Update app.py

app.py

@@ -1,98 +1,4 @@
-# import os
-# from groq import Groq
-# from langchain_community.embeddings import HuggingFaceEmbeddings
-# from langchain_community.vectorstores import FAISS
-# from langchain.text_splitter import RecursiveCharacterTextSplitter
-# from PyPDF2 import PdfReader
-# import streamlit as st
-# from tempfile import NamedTemporaryFile
-
-# # Initialize Groq client
-# client = Groq(api_key=os.getenv("Groq_api_key"))
-# # client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
-
-# # Function to extract text from a PDF
-# def extract_text_from_pdf(pdf_file_path):
-#     pdf_reader = PdfReader(pdf_file_path)
-#     text = ""
-#     for page in pdf_reader.pages:
-#         text += page.extract_text()
-#     return text
-
-# # Function to split text into chunks
-# def chunk_text(text, chunk_size=500, chunk_overlap=50):
-#     text_splitter = RecursiveCharacterTextSplitter(
-#         chunk_size=chunk_size, chunk_overlap=chunk_overlap
-#     )
-#     return text_splitter.split_text(text)
-
-# # Function to create embeddings and store them in FAISS
-# def create_embeddings_and_store(chunks):
-#     embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-#     vector_db = FAISS.from_texts(chunks, embedding=embeddings)
-#     return vector_db
-
-# # Function to query the vector database and interact with Groq
-# def query_vector_db(query, vector_db):
-#     # Retrieve relevant documents
-#     docs = vector_db.similarity_search(query, k=3)
-#     context = "\n".join([doc.page_content for doc in docs])
-
-#     # Interact with Groq API
-#     chat_completion = client.chat.completions.create(
-#         messages=[
-#             {"role": "system", "content": f"Use the following context:\n{context}"},
-#             {"role": "user", "content": query},
-#         ],
-#         model="llama3-8b-8192",
-#     )
-#     return chat_completion.choices[0].message.content
-
-# # Streamlit app
-# st.title("Interactive PDF Reader and Chat")
-
-# # Upload PDF
-# uploaded_file = st.file_uploader("Upload a PDF document", type=["pdf"])
-
-# if uploaded_file:
-#     with NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
-#         temp_file.write(uploaded_file.read())
-#         pdf_path = temp_file.name
-
-#     # Extract text, chunk it, and create embeddings
-#     text = extract_text_from_pdf(pdf_path)
-#     chunks = chunk_text(text)
-#     vector_db = create_embeddings_and_store(chunks)
-
-#     # State management for the chat
-#     if "chat_history" not in st.session_state:
-#         st.session_state.chat_history = []
-
-#     # Display chat history
-#     for i, chat in enumerate(st.session_state.chat_history):
-#         st.write(f"**Query {i+1}:** {chat['query']}")
-#         st.write(f"**Response:** {chat['response']}")
-#         st.write("---")
-
-#     # Add new query input dynamically
-#     if "query_count" not in st.session_state:
-#         st.session_state.query_count = 1
-
-#     query_key = f"query_{st.session_state.query_count}"
-#     user_query = st.text_input(f"Enter Query {st.session_state.query_count}:", key=query_key)
-
-#     if user_query:
-#         # Generate response
-#         response = query_vector_db(user_query, vector_db)
-
-#         # Append query and response to the chat history
-#         st.session_state.chat_history.append({"query": user_query, "response": response})
-
-#         # Increment query count for the next input box
-#         st.session_state.query_count += 1
-
-#         # Rerun to show the updated UI
-#         st.experimental_rerun()
+
 
 import os
 from groq import Groq