Update app.py

app.py

@@ -1,287 +1,189 @@
 import gradio as gr
 import os
-
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import Chroma
 from langchain.chains import ConversationalRetrievalChain
-from langchain_community.embeddings import HuggingFaceEmbeddings 
-from langchain_community.llms import HuggingFacePipeline
-from langchain.chains import ConversationChain
-from langchain.memory import ConversationBufferMemory
+from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.llms import HuggingFaceEndpoint
-
+from langchain.memory import ConversationBufferMemory
 from pathlib import Path
 import chromadb
 from unidecode import unidecode
-
-from transformers import AutoTokenizer
-import transformers
-import torch
-import tqdm 
-import accelerate
 import re
 
-
-
-# default_persist_directory = './chroma_HF/'
-list_llm = ["mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.1", \
-    "google/gemma-7b-it","google/gemma-2b-it", \
-    "HuggingFaceH4/zephyr-7b-beta", "HuggingFaceH4/zephyr-7b-gemma-v0.1", \
-    "meta-llama/Llama-2-7b-chat-hf", "microsoft/phi-2", \
-    "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "mosaicml/mpt-7b-instruct", "tiiuae/falcon-7b-instruct", \
+# Lista de modelos LLM disponíveis
+list_llm = [
+    "mistralai/Mistral-7B-Instruct-v0.2",
+    "mistralai/Mixtral-8x7B-Instruct-v0.1",
+    "mistralai/Mistral-7B-Instruct-v0.1",
+    "google/gemma-7b-it",
+    "google/gemma-2b-it",
+    "HuggingFaceH4/zephyr-7b-beta",
+    "HuggingFaceH4/zephyr-7b-gemma-v0.1",
+    "meta-llama/Llama-2-7b-chat-hf",
+    "microsoft/phi-2",
+    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    "mosaicml/mpt-7b-instruct",
+    "tiiuae/falcon-7b-instruct",
     "google/flan-t5-xxl"
 ]
 list_llm_simple = [os.path.basename(llm) for llm in list_llm]
 
-# Load PDF document and create doc splits
+# Função para carregar documentos PDF e dividir em chunks
 def load_doc(list_file_path, chunk_size, chunk_overlap):
-    # Processing for one document only
-    # loader = PyPDFLoader(file_path)
-    # pages = loader.load()
     loaders = [PyPDFLoader(x) for x in list_file_path]
     pages = []
     for loader in loaders:
         pages.extend(loader.load())
-    # text_splitter = RecursiveCharacterTextSplitter(chunk_size = 600, chunk_overlap = 50)
     text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size = chunk_size, 
-        chunk_overlap = chunk_overlap)
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap
+    )
     doc_splits = text_splitter.split_documents(pages)
     return doc_splits
 
-
-# Create vector database
+# Função para criar o banco de dados vetorial
 def create_db(splits, collection_name):
     embedding = HuggingFaceEmbeddings()
-    new_client = chromadb.EphemeralClient()
+    # Usando PersistentClient para persistir o banco de dados
+    new_client = chromadb.PersistentClient(path="./chroma_db")
     vectordb = Chroma.from_documents(
         documents=splits,
         embedding=embedding,
         client=new_client,
         collection_name=collection_name,
-        # persist_directory=default_persist_directory
     )
     return vectordb
 
