app.py

import gradio as gr
import os
api_token = os.getenv("HF_TOKEN")

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_community.embeddings import HuggingFaceEmbeddings 
from langchain.memory import ConversationBufferMemory
from langchain_community.llms import HuggingFaceEndpoint

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]

# Funções existentes (load_doc, create_db, initialize_llmchain, etc.) permanecem iguais...

def format_chat_history(message, chat_history):
    formatted_chat_history = []
    for user_message, bot_message in chat_history:
        formatted_chat_history.append(f"User: {user_message}")
        formatted_chat_history.append(f"Assistant: {bot_message}")
    return formatted_chat_history

# Ajuste na função conversation para suportar idioma
def conversation(qa_chain, message, history, language):
    formatted_chat_history = format_chat_history(message, history)
    # Generate response using QA chain
    response = qa_chain.invoke({"question": message, "chat_history": formatted_chat_history})
    response_answer = response["answer"]
    if response_answer.find("Helpful Answer:") != -1:
        response_answer = response_answer.split("Helpful Answer:")[-1]
    
    # Ajustar resposta com base no idioma
    if language == "Português":
        # Aqui, idealmente, você usaria uma API de tradução ou o modelo geraria diretamente em português
        # Como exemplo, adiciono uma mensagem fixa para demonstrar
        response_answer = f"Resposta em português: {response_answer}"
    else:
        response_answer = f"Response in English: {response_answer}"

    response_sources = response["source_documents"]
    response_source1 = response_sources[0].page_content.strip()
    response_source2 = response_sources[1].page_content.strip()
    response_source3 = response_sources[2].page_content.strip()
    response_source1_page = response_sources[0].metadata["page"] + 1
    response_source2_page = response_sources[1].metadata["page"] + 1
    response_source3_page = response_sources[2].metadata["page"] + 1
    new_history = history + [(message, response_answer)]
    return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page

def demo():
    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky")) as demo:
        vector_db = gr.State()
        qa_chain = gr.State()
        gr.HTML("<center><h1>RAG PDF Chatbot</h1></center>")
        gr.Markdown("""<b>Query your PDF documents!</b> This AI agent is designed to perform retrieval augmented generation (RAG) on PDF documents. \
        <b>Please do not upload confidential documents.</b>""")
        
        with gr.Row():
            with gr.Column(scale=86):
                gr.Markdown("<b>Step 1 - Upload PDF documents and Initialize RAG pipeline</b>")
                document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
                db_btn = gr.Button("Create vector database")
                db_progress = gr.Textbox(value="Not initialized", show_label=False)
                gr.Markdown("<b>Select Large Language Model (LLM) and input parameters</b>")
                llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple[0], type="index")
                with gr.Accordion("LLM input parameters", open=False):
                    slider_temperature = gr.Slider(minimum=0.01, maximum=1.0, value=0.5, step=0.1, label="Temperature", interactive=True)
                    slider_maxtokens = gr.Slider(minimum=128, maximum=9192, value=4096, step=128, label="Max New Tokens", interactive=True)
                    slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k", interactive=True)
                qachain_btn = gr.Button("Initialize Question Answering Chatbot")
                llm_progress = gr.Textbox(value="Not initialized", show_label=False)

            with gr.Column(scale=200):
                gr.Markdown("<b>Step 2 - Chat with your Document</b>")
                # Adicionar seletor de idioma
                language_selector = gr.Radio(["English", "Português"], label="Select Language", value="English")
                chatbot = gr.Chatbot(height=505)
                with gr.Accordion("Relevant context from the source document", open=False):
                    doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
                    source1_page = gr.Number(label="Page", scale=1)
                    doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
                    source2_page = gr.Number(label="Page", scale=1)
                    doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
                    source3_page = gr.Number(label="Page", scale=1)
                msg = gr.Textbox(placeholder="Ask a question", container=True)
                submit_btn = gr.Button("Submit")
                clear_btn = gr.ClearButton([msg, chatbot], value="Clear")

        # Preprocessing events
        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]).then(
            lambda: [None, "", 0, "", 0, "", 0], inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False
        )

        # Chatbot events com o idioma
        msg.submit(conversation, inputs=[qa_chain, msg, chatbot, language_selector], outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False)
        submit_btn.click(conversation, inputs=[qa_chain, msg, chatbot, language_selector], outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False)
        clear_btn.click(lambda: [None, "", 0, "", 0, "", 0], inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False)

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

if __name__ == "__main__":
    demo()