Delete app.py.py

app.py.py

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-# -*- coding: utf-8 -*-
-"""RAG Conversational Chat Application using lanchain, Mistral 7B , Pinecone vector DB
-
-### Step-1: Upload Documents and Load with Langchain Document Loader
-- Upload the documents to Google Colab.
-- Use Langchain document loader to load the documents.
-
-### Step-2: Perform Chunking
-- Perform chunking on the loaded documents.
-
-### Step-3: Initialize LLM and Use Huggingface Embedding Model
-- Initialize a Large Language Model (LLM).
-- Use the Huggingface Embedding Model to convert the chunks into embeddings.
-
-### Step-4: Initialize Vector Database
-- Initialize a Vector Database to store the resulting embeddings.
-
-### Step-5: Upload Embeddings to Vector Database
-- Upload the embeddings to the Vector Database.
-
-### Step-6: Create Langchain Conversational Buffer Memory
-- Create a Langchain conversational buffer memory.
-
-### Step-7: Create Prompt Template
-- Create a prompt template for generating responses.
-
-### Step-8: Use Langchain RetreivalQA
-- Use Langchain RetreivalQA for creating the conversational chat.
-
-### Step-9: Create Front End with Streamlit
-- Create a front end for the application using Gradio.
-
-### Step-10: Upload Code to GitHub
-- Upload the code to a GitHub repository.
-
-### Step-11: Deploy App in Huggingface Spaces
-- Deploy the application in Huggingface Spaces.
-
-### Step-12: Create Documentation
-- Create documentation for the entire process followed.
-"""
-
-# Installing the required libraries
-# !pip install langchain
-# !pip install pypdf
-# !pip install sentence-transformers==2.2.2
-# !pip install pinecone-client==2.2.4
-# !pip install unstructured
-# !pip install "unstructured[pdf]"
-
-# initializing the Huggingface API to access Embeddig models
-# from google.colab import userdata
-# HUGGINGFACE_API_KEY = userdata.get('Hugging_Face_API_Key')
-# HUGGINGFACE_API_KEY=HUGGINGFACE_API_KEY
-
-# Creating a directory to store the data
-
-# from langchain.document_loaders import PyPDFDirectoryLoader
-
-# loader = PyPDFDirectoryLoader("data")
-
-# importing all the required Libraries
-from PyPDF2 import PdfReader
-from langchain.chains.question_answering import load_qa_chain
-from langchain.prompts import PromptTemplate
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-
-# from langchain.document_loaders import PyPDFDirectoryLoader
-# loader = PyPDFDirectoryLoader("data")
-# data = loader.load()
-
-# len(data)
-
-import os
-
-huggingfacehub_api_token = os.getenv("HF_API_TOKEN")
-
-def get_pdf_text(pdf_docs):
-    text=""
-    for pdf in pdf_docs:
-        pdf_reader= PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text+= page.extract_text()
-    return  text
-
-# creating chunking for the above data
-def get_text_chunks(text):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-# # creating chunking for the above data
-# from langchain.text_splitter import RecursiveCharacterTextSplitter
-# text_splitter=RecursiveCharacterTextSplitter(chunk_size=200,chunk_overlap=20)
-# chunked_data=text_splitter.split_text(data)
-
-# Create Embeddings using Huggingface Embeddings
-import sentence_transformers
-from langchain.embeddings import HuggingFaceEmbeddings
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-
-
-# Initializing Pinecone
-PINECONE_API_KEY=os.environ.get('PINECONE_API_KEY', 'f7384d73-ea97-45ca-abaa-9b14327fd50f')
-PINECONE_API_ENV=os.environ.get('PINECONE_API_ENV', 'gcp-starter')
-
-import pinecone
-# initialize pinecone
-pinecone.init(
-    api_key=PINECONE_API_KEY,  # find at app.pinecone.io
-    environment=PINECONE_API_ENV  # next to api key in console
-)
-index_name = "pinecone-demo" # put in the name of your pinecone index here
-
-from langchain.vectorstores import Pinecone
-
-# Load the data into pinecone database
-def get_vector_store(text_chunks):
-   #docsearch = Pinecone.from_texts(chunked_data, embeddings, index_name=index_name)
-   docsearch = Pinecone.from_texts([t for t in text_chunks], embeddings, index_name=index_name)
-   return docsearch
-
-
-# query = "How many topics are covered?"