-
-# Load vector database
-def load_db():
-    embedding = HuggingFaceEmbeddings()
-    vectordb = Chroma(
-        # persist_directory=default_persist_directory, 
-        embedding_function=embedding)
-    return vectordb
-
-
-# Initialize langchain LLM chain
+# Função para inicializar a cadeia de QA com o modelo LLM
 def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
-    progress(0.1, desc="Initializing HF tokenizer...")
-    # HuggingFacePipeline uses local model
-    # Note: it will download model locally...
-    # tokenizer=AutoTokenizer.from_pretrained(llm_model)
-    # progress(0.5, desc="Initializing HF pipeline...")
-    # pipeline=transformers.pipeline(
-    #     "text-generation",
-    #     model=llm_model,
-    #     tokenizer=tokenizer,
-    #     torch_dtype=torch.bfloat16,
-    #     trust_remote_code=True,
-    #     device_map="auto",
-    #     # max_length=1024,
-    #     max_new_tokens=max_tokens,
-    #     do_sample=True,
-    #     top_k=top_k,
-    #     num_return_sequences=1,
-    #     eos_token_id=tokenizer.eos_token_id
-    #     )
-    # llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': temperature})
-    
-    # HuggingFaceHub uses HF inference endpoints
-    progress(0.5, desc="Initializing HF Hub...")
-    # Use of trust_remote_code as model_kwargs
-    # Warning: langchain issue
-    # URL: https://github.com/langchain-ai/langchain/issues/6080
+    progress(0.1, desc="Inicializando tokenizer da HF...")
+    progress(0.5, desc="Inicializando Hub da HF...")
     if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
         llm = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
-            load_in_8bit = True,
-        )
-    elif llm_model in ["HuggingFaceH4/zephyr-7b-gemma-v0.1","mosaicml/mpt-7b-instruct"]:
-        raise gr.Error("LLM model is too large to be loaded automatically on free inference endpoint")
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
+            repo_id=llm_model,
+            temperature=temperature,
+            max_new_tokens=max_tokens,
+            top_k=top_k,
+            load_in_8bit=True,
         )
+    elif llm_model in ["HuggingFaceH4/zephyr-7b-gemma-v0.1", "mosaicml/mpt-7b-instruct"]:
+        raise gr.Error("O modelo LLM é muito grande para ser carregado automaticamente no endpoint de inferência gratuito")
     elif llm_model == "microsoft/phi-2":
-        # raise gr.Error("phi-2 model requires 'trust_remote_code=True', currently not supported by langchain HuggingFaceHub...")
         llm = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
-            trust_remote_code = True,
-            torch_dtype = "auto",
+            repo_id=llm_model,
+            temperature=temperature,
+            max_new_tokens=max_tokens,
+            top_k=top_k,
+            trust_remote_code=True,
+            torch_dtype="auto",
         )
     elif llm_model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0":
         llm = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": 250, "top_k": top_k}
-            temperature = temperature,
-            max_new_tokens = 250,
-            top_k = top_k,
+            repo_id=llm_model,
+            temperature=temperature,
+            max_new_tokens=250,
+            top_k=top_k,
         )
     elif llm_model == "meta-llama/Llama-2-7b-chat-hf":
-        raise gr.Error("Llama-2-7b-chat-hf model requires a Pro subscription...")
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
-        )
+        raise gr.Error("O modelo Llama-2-7b-chat-hf requer uma assinatura Pro...")
     else:
         llm = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
+            repo_id=llm_model,
+            temperature=temperature,
+            max_new_tokens=max_tokens,
+            top_k=top_k,
         )
-    
-    progress(0.75, desc="Defining buffer memory...")
+
+    progress(0.75, desc="Definindo memória de buffer...")
     memory = ConversationBufferMemory(
         memory_key="chat_history",
         output_key='answer',
         return_messages=True
     )
-    # retriever=vector_db.as_retriever(search_type="similarity", search_kwargs={'k': 3})
-    retriever=vector_db.as_retriever()
-    progress(0.8, desc="Defining retrieval chain...")
+    retriever = vector_db.as_retriever()
+    progress(0.8, desc="Definindo cadeia de recuperação...")
     qa_chain = ConversationalRetrievalChain.from_llm(
         llm,
         retriever=retriever,
-        chain_type="stuff", 
+        chain_type="stuff",
         memory=memory,
-        # combine_docs_chain_kwargs={"prompt": your_prompt})
         return_source_documents=True,
-        #return_generated_question=False,
         verbose=False,
     )
-    progress(0.9, desc="Done!")
+    progress(0.9, desc="Concluído!")
     return qa_chain
 
-
-# Generate collection name for vector database
-#  - Use filepath as input, ensuring unicode text
+# Função para gerar um nome de coleção válido
 def create_collection_name(filepath):
-    # Extract filename without extension
     collection_name = Path(filepath).stem
-    # Fix potential issues from naming convention
-    ## Remove space
-    collection_name = collection_name.replace(" ","-") 
-    ## ASCII transliterations of Unicode text
+    collection_name = collection_name.replace(" ", "-")
     collection_name = unidecode(collection_name)
-    ## Remove special characters
-    #collection_name = re.findall("[\dA-Za-z]*", collection_name)[0]
     collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
-    ## Limit length to 50 characters
     collection_name = collection_name[:50]
-    ## Minimum length of 3 characters
     if len(collection_name) < 3:
         collection_name = collection_name + 'xyz'
-    ## Enforce start and end as alphanumeric character
     if not collection_name[0].isalnum():
         collection_name = 'A' + collection_name[1:]
     if not collection_name[-1].isalnum():
         collection_name = collection_name[:-1] + 'Z'
-    print('Filepath: ', filepath)
-    print('Collection name: ', collection_name)
+    print('Caminho do arquivo: ', filepath)
+    print('Nome da coleção: ', collection_name)
     return collection_name
 
-
-# Initialize database
+# Função para inicializar o banco de dados
 def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
-    # Create list of documents (when valid)
     list_file_path = [x.name for x in list_file_obj if x is not None]
-    # Create collection_name for vector database
-    progress(0.1, desc="Creating collection name...")
+    progress(0.1, desc="Criando nome da coleção...")
     collection_name = create_collection_name(list_file_path[0])
-    progress(0.25, desc="Loading document...")
-    # Load document and create splits
+    progress(0.25, desc="Carregando documento...")
     doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
-    # Create or load vector database
-    progress(0.5, desc="Generating vector database...")
-    # global vector_db
+    progress(0.5, desc="Gerando banco de dados vetorial...")
     vector_db = create_db(doc_splits, collection_name)
-    progress(0.9, desc="Done!")
-    return vector_db, collection_name, "Complete!"
-
+    progress(0.9, desc="Concluído!")
+    return vector_db, collection_name, "Completo!"
 
