=max_cpus-2)

# --- GROQ-BASED CHART GENERATION ---
# def groq_chart(csv_url: str, question: str):
#     """
#     Generate a chart using the groq-based method.
#     Modifications:
#       • No deletion of a shared cache file (avoid interference).
#       • After chart generation, close all matplotlib figures.
#       • Return the full path of the saved chart.
#     """
#     global current_groq_chart_key_index, current_groq_chart_lock
    
#     for attempt in range(len(groq_api_keys)):
#         try:
#             # Instead of deleting a global cache file, you might later configure a per-request cache.
#             cache_db_path = "/app/cache/cache_db_0.11.db"
#             if os.path.exists(cache_db_path):
#                 try:
#                     os.remove(cache_db_path)
#                     print(f"Deleted cache DB file: {cache_db_path}")
#                 except Exception as e:
#                     print(f"Error deleting cache DB file: {e}")
                    
#             chart_dir = "generated_charts"
#             if not os.path.exists(chart_dir):
#                os.makedirs(chart_dir)
                    
#             data = clean_data(csv_url)
#             with current_groq_chart_lock:
#                 current_api_key = groq_api_keys[current_groq_chart_key_index]
            
#             llm = ChatGroq(model=model_name, api_key=current_api_key)
            
#             # Generate a unique filename and full path for the chart
#             chart_filename = f"chart_{uuid.uuid4().hex}.png"
#             chart_path = os.path.join("generated_charts", chart_filename)
            
#             # Configure your dataframe tool (e.g. using SmartDataframe) to save charts.
#             # (Assuming your SmartDataframe uses these settings to save charts.)
#             from pandasai import SmartDataframe  # Import here if not already imported
#             df = SmartDataframe(
#                 data,
#                 config={
#                     'llm': llm,
#                     'save_charts': True,
#                     'open_charts': False,
#                     'save_charts_path': os.path.dirname(chart_path),
#                     'custom_chart_filename': chart_filename
#                 }
#             )
            
#             # Append any extra instructions if needed
#             instructions = """
#             - Ensure each value is clearly visible.
#             - Adjust font sizes, rotate labels if necessary.
#             - Use a colorblind-friendly palette.
#             - Arrange multiple charts in a grid if needed.
#             """
#             answer = df.chat(question + instructions)
            
#             # Make sure to close figures so they don't conflict between processes
#             plt.close('all')
            
#             # If process_answer indicates a problem, return a failure message.
#             if process_answer(answer):
#                 return "Chart not generated"
#             # Return the chart path that was used for saving
#             return chart_path

#         except Exception as e:
#             error = str(e)
#             if "429" in error:
#                 with current_groq_chart_lock:
#                     current_groq_chart_key_index = (current_groq_chart_key_index + 1) % len(groq_api_keys)
#             else:
#                 print(f"Groq chart generation error: {error}")
#                 return {"error": error}
    
#     return {"error": "All API keys exhausted for chart generation"}


# --- LANGCHAIN-BASED CHART GENERATION ---
def langchain_csv_chart(csv_url: str, question: str, chart_required: bool):
    """
    Generate a chart using the langchain-based method.
    Modifications:
      • No shared deletion of cache.
      • Close matplotlib figures after generation.
      • Return a list of full chart file paths.
    """
    global current_langchain_chart_key_index, current_langchain_chart_lock
    
    data = clean_data(csv_url)

    for attempt in range(len(groq_api_keys)):
        try:
            with current_langchain_chart_lock:
                api_key = groq_api_keys[current_langchain_chart_key_index]
                current_key = current_langchain_chart_key_index
                current_langchain_chart_key_index = (current_langchain_chart_key_index + 1) % len(groq_api_keys)

            llm = ChatGroq(model=model_name, api_key=api_key)
            tool = PythonAstREPLTool(locals={
                "df": data,
                "pd": pd,
                "np": np,
                "plt": plt,
                "sns": sns,
                "matplotlib": matplotlib,
                "uuid": uuid
            })

            agent = create_pandas_dataframe_agent(
                llm,
                data,
                agent_type="tool-calling",
                verbose=True,
                allow_dangerous_code=True,
                extra_tools=[tool],
                return_intermediate_steps=True
            )

            result = agent.invoke({"input": _prompt_generator(question, True)})
            output = result.get("output", "")

