app.py

import os
import gradio as gr

from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceEndpoint

# ------------------------------
# Configuration & LLM Selection
# ------------------------------
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]

# Token đọc từ Space secret
api_token = os.getenv("hf_token")  # Space secret, không hardcode

# ------------------------------
# PDF Loading & Splitting
# ------------------------------
def load_doc(list_file_path):
    pages = []
    for file_path in list_file_path:
        try:
            loader = PyPDFLoader(file_path)
            pages.extend(loader.load())
        except Exception as e:
            print(f"Error loading {file_path}: {e}")
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1024,
        chunk_overlap=32
    )
    return text_splitter.split_documents(pages)

# ------------------------------
# Vector Database Creation
# ------------------------------
def create_db(doc_splits):
    embeddings = HuggingFaceEmbeddings()  # CPU-only
    vectordb = FAISS.from_documents(doc_splits, embeddings)
    return vectordb

# ------------------------------
# Initialize LLM + QA Chain
# ------------------------------
def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
    llm = HuggingFaceEndpoint(
        repo_id=llm_model,
        huggingfacehub_api_token=api_token,
        temperature=temperature,
        max_new_tokens=max_tokens,
        top_k=top_k,
    )

    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

# ------------------------------
# Database Initialization
# ------------------------------
def initialize_database(list_file_obj):
    list_file_path = [x.name for x in list_file_obj if x is not None]
    doc_splits = load_doc(list_file_path)
    vector_db = create_db(doc_splits)
    return vector_db, "Database created!"

# ------------------------------
# LLM Initialization
# ------------------------------
def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db):
    llm_name = list_llm[llm_option]
    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db)
    return qa_chain, "QA chain initialized. Chatbot is ready!"

# ------------------------------
# Conversation Utilities
# ------------------------------
def format_chat_history(chat_history, max_messages=5):
    formatted = []
    for user_msg, bot_msg in chat_history[-max_messages:]:
        formatted.append(f"User: {user_msg}")
        formatted.append(f"Assistant: {bot_msg}")
    return formatted

def conversation(qa_chain, message, history):
    formatted_history = format_chat_history(history)
    try:
        response = qa_chain.invoke({"question": message, "chat_history": formatted_history})
        answer = response["answer"]
        if "Helpful Answer:" in answer:
            answer = answer.split("Helpful Answer:")[-1]

        sources = response["source_documents"]
        top_sources = [(s.page_content.strip(), s.metadata.get("page", 0) + 1) for s in sources[:3]]
        while len(top_sources) < 3:
            top_sources.append(("", 0))

        new_history = history + [(message, answer)]
        return qa_chain, gr.update(value=""), new_history, *sum(top_sources, ())
    except Exception as e:
        print(f"Conversation error: {e}")
        return qa_chain, gr.update(value=""), history, "", 0, "", 0, "", 0

# ------------------------------
# Gradio UI
# ------------------------------
def demo():
    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink")) as demo:
        vector_db = gr.State()
        qa_chain = gr.State()

        gr.HTML("<center><h1>AERO RAG (CPU-only, Safe Secret)</h1></center>")
        gr.Markdown("<b>Query your PDF documents!</b> CPU-only mode. Token must be stored in Hugging Face Space secret `hf_token`.")

        with gr.Row():
            # Left Column
            with gr.Column(scale=1):
                document = gr.Files(file_count="multiple", file_types=[".pdf"], label="Upload PDFs")
                db_btn = gr.Button("Create vector DB")
                db_progress = gr.Textbox(value="Not initialized", show_label=False)

                llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple[0], type="index")
                slider_temperature = gr.Slider(0.01, 1.0, 0.5, 0.1, label="Temperature")
                slider_maxtokens = gr.Slider(128, 4096, 1024, 128, label="Max New Tokens")
                slider_topk = gr.Slider(1, 10, 3, 1, label="Top-K Tokens")
                qachain_btn = gr.Button("Initialize QA Chatbot")
                llm_progress = gr.Textbox(value="Not initialized", show_label=False)

            # Right Column
            with gr.Column(scale=8):
                chatbot = gr.Chatbot(height=480)
                doc_source1 = gr.Textbox(label="Reference 1", lines=2)
                source1_page = gr.Number(label="Page")
                doc_source2 = gr.Textbox(label="Reference 2", lines=2)
                source2_page = gr.Number(label="Page")
                doc_source3 = gr.Textbox(label="Reference 3", lines=2)
                source3_page = gr.Number(label="Page")
                msg = gr.Textbox(placeholder="Ask a question")
                submit_btn = gr.Button("Submit")
                clear_btn = gr.ClearButton([msg, chatbot], value="Clear")

        # Event Bindings
        db_btn.click(initialize_database, inputs=[document], outputs=[vector_db, db_progress])
        qachain_btn.click(initialize_LLM, inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db],
                           outputs=[qa_chain, llm_progress])
        msg.submit(conversation, inputs=[qa_chain, msg, chatbot],
                   outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page])
        submit_btn.click(conversation, inputs=[qa_chain, msg, chatbot],
                         outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page])
        clear_btn.click(lambda: [None, "", 0, "", 0, "", 0],
                        inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page])

    demo.queue().launch(debug=True)

if __name__ == "__main__":
    demo()