+# Função para inicializar o modelo LLM
 def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
-    # print("llm_option",llm_option)
     llm_name = list_llm[llm_option]
-    print("llm_name: ",llm_name)
+    print("Nome do LLM: ", llm_name)
     qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
-    return qa_chain, "Complete!"
-
+    return qa_chain, "Completo!"
 
+# Função para formatar o histórico de conversa
 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}")
+        formatted_chat_history.append(f"Usuário: {user_message}")
+        formatted_chat_history.append(f"Assistente: {bot_message}")
     return formatted_chat_history
-    
 
+# Função para realizar a conversa com o chatbot
 def conversation(qa_chain, message, history):
     formatted_chat_history = format_chat_history(message, history)
-    #print("formatted_chat_history",formatted_chat_history)
-   
-    # Generate response using QA chain
     response = qa_chain({"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]
+    if response_answer.find("Resposta útil:") != -1:
+        response_answer = response_answer.split("Resposta útil:")[-1]
     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()
-    # Langchain sources are zero-based
     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
-    # print ('chat response: ', response_answer)
-    # print('DB source', response_sources)
-    
-    # Append user message and response to chat history
     new_history = history + [(message, response_answer)]
-    # return gr.update(value=""), new_history, response_sources[0], response_sources[1] 
     return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
-    
 
+# Função para carregar arquivos
 def upload_file(file_obj):
     list_file_path = []
     for idx, file in enumerate(file_obj):
         file_path = file_obj.name
         list_file_path.append(file_path)
-    # print(file_path)
-    # initialize_database(file_path, progress)
     return list_file_path
 
-
-import gradio as gr
-
-import gradio as gr
-
 def demo():
     with gr.Blocks(theme="base") as demo:
         vector_db = gr.State()
@@ -289,82 +191,85 @@ def demo():
         collection_name = gr.State()
         
         gr.Markdown(
-        """<center><h2>PDF-based chatbot</center></h2>
-        <h3>Ask any questions about your PDF documents</h3>""")
+        """<center><h2>Chatbot baseado em PDF</center></h2>
+        <h3>Faça qualquer pergunta sobre seus documentos PDF</h3>""")
         gr.Markdown(
-        """<b>Note:</b> This AI assistant, using Langchain and open-source LLMs, performs retrieval-augmented generation (RAG) from your PDF documents. \
-        The user interface explicitly shows multiple steps to help understand the RAG workflow. 
-        This chatbot takes past questions into account when generating answers (via conversational memory), and includes document references for clarity purposes.<br>
-        <br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate a reply.
+        """<b>Nota:</b> Este assistente de IA, utilizando Langchain e LLMs de código aberto, realiza geração aumentada por recuperação (RAG) a partir de seus documentos PDF. \
+        A interface do usuário mostra explicitamente várias etapas para ajudar a entender o fluxo de trabalho do RAG. 
+        Este chatbot leva em consideração perguntas anteriores ao gerar respostas (via memória conversacional), e inclui referências documentais para maior clareza.<br>
+        <br><b>Aviso:</b> Este espaço usa a CPU básica gratuita do Hugging Face. Algumas etapas e modelos LLM utilizados abaixo (pontos finais de inferência gratuitos) podem levar algum tempo para gerar uma resposta.
         """)
         
-        with gr.Tab("Step 1 - Upload PDF"):
+        with gr.Tab("Etapa 1 - Carregar PDF"):
             with gr.Row():
-                document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
+                document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Carregue seus documentos PDF (único ou múltiplos)")
+                # upload_btn = gr.UploadButton("Carregando documento...", height=100, file_count="multiple", file_types=["pdf"], scale=1)
         
