# 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 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.environ.get("GROQ_API_KEY")) client = Groq(api_key=os.getenv("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("Pdf reading AI Application") # 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 text = extract_text_from_pdf(pdf_path) # st.write("PDF Text Extracted Successfully!") # Chunk text chunks = chunk_text(text) # st.write("Text Chunked Successfully!") # Generate embeddings and store in FAISS vector_db = create_embeddings_and_store(chunks) # st.write("Embeddings Generated and Stored Successfully!") # Interactive chat section st.write("### Interactive Chat Section") # State management for chat history if "chat_history" not in st.session_state: st.session_state.chat_history = [] # User query input user_query = st.text_input("Enter your query:", key="user_query") if st.button("Submit Query"): if user_query: # Get response from the model response = query_vector_db(user_query, vector_db) # Append the query and response to the chat history st.session_state.chat_history.append({"query": user_query, "response": response}) # Display chat history for chat in st.session_state.chat_history: st.write(f"**User Query:** {chat['query']}") st.write(f"**Response:** {chat['response']}") st.write("---")