            # Close figures to avoid interference
            plt.close('all')

            # Extract chart filenames (assuming extract_chart_filenames returns a list)
            chart_files = extract_chart_filenames(output)
            if len(chart_files) > 0:
                # Return full paths (join with your image_file_path)
                return [os.path.join(image_file_path, f) for f in chart_files]

            if attempt < len(groq_api_keys) - 1:
                logger.info(f"Langchain chart error (key {current_key}): {output}")

        except Exception as e:
            error_message = str(e)
            if error_message != "":
                with current_langchain_chart_lock:
                    current_langchain_chart_key_index = (current_langchain_chart_key_index + 1)
                    logger.warning(f"Rate limit exceeded. Switching to next API key: {groq_api_keys[current_langchain_chart_key_index]}")
            else:
                logger.error(f"Error with API key {api_key}: {error_message}")
                return {"error": error_message}
    
    logger.error("All API keys exhausted for chart generation")
    return "Chart generation failed after all retries"


# --- FASTAPI ENDPOINT FOR CHART GENERATION ---
@app.post("/api/csv-chart")
async def csv_chart(request: dict, authorization: str = Header(None)):
    """
    Endpoint for generating a chart from CSV data.
    This endpoint uses a ProcessPoolExecutor to run the (CPU-bound) chart generation
    functions in separate processes so that multiple requests can run in parallel.
    """
    # --- Authorization Check ---
    if not authorization or not authorization.startswith("Bearer "):
        raise HTTPException(status_code=401, detail="Authorization required")
    
    token = authorization.split(" ")[1]
    if token != os.getenv("AUTH_TOKEN"):
        raise HTTPException(status_code=403, detail="Invalid credentials")

    try:
        query = request.get("query", "")
        csv_url = unquote(request.get("csv_url", ""))
        detailed_answer = request.get("detailed_answer", False)
        conversation_history = request.get("conversation_history", [])
        generate_report = request.get("generate_report", False)
        chat_id = request.get("chat_id", "")
        
        if generate_report is True:
            report_files = await generate_csv_report(csv_url, query, chat_id)
            if report_files is not None:
                return {"orchestrator_response": jsonable_encoder(report_files)}

        loop = asyncio.get_running_loop()
        # First, try the langchain-based method if the question qualifies
        if if_initial_chart_question(query):
            langchain_result = await loop.run_in_executor(
                process_executor, langchain_csv_chart, csv_url, query, True
            )
            logger.info("Langchain chart result:", langchain_result)
            if isinstance(langchain_result, list) and len(langchain_result) > 0:
                unique_file_name =f'{str(uuid.uuid4())}.png'
                logger.info("Uploading the chart to supabase...")
                image_public_url = await upload_file_to_supabase(f"{langchain_result[0]}", unique_file_name, chat_id=chat_id)
                logger.info("Image uploaded to Supabase and Image URL is... ", {image_public_url})
                os.remove(langchain_result[0])
                return {"image_url": image_public_url}
               # return FileResponse(langchain_result[0], media_type="image/png")
               
        # Use orchestrator to handle the user's chart query first
        if detailed_answer is True:
           orchestrator_answer = await asyncio.to_thread(
             csv_orchestrator_chat, csv_url, query, conversation_history, chat_id
           )
        
           if orchestrator_answer is not None:
             return {"orchestrator_response": jsonable_encoder(orchestrator_answer)}
        
        # Next, try the groq-based method
        groq_result = await loop.run_in_executor(
            process_executor, groq_chart, csv_url, query
        )
        logger.info(f"Groq chart result: {groq_result}")
        if isinstance(groq_result, str) and groq_result != "Chart not generated":
            unique_file_name =f'{str(uuid.uuid4())}.png'
            logger.info("Uploading the chart to supabase...")
            image_public_url = await upload_file_to_supabase(f"{groq_result}", unique_file_name, chat_id=chat_id)
            logger.info("Image uploaded to Supabase and Image URL is... ", {image_public_url})
            os.remove(groq_result)
            return {"image_url": image_public_url}
            # return FileResponse(groq_result, media_type="image/png")
          
        # Fallback: try langchain-based again
        logger.error("Groq chart generation failed, trying langchain....")
        langchain_paths = await loop.run_in_executor(
            process_executor, langchain_csv_chart, csv_url, query, True
        )
        logger.info("Fallback langchain chart result:", langchain_paths)
        if isinstance(langchain_paths, list) and len(langchain_paths) > 0:
           unique_file_name =f'{str(uuid.uuid4())}.png'
           logger.info("Uploading the chart to supabase...")
           image_public_url = await upload_file_to_supabase(f"{langchain_paths[0]}", unique_file_name, chat_id=chat_id)
           logger.info("Image uploaded to Supabase and Image URL is... ", {image_public_url})
           os.remove(langchain_paths[0])
           return {"image_url": image_public_url}
           # return FileResponse(langchain_paths[0], media_type="image/png")
        else:
           logger.error("All chart generation methods failed")
           return {"answer": "error"}

    except Exception as e:
        logger.error(f"Critical chart error: {str(e)}")
        return {"answer": "error"}