-        with gr.Tab("Step 2 - Process document"):
+        with gr.Tab("Etapa 2 - Processar documento"):
             with gr.Row():
-                db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value = "ChromaDB", type="index", info="Choose your vector database")
-            with gr.Accordion("Advanced options - Document text splitter", open=False):
+                db_btn = gr.Radio(["ChromaDB"], label="Tipo de banco de dados vetorial", value = "ChromaDB", type="index", info="Escolha o banco de dados vetorial")
+            with gr.Accordion("Opções avançadas - Divisor de texto do documento", open=False):
                 with gr.Row():
-                    slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True)
+                    slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Tamanho do bloco", info="Tamanho do bloco", interactive=True)
                 with gr.Row():
-                    slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True)
+                    slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label="Sobreposição do bloco", info="Sobreposição do bloco", interactive=True)
             with gr.Row():
-                db_progress = gr.Textbox(label="Vector database initialization", value="None")
+                db_progress = gr.Textbox(label="Inicialização do banco de dados vetorial", value="Nenhum")
             with gr.Row():
-                db_btn = gr.Button("Generate vector database")
+                db_btn = gr.Button("Gerar banco de dados vetorial")
             
-        with gr.Tab("Step 3 - Initialize QA chain"):
+        with gr.Tab("Etapa 3 - Inicializar cadeia de QA"):
             with gr.Row():
-                llm_btn = gr.Radio(["LLM1", "LLM2"], label="LLM models", value = "LLM1", type="index", info="Choose your LLM model")
-            with gr.Accordion("Advanced options - LLM model", open=False):
+                llm_btn = gr.Radio(list_llm_simple, \
+                    label="Modelos LLM", value = list_llm_simple[0], type="index", info="Escolha seu modelo LLM")
+            with gr.Accordion("Opções avançadas - Modelo LLM", open=False):
                 with gr.Row():
-                    slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
+                    slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.7, step=0.1, label="Temperatura", info="Temperatura do modelo", interactive=True)
                 with gr.Row():
-                    slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
+                    slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Máximo de Tokens", info="Máximo de tokens do modelo", interactive=True)
                 with gr.Row():
-                    slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True)
+                    slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="Amostras top-k", info="Amostras top-k do modelo", interactive=True)
             with gr.Row():
-                llm_progress = gr.Textbox(value="None",label="QA chain initialization")
+                llm_progress = gr.Textbox(value="Nenhum",label="Inicialização da cadeia QA")
             with gr.Row():
-                qachain_btn = gr.Button("Initialize Question Answering chain")
+                qachain_btn = gr.Button("Inicializar cadeia de Pergunta e Resposta")
 
-        with gr.Tab("Step 4 - Chatbot"):
+        with gr.Tab("Etapa 4 - Chatbot"):
             chatbot = gr.Chatbot(height=300)
-            with gr.Accordion("Advanced - Document references", open=False):
+            with gr.Accordion("Avançado - Referências do documento", open=False):
                 with gr.Row():
-                    doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
-                    source1_page = gr.Number(label="Page", scale=1)
+                    doc_source1 = gr.Textbox(label="Referência 1", lines=2, container=True, scale=20)
+                    source1_page = gr.Number(label="Página", scale=1)
                 with gr.Row():
-                    doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
-                    source2_page = gr.Number(label="Page", scale=1)
+                    doc_source2 = gr.Textbox(label="Referência 2", lines=2, container=True, scale=20)
+                    source2_page = gr.Number(label="Página", scale=1)
                 with gr.Row():
-                    doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
-                    source3_page = gr.Number(label="Page", scale=1)
+                    doc_source3 = gr.Textbox(label="Referência 3", lines=2, container=True, scale=20)
+                    source3_page = gr.Number(label="Página", scale=1)
             with gr.Row():
-                msg = gr.Textbox(placeholder="Type message (e.g. 'What is this document about?')", container=True)
+                msg = gr.Textbox(placeholder="Digite a mensagem (exemplo: 'Sobre o que é este documento?')", container=True)
             with gr.Row():
-                submit_btn = gr.Button("Submit message")
-                clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
+                submit_btn = gr.Button("Enviar mensagem")
+                clear_btn = gr.ClearButton([msg, chatbot], value="Limpar conversa")
             
-        # Preprocessing events
-        db_btn.click(lambda: ("Vector DB Initialized", "Collection Name", "DB Progress"), \
+        # Eventos de pré-processamento
+        #upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document])
+        db_btn.click(initialize_database, \
             inputs=[document, slider_chunk_size, slider_chunk_overlap], \
             outputs=[vector_db, collection_name, db_progress])
-        qachain_btn.click(lambda: ("QA Chain Initialized", "LLM 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
-        msg.submit(lambda: ("QA Chain", "Message", "Chatbot", "Doc Source 1", 1, "Doc Source 2", 2, "Doc Source 3", 3), \
+        # Eventos do Chatbot
+        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], \
             queue=False)
-        submit_btn.click(lambda: ("QA Chain", "Message", "Chatbot", "Doc Source 1", 1, "Doc Source 2", 2, "Doc Source 3", 3), \
+        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], \
             queue=False)
@@ -374,5 +279,6 @@ def demo():
             queue=False)
     demo.queue().launch(debug=True)
 
+
 if __name__ == "__main__":
-    demo()
+    demo()