# import os
# import sys
# import asyncio
# sys.path.append(os.getcwd())

# from dotenv import load_dotenv
# load_dotenv()
# from logger import *
# import logging

# from src.MultiRag.pipeline.run_pipeline import RunPipeline


# from src.MultiRag.models.rag_model import Content
# from src.MultiRag.components.content_embedder import ContentEmbedder
# from src.MultiRag.entity.config_entity import ContentEmbedderConfig
# import os

# # ============= generating retreivers ===========================

# async def generate_retreivers(thread_id):
#     for file in os.listdir("docs"):
#         logging.info(f"Processing file: {file}")
    
#         content_embedder_config = ContentEmbedderConfig(
#             file_path=f"docs/{file}",
#             vector_store_path=f"db/{thread_id}/{file}",  # Updated path structure
#         )
#         component = ContentEmbedder(content_embedder_config=content_embedder_config)
#         retreiver = await component.embed_content()
#         logging.info(f"Generated retreiver for {file}: {retreiver}")


# # ============= testing pdf query loading =======================
# async def pdf_test():

#     run_pipeline = RunPipeline()
    
#     # Mocking user uploaded files
#     temp_user_content = [
#         Content(
#             name="AI_Intro.pdf",
#             about="An introductory document about Artificial Intelligence and Machine Learning.",
#             path="docs/AI_Intro.pdf"
#         )
#     ]

#     res = await run_pipeline.initiate(
#         thread_id="1", 
#         query="What does the AI_Intro.pdf say about Neural Networks? Use the pdf",
#         userContent=temp_user_content
#     )

#     logging.info(f"Final Pipeline Response: {res}")

# # ============= testing txt query loading =======================
# async def txt_test():
#     run_pipeline = RunPipeline()
    
#     # Mocking user uploaded files
#     temp_user_content = [
#         Content(
#             name="growing_ai_tools.txt",
#             about="General notes about growing AI tools.",
#             path="docs/growing_ai_tools.txt"
#         )
#     ]

#     res = await run_pipeline.initiate(
#         thread_id="1", 
#         query="What does the growing_ai_tools.txt say about AI tools? use the txt file",
#         userContent=temp_user_content
#     )

#     logging.info(f"Final Pipeline Response: {res}")


# # ============= testing docs query loading =======================
# async def docx_test():
#     run_pipeline = RunPipeline()
    
#     # Mocking user uploaded files
#     temp_user_content = [
#         Content(
#             name="google.docx",
#             about="General notes about company Google.",
#             path="docs/google.docx"
#         )
#     ]

#     res = await run_pipeline.initiate(
#         thread_id="1", 
#         query="What does the google.docx say about Google? use the docx file",
#         userContent=temp_user_content
#     )

#     logging.info(f"Final Pipeline Response: {res}")


# # ============= testing image query loading =======================
# async def image_test():
#     run_pipeline = RunPipeline()
    
#     # Mocking user uploaded files
#     temp_user_content = [
#         Content(
#             name="lena.png",
#             about="An image of a girl.",
#             path="docs/lena.png"
#         )
#     ]

#     res = await run_pipeline.initiate(
#         thread_id="1", 
#         query="What does the lena.png say about the girl? use the image file",
#         userContent=temp_user_content
#     )

#     logging.info(f"Final Pipeline Response: {res}")




# # ============== Running all the tests =============================
# async def main():
#     logging.info("Starting generating retreivers...")
#     await generate_retreivers(thread_id="1")
#     logging.info("Retreivers generated successfully. Starting pipeline tests...")
#     logging.info("Starting pipeline tests...")
#     await pdf_test()
#     await txt_test()
#     await docx_test()
#     await image_test()
#     logging.info("Pipeline tests completed.")


# asyncio.run(main())