Create app.py

app.py

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+import streamlit as st
+from streamlit_chat import message
+from langchain.chains import ConversationalRetrievalChain
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.llms import LlamaCpp
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+from langchain.document_loaders import PyPDFLoader
+import os
+import tempfile
+from langchain.document_loaders import PyPDFDirectoryLoader
+from langchain.chains import RetrievalQA
+
+
+
+
+def initialize_session_state():
+    if 'history' not in st.session_state:
+        st.session_state['history'] = []
+
+    if 'generated' not in st.session_state:
+        st.session_state['generated'] = ["Hello! Ask me anything about 🤗"]
+
+    if 'past' not in st.session_state:
+        st.session_state['past'] = ["Hey! 👋"]
+
+def conversation_chat(query, chain, history):
+    result = chain({"question": query, "chat_history": history})
+    history.append((query, result["answer"]))
+    return result["answer"]
+
+def display_chat_history(chain):
+    reply_container = st.container()
+    container = st.container()
+
+    with container:
+        with st.form(key='my_form', clear_on_submit=True):
+            user_input = st.text_input("Question:", placeholder="Ask about your PDF", key='input')
+            submit_button = st.form_submit_button(label='Send')
+
+        if submit_button and user_input:
+            with st.spinner('Generating response...'):
+                output = conversation_chat(user_input, chain, st.session_state['history'])
+
+            st.session_state['past'].append(user_input)
+            st.session_state['generated'].append(output)
+
+    if st.session_state['generated']:
+        with reply_container:
+            for i in range(len(st.session_state['generated'])):
+                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="thumbs")
+                message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji")
+
+def create_conversational_chain(vector_store):
+    # Create llm
+    # Importing the Model
+    llm = LlamaCpp(
+        streaming = True,
+        model_path = "model/mistral-7b-instruct-v0.2.Q4_K_M.gguf",
+        temperature = 0.75,
+        top_p = 1,
+        verbose = True,
+        n_ctx = 4096
+    )
+    
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+    chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type='stuff',
+                                                 retriever=vector_store.as_retriever(search_kwargs={"k": 2}),
+                                                 memory=memory)
+    return chain
+
+def main():
+    # Initialize session state
+    initialize_session_state()
+    st.title("Multi-PDF ChatBot using Mistral-7B-Instruct :books:")
+    # Initialize Streamlit
+    # st.sidebar.title("Document Processing")
+    # uploaded_files = st.sidebar.file_uploader("Upload files", accept_multiple_files=True)
+    # Loading the file directory using PyPDF Directory
+    loader = PyPDFDirectoryLoader("data_pdf/")
+    data = loader.load()
+
+    # Splitting extracted text data into chunks for easier processing
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size = 100000, chunk_overlap = 20)
+    text_chunks = text_splitter.split_documents(data)
+
+    # Downloading Embessings
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+    # Creating Embeddings for each of the text chunk
+    vector_store = FAISS.from_documents(text_chunks, embedding=embeddings)
+
+    # Create the chain object
+    chain = create_conversational_chain(vector_store)
+        
+    display_chat_history(chain)
+
+if __name__ == "__main__":
+    main()