Create app.py

app.py

@@ -0,0 +1,183 @@
+import streamlit as st
+import os
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_huggingface import HuggingFaceEndpoint  # Updated import
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+import tempfile
+
+api_token = os.getenv("HF_TOKEN")
+list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]
+list_llm_simple = [os.path.basename(llm) for llm in list_llm]
+
+def load_doc(uploaded_files):
+    try:
+        temp_files = []
+        for uploaded_file in uploaded_files:
+            temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
+            temp_file.write(uploaded_file.read())
+            temp_file.close()
+            temp_files.append(temp_file.name)
+        
+        loaders = [PyPDFLoader(x) for x in temp_files]
+        pages = []
+        for loader in loaders:
+            pages.extend(loader.load())
+        
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)
+        doc_splits = text_splitter.split_documents(pages)
+
+        for temp_file in temp_files:
+            os.remove(temp_file)  # Clean up temporary files
+
+        return doc_splits
+    except Exception as e:
+        st.error(f"Error loading document: {e}")
+        return []
+
+def create_db(splits):
+    try:
+        embeddings = HuggingFaceEmbeddings()
+        vectordb = FAISS.from_documents(splits, embeddings)
+        return vectordb
+    except Exception as e:
+        st.error(f"Error creating vector database: {e}")
+        return None
+
+def initialize_llmchain(llm_model, vector_db):
+    try:
+        llm = HuggingFaceEndpoint(
+            repo_id=llm_model,
+            huggingfacehub_api_token=api_token,
+            temperature=0.5,
+            max_new_tokens=4096,
+            top_k=3,
+        )
+        memory = ConversationBufferMemory(
+            memory_key="chat_history",
+            output_key='answer',
+            return_messages=True
+        )
+
+        retriever = vector_db.as_retriever()
+        qa_chain = ConversationalRetrievalChain.from_llm(
+            llm,
+            retriever=retriever,
+            chain_type="stuff",
+            memory=memory,
+            return_source_documents=True,
+            verbose=False,
+        )
+        return qa_chain
+    except Exception as e:
+        st.error(f"Error initializing LLM chain: {e}")
+        return None
+
+def initialize_database(uploaded_files):
+    try:
+        doc_splits = load_doc(uploaded_files)
+        if not doc_splits:
+            return None, "Failed to load documents."
+        vector_db = create_db(doc_splits)
+        if vector_db is None:
+            return None, "Failed to create vector database."
+        return vector_db, "Database created!"
+    except Exception as e:
+        st.error(f"Error initializing database: {e}")
+        return None, "Failed to initialize database."
+
+def initialize_LLM(llm_option, vector_db):
+    try:
+        llm_name = list_llm[llm_option]
+        qa_chain = initialize_llmchain(llm_name, vector_db)
+        if qa_chain is None:
+            return None, "Failed to initialize QA chain."
+        return qa_chain, "QA chain initialized. Chatbot is ready!"
+    except Exception as e:
+        st.error(f"Error initializing LLM: {e}")
+        return None, "Failed to initialize LLM."
+
+def format_chat_history(chat_history):
+    formatted_chat_history = []
+    for user_message, bot_message in chat_history:
+        formatted_chat_history.append(f"User: {user_message}\nAssistant: {bot_message}\n")
+    return formatted_chat_history
+
+def conversation(qa_chain, message, history):
+    try:
+        formatted_chat_history = format_chat_history(history)
+        response = qa_chain.invoke({"question": message, "chat_history": formatted_chat_history})
+        response_answer = response["answer"]
+        response_sources = response["source_documents"]
+
+        sources = []
+        for doc in response_sources:
+            sources.append({
+                "content": doc.page_content.strip(),
+                "page": doc.metadata["page"] + 1
+            })
+
+        new_history = history + [(message, response_answer)]
+        return qa_chain, new_history, response_answer, sources
+    except Exception as e:
+        st.error(f"Error in conversation: {e}")
+        return qa_chain, history, "", []
+
+def main():
+    st.sidebar.title("PDF Chatbot")
+
+    st.sidebar.markdown("### Step 1 - Upload PDF documents and Initialize RAG pipeline")
+    uploaded_files = st.sidebar.file_uploader("Upload PDF documents", type="pdf", accept_multiple_files=True)
+
+    if uploaded_files:
+        if st.sidebar.button("Create vector database"):
+            with st.spinner("Creating vector database..."):
+                vector_db, db_message = initialize_database(uploaded_files)
+                st.sidebar.success(db_message)
+                st.session_state['vector_db'] = vector_db
+
+    if 'vector_db' not in st.session_state:
+        st.session_state['vector_db'] = None
+
+    if 'qa_chain' not in st.session_state:
+        st.session_state['qa_chain'] = None
+
+    if 'chat_history' not in st.session_state:
+        st.session_state['chat_history'] = []
+
+    st.sidebar.markdown("### Select Large Language Model (LLM)")
+    llm_option = st.sidebar.radio("Available LLMs", list_llm_simple)
+
+    if st.sidebar.button("Initialize Question Answering Chatbot"):
+        with st.spinner("Initializing QA chatbot..."):
+            qa_chain, llm_message = initialize_LLM(list_llm_simple.index(llm_option), st.session_state['vector_db'])
+            st.session_state['qa_chain'] = qa_chain
+            st.sidebar.success(llm_message)
+
+    st.title("Chat with your Document")
+
+    if st.session_state['qa_chain']:
+        message = st.text_input("Ask a question")
+
+        if st.button("Submit"):
+            with st.spinner("Generating response..."):
+                qa_chain, chat_history, response_answer, sources = conversation(st.session_state['qa_chain'], message, st.session_state['chat_history'])
+                st.session_state['qa_chain'] = qa_chain
+                st.session_state['chat_history'] = chat_history
+
+        st.markdown("### Chatbot Response")
+
+        # Display the chat history in a chat-like interface
+        for i, (user_msg, bot_msg) in enumerate(st.session_state['chat_history']):
+            st.markdown(f"**User:** {user_msg}")
+            st.markdown(f"**Assistant:** {bot_msg}")
+
+        with st.expander("Relevant context from the source document"):
+            for source in sources:
+                st.text_area(f"Source - Page {source['page']}", value=source["content"], height=100)
+
+if __name__ == "__main__":
+    main()