Delete app.py

app.py

@@ -1,214 +0,0 @@
-import os
-import streamlit as st
-import logging
-from langchain_community.chat_message_histories import StreamlitChatMessageHistory
-from langchain_community.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings
-from langchain_community.retrievers import BM25Retriever
-
-from ensemble import ensemble_retriever_from_docs
-from full_chain import create_full_chain, ask_question
-from local_loader import load_data_files, load_file
-from vector_store import EmbeddingProxy 
-from memory import clean_session_history
-from pathlib import Path
-
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain.schema import AIMessage, HumanMessage
-
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-def show_ui(message, history, request: gr.Request):
-    """
-    Displays the Streamlit chat UI and handles user interactions.
-
-    Args:
-        qa: The LangChain chain for question answering.
-        prompt_to_user: The initial prompt to display to the user.
-    """
-    global chain
-    session_id = request.session_hash
-    response = ask_question(chain, message, session_id)
-    # logging.info(f"Response: {response}")
-    return response.content
-
-
-def get_retriever(openai_api_key=None):
-    """
-    Creates and caches the document retriever.
-
-    Args:
-        openai_api_key: The OpenAI API key.
-
-    Returns:
-        An ensemble document retriever.
-    """
-    try:
-        docs = load_data_files(data_dir="data")  
-        # embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key, model="text-embedding-3-small")
-        embeddings = HuggingFaceEmbeddings()
-        return ensemble_retriever_from_docs(docs, embeddings=embeddings)
-    except Exception as e:
-        logging.error(f"Error creating retriever: {e}")
-        logging.exception(f"message")
-        st.error("Error initializing the application. Please check the logs.")
-        st.stop()  # Stop execution if retriever creation fails
-
-
-def get_chain(openai_api_key=None, huggingfacehub_api_token=None):
-    """
-    Creates the question answering chain.
-
-    Args:
-        openai_api_key: The OpenAI API key.
-        huggingfacehub_api_token: The Hugging Face Hub API token.
-
-    Returns:
-        A LangChain question answering chain.
-    """
-    try:
-        ensemble_retriever = get_retriever(openai_api_key=openai_api_key)
-        chain = create_full_chain(
-            ensemble_retriever,
-            openai_api_key=openai_api_key,
-        )
-        return ensemble_retriever, chain
-    except Exception as e:
-        logging.error(f"Error creating chain: {e}")
-        logging.exception(f"message")
-        st.error("Error initializing the application. Please check the logs.")
-        st.stop()  # Stop execution if chain creation fails
-
-def get_secret_or_input(secret_key, secret_name, info_link=None):
-    """
-    Retrieves a secret from Streamlit secrets or prompts the user for input.
-
-    Args:
-        secret_key: The key of the secret in Streamlit secrets.
-        secret_name: The user-friendly name of the secret.
-        info_link: An optional link to provide information about the secret.
-
-    Returns:
-        The secret value.
-    """
-    if secret_key in st.secrets:
-        st.write("Found %s secret" % secret_key)
-        secret_value = st.secrets[secret_key]
-    else:
-        st.write(f"Please provide your {secret_name}")
-        secret_value = st.text_input(secret_name, key=f"input_{secret_key}", type="password")
-        if secret_value:
-            st.session_state[secret_key] = secret_value
-        if info_link:
-            st.markdown(f"[Get an {secret_name}]({info_link})")
-    return secret_value
-
-def process_uploaded_file(uploaded_file):
-    """
-    Processes the uploaded file and adds it to the vector database.
-
-    Args:
-        uploaded_file: The uploaded file object from Streamlit.
-        openai_api_key: The OpenAI API key for embedding generation.
-    """
-    # try:
-    if uploaded_file is not None:
-        logging.info(f'run upload {uploaded_file}')
-
-        if isinstance(uploaded_file, str):
-            filename = uploaded_file
-        else:
-            filename = str(uploaded_file.name)
-
-        # Load the document using the saved file path
-        docs = load_file(Path(filename))
-
-        global ensemble_retriever
-        global chain
-
-        all_docs = ensemble_retriever.retrievers[0].docs
-        all_docs.extend(docs)
-
-        ensemble_retriever.retrievers[1].add_documents(docs)
-
-        new_bm25 = BM25Retriever.from_texts([t.page_content for t in all_docs])
-
-        ensemble_retriever.retrievers[0] = new_bm25
-
-        chain = create_full_chain(
-            ensemble_retriever,
-            openai_api_key=open_api_key,
-        )
-
-        logging.info("File uploaded and added to the knowledge base!")
-        gr.Info('File uploaded and added to the knowledge base!', duration=3)
-    
-    return None
-        
-    # except Exception as e:
-    #     logging.error(f"Error processing uploaded file: {e}")
-    #     st.error("Error processing the file. Please check the logs.")
-
-SUPPORTED_FORMATS = ['.txt']
-
-def activate():
-    return gr.update(interactive=True)
-
-def deactivate():
-    return gr.update(interactive=False)
-
-def reset(z, request: gr.Request):
-    session_id = request.session_hash
-    clean_session_history(session_id)
-    return [], []
-
-def main():
-    with gr.Blocks() as demo:
-        with gr.Row(variant="panel"):
-            with gr.Column(scale=5):
-                clean_btn = gr.Button(value="Clean history", variant="secondary", size='sm', render=False)
-                bot = gr.Chatbot(elem_id="chatbot", render=False)
-
-                chat = gr.ChatInterface(
-                    show_ui,
-                    chatbot=bot,
-                    undo_btn=None,
-                    retry_btn=None,
-                    clear_btn=clean_btn,
-                    
-                )
-            with gr.Column(scale=2):
-                file_input = gr.File(
-                    label=f'{", ".join([str(f) for f in SUPPORTED_FORMATS])}',
-                    file_types=SUPPORTED_FORMATS,
-                )
-                submit_btn = gr.Button(value="Index file", variant="primary", interactive=False)
-
-        clean_btn.click(fn=reset, inputs=clean_btn, outputs=[bot, chat.chatbot_state])
-
-        submit_btn.click(
-                fn=process_uploaded_file,
-                inputs=file_input,
-                outputs=file_input,
-                api_name="Index file"
-            )
-        
-        file_input.upload(fn=activate, outputs=[submit_btn])
-        file_input.clear(fn=deactivate, outputs=[submit_btn])
-
-    demo.launch(share=True)
-
-open_api_key = os.getenv('OPEN_API_KEY')
-# os.environ["TAVILY_API_KEY"] = os.getenv('TAVILY_API_KEY')
-
-ensemble_retriever, chain = get_chain(
-    openai_api_key=open_api_key,
-    huggingfacehub_api_token=None
-)
-
-
-
-if __name__ == "__main__":
-    main()