Delete app.py

app.py

@@ -1,195 +0,0 @@
-import streamlit as st
-import os
-import tempfile
-from langchain_community.vectorstores import FAISS
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_huggingface import HuggingFaceEmbeddings, HuggingFaceEndpoint
-from langchain.chains import ConversationalRetrievalChain
-from langchain.memory import ConversationBufferMemory
-
-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']:
-        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}")
-
-        st.markdown("### Relevant context from the source document")
-
-        with st.expander("Relevant context from the source document"):
-            if 'sources' in st.session_state:
-                for i, source in enumerate(st.session_state['sources']):
-                    st.text_area(f"Source {i + 1} - Page {source['page']}", value=source["content"], height=100)
-
-        message = st.text_input("Ask a question", key="message")
-        if st.button("Submit"):
-            if message:
-                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.session_state['sources'] = sources
-
-                    # Display the new response immediately
-                    st.markdown(f"**User:** {message}")
-                    st.markdown(f"**Assistant:** {response_answer}")
-
-                    st.markdown("### Relevant context from the source document")
-                    with st.expander("Relevant context from the source document"):
-                        for i, source in enumerate(sources):
-                            st.text_area(f"Source {i + 1} - Page {source['page']}", value=source["content"], height=100)
-
-if __name__ == "__main__":
-    main()