-# docs = docsearch.similarity_search(query, k=1)
-# docs
-
-from langchain import HuggingFaceHub
-
-llm=HuggingFaceHub(huggingfacehub_api_token= huggingfacehub_api_token ,repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1")
-
-# from langchain.chains import RetrievalQA
-# # retriever = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=docsearch.as_retriever())
-# retriever = docsearch.as_retriever(search_kwargs={"k": 2})
-
-# qa_chain = RetrievalQA.from_chain_type(llm=llm,
-#                                   chain_type="stuff",
-#                                   retriever=retriever,
-#                                   return_source_documents=True)
-
-#question = "What are the Technical  Skills to learn for a Promising AI Career?"
-
-#print(qa_chain(question))
-
-## Adding Memory component
-memory = ConversationBufferMemory(
-    memory_key="chat_history",
-    return_messages=True, max_history_length=5
-)
-
-
-import streamlit as st
-
-
-# Chat History
-#chat = llm.start_chat(history=[])
-# intialize session state for chat history if it doesn't exist
-if 'chat_history' not in st.session_state:
-    st.session_state['chat_history'] = []
-
-def user_input(user_question):
-    # Load embeddings only once (assuming same model for both)
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-
-    # Pinecone search using the loaded embeddings
-    docsearch = Pinecone.from_existing_index(index_name, embeddings)
-    docs = docsearch.similarity_search(user_question)
-
-    # Define prompt template
-    template = """Answer the question as detailed as possible from the provided context, make sure to provide all the details,
-                if the answer is not available in the provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
-                {context}
-                Donot provide the Context , Provide the Answer only , to the question in the following format
-                Question: {question}
-                Helpful Answer:"""
-    prompt = PromptTemplate(input_variables=["context", "question"], template=template)
-    #prompt =  PromptTemplate(input_variables=["question"], template=template)
-    
-    # Create retriever and chain using the loaded embeddings
-    retriever = docsearch.as_retriever()
-    qa_chain = ConversationalRetrievalChain.from_llm(
-        llm, 
-        retriever=retriever,
-        memory=memory
-    )
-
-    # Extract context from retrieved documents (replace with your logic)
-    # Consider filtering or summarizing retrieved documents
-    #context = " ".join([doc.get("text", "") for doc in docs[:3]])
-    context = docs
-
-    # Inject prompt into query (alternative approach)
-    query = f"{template.format(context=context, question=user_question)}\nQuestion: {user_question}"
-    #query = f"{template.format( question=user_question)}\nQuestion: {user_question}"
-    
-    response = qa_chain(
-        {"question": query},
-        return_only_outputs=True
-    )
-
-    # Display response
-    #st.write("Reply: ", response["answer"])
-    Ans = extract_helpful_answer(response)
-    st.write(Ans)
-    
-    # Feature to load Chat history
-    if st.button("Load Chat History"):
-        # add user query and response to session chat history
-        st.session_state['chat_history'].append(("you",user_question))
-        # for chunk in Ans:
-        #     #st.write(chunk.text)
-        #     st.session_state['chat_history'].append(("AI Assistant",chunk))
-        st.session_state['chat_history'].append(("AI Assistant",Ans))
-        st.subheader("The chat history is ")
-        for role,text in st.session_state['chat_history']:
-            st.write(f"{role}: {text}")
-            
-    # Feature to load Related Context from the uploaded Documents
-    if st.button("Load Related Context from Your Document"):
-        related_context = docs
-        st.subheader("Related Context from Your Document:")
-        for doc in related_context:
-            st.write(f"Document: {doc}")
-            st.write("\n")
-    else:
-        st.warning("Please enter a question before loading related context.")
-
-def extract_helpful_answer(response):
-    # Split the response by the delimiter "Helpful Answer:"
-    parts = response["answer"].split("Helpful Answer:")
-
-    # If there are two parts (before and after "Helpful Answer:"), return the second part
-    return parts[2].strip()
-
-
-
-      
-def main():
-    #st.set_page_config("Chat PDF")
-    st.header("Chat with PDF using Mistral")
-
-    user_question = st.text_input("Ask a Question from the PDF Files")
-
-    if user_question:
-        user_input(user_question)
-
-    with st.sidebar:
-        st.title("Menu:")
-        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
-        if st.button("Submit & Process"):
-            with st.spinner("Processing..."):
-                raw_text = get_pdf_text(pdf_docs)
-                text_chunks = get_text_chunks(raw_text)
-                get_vector_store(text_chunks)
-                st.success("Done")
-
-
-
-if __name__ == "__main__":
-    main()
-