Update app.py

app.py

@@ -1,115 +1,3 @@
-# import os
-# from typing import List
-# from dotenv import load_dotenv
-# import chainlit as cl
-# from langchain_community.embeddings import HuggingFaceEmbeddings
-# from langchain_text_splitters import RecursiveCharacterTextSplitter
-# from langchain_community.vectorstores import FAISS
-# from langchain_community.document_loaders import PyPDFLoader
-# from langchain.chains import RetrievalQA
-# from langchain_groq import ChatGroq
-# from langchain_huggingface import HuggingFaceEmbeddings
-
-# # Load environment variables
-# load_dotenv()
-
-# # Initialize embedding model
-# # embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-
-# openai.api_key = os.getenv("OPENAI_API_KEY")
-
-# # Initialize embedding model using OpenAI
-# embeddings = OpenAIEmbeddings(openai_api_key=openai.api_key,model="text-embedding-3-small")
-
-
-# # Initialize vector store
-# vector_store = None
-
-# # Store PDF file paths
-# pdf_files = {}
-
-# # Define the path for the FAISS index
-# FAISS_INDEX_PATH = "faiss_index"
-
-# def process_pdfs(directory: str) -> None:
-#     """Process all PDFs in the given directory and add them to the vector store."""
-#     global vector_store, pdf_files
-#     documents = []
-
-#     for filename in os.listdir(directory):
-#         if filename.endswith(".pdf"):
-#             file_path = os.path.join(directory, filename)
-#             loader = PyPDFLoader(file_path)
-#             documents.extend(loader.load())
-#             pdf_files[filename] = file_path
-
-#     text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-#     texts = text_splitter.split_documents(documents)
-
-#     if os.path.exists(FAISS_INDEX_PATH):
-#         vector_store = FAISS.load_local(FAISS_INDEX_PATH, embeddings, allow_dangerous_deserialization=True)
-#         vector_store.add_documents(texts)
-#     else:
-#         vector_store = FAISS.from_documents(texts, embeddings)
-
-#     # Save the updated vector store
-#     vector_store.save_local(FAISS_INDEX_PATH)
-# @cl.on_chat_start
-# async def start():
-#     """Initialize the chat session."""
-#     await cl.Message(content="Welcome! Processing PDFs...").send()
-
-#     # Process PDFs (replace with your PDF directory)
-#     process_pdfs(r"C:\Users\sumes\OneDrive\Documents\pdf_docs")
-
-#     await cl.Message(content="PDFs processed. You can now ask questions!").send()
-
-# @cl.on_message
-# async def main(message: cl.Message):
-#     """Handle user messages and generate responses."""
-#     if vector_store is None:
-#         await cl.Message(content="Error: Vector store not initialized.").send()
-#         return
-
-#     query = message.content
-
-#     retriever = vector_store.as_retriever(search_kwargs={"k": 1})
-
-#     llm = OpenAI(openai_api_key=openai.api_key, model="gpt-4o-mini", temperature=0.4)
-
-#     qa_chain = RetrievalQA.from_chain_type(
-#         llm=llm,
-#         chain_type="stuff",
-#         retriever=retriever,
-#         return_source_documents=True
-#     )
-
-#     result = qa_chain(query)
-#     answer = result['result']
-#     source_docs = result['source_documents']
-
-#     await cl.Message(content=answer).send()
-
-#     if source_docs:
-#         sources_message = "Sources:\n"
-#         for doc in source_docs:
-#             file_name = os.path.basename(doc.metadata['source'])
-#             if file_name in pdf_files:
-#                 file_path = pdf_files[file_name]
-#                 elements = [
-#                     cl.Text(name=file_name, content=f"Source: {file_name}"),
-#                     cl.File(name=file_name, path=file_path, display="inline")
-#                 ]
-#                 await cl.Message(content=f"Source: {file_name}", elements=elements).send()
-#             else:
-#                 sources_message += f"- {doc.metadata['source']}\n"
-        
-#         if sources_message != "Sources:\n":
-#             await cl.Message(content=sources_message).send()
-
-# if __name__ == "__main__":
-#     cl.run()
-
 import os
 from typing import List
 from dotenv import load_dotenv
@@ -122,13 +10,11 @@ from langchain.chains import RetrievalQA
 from langchain_openai import ChatOpenAI
 from langchain_openai import OpenAIEmbeddings
 
-
 # Load environment variables
 load_dotenv()
 
 # Initialize OpenAI API key
-
-openai_api_key = 'sk-None-Nn6BodKwwjNYiNYT2QtWT3BlbkFJqTm7b3Fq4HftPntWdkUa'
+openai_api_key = os.getenv("OPENAI_API_KEY")
 
 # Initialize embedding model using OpenAI
 embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key,model="text-embedding-3-small")
@@ -183,52 +69,6 @@ async def start():
 
     await cl.Message(content="PDFs processed. You can now ask questions!").send()
 
-# @cl.on_message
-# async def main(message: cl.Message):
-#     """Handle user messages and generate responses."""
-#     if vector_store is None:
-#         await cl.Message(content="Error: Vector store not initialized.").send()
-#         return
-
-#     query = message.content
-
-#     retriever = vector_store.as_retriever(search_kwargs={"k": 3})
-
-#     # Initialize the OpenAI language model
-#     llm = ChatOpenAI(openai_api_key=openai_api_key, model="gpt-4o-mini", temperature=0)
-
-#     qa_chain = RetrievalQA.from_chain_type(
-#         llm=llm,
-#         chain_type="stuff",
-#         retriever=retriever,
-#         return_source_documents=True
-#     )
-
-#     result = qa_chain(query)
-#     answer = result['result']
-#     source_docs = result['source_documents']
-
-#     await cl.Message(content=answer).send()
-
-#     if source_docs:
-#         sources_message = "Sources:\n"
-#         for doc in source_docs:
-#             file_name = os.path.basename(doc.metadata['source'])
-#             if file_name in pdf_files:
-#                 file_path = pdf_files[file_name]
-#                 elements = [
-#                     cl.Text(name=file_name, content=f"Source: {file_name}"),
-#                     cl.File(name=file_name, path=file_path, display="inline")
-#                 ]
-#                 await cl.Message(content=f"Source: {file_name}", elements=elements).send()
-#             else:
-#                 sources_message += f"- {doc.metadata['source']}\n"
-        
-#         if sources_message != "Sources:\n":
-#             await cl.Message(content=sources_message).send()
-
-
-
 @cl.on_message
 async def main(message: cl.Message):
     """Handle user messages and generate responses."""
@@ -276,4 +116,4 @@ async def main(message: cl.Message):
             await cl.Message(content=sources_message).send()
 
 if __name__ == "__main__":
-    cl.run()
+    cl.run()