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| # 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("Goq_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 | |
| if "vector_db" not in st.session_state: | |
| text = extract_text_from_pdf(pdf_path) | |
| chunks = chunk_text(text) | |
| st.session_state.vector_db = create_embeddings_and_store(chunks) | |
| # Initialize chat history if not already done | |
| 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 | |
| query_key = f"query_{len(st.session_state.chat_history) + 1}" | |
| user_query = st.text_input("Enter your query:", key=query_key) | |
| if user_query: | |
| # Generate response | |
| response = query_vector_db(user_query, st.session_state.vector_db) | |
| # Append query and response to the chat history | |
| st.session_state.chat_history.append({"query": user_query, "response": response}) | |
| # Refresh the app without needing manual rerun | |
| st.experimental_set_query_params(rerun="true") | |