Update app.py

app.py

@@ -1,136 +1,215 @@
-# Projeto 6 - Construção e Deploy de Interface Web Para Agente de Conversação e Busca com LangChain e LLM
-
-# Framework para criação de aplicações web
-import streamlit as st  
-
-# Para criação e execução de agentes conversacionais
-from langchain.agents import ConversationalChatAgent, AgentExecutor  
-
-# Callback para interação com a interface do Streamlit
-from langchain_community.callbacks import StreamlitCallbackHandler  
-
-# Integração com o modelo de linguagem da OpenAI
-from langchain_openai import ChatOpenAI  
-
-# Memória para armazenar o histórico de conversa
-from langchain.memory import ConversationBufferMemory  
-
-# Histórico de mensagens para o Streamlit
-from langchain_community.chat_message_histories import StreamlitChatMessageHistory 
-
-# Ferramenta de busca DuckDuckGo para o agente 
-from langchain_community.tools import DuckDuckGoSearchRun  
-
-# Pacote para manipulação dos dados em formato JSON
-import json
-
-# Pacote para requisições
-import requests
-
-# Remove warnings
-import warnings
-warnings.filterwarnings('ignore')
-
-# Configuração do título da página
-st.set_page_config(page_title = "DSA")
-
-# Criação de colunas para layout da página
-# Define a proporção das colunas
-col1, col4 = st.columns([4, 1])  
-
-# Configuração da primeira coluna para exibir o título do projeto
-with col1:
-    st.title("Deploy UI Web para Chatbot com LangChain e LLM")
-
-# Campo para entrada da chave de API da OpenAI
-openai_api_key = st.sidebar.text_input("OpenAI API Key", type = "password")
-
-# Inicialização do histórico de mensagens
-# https://python.langchain.com/docs/integrations/memory/streamlit_chat_message_history/
-msgs = StreamlitChatMessageHistory()
-
-# Configuração da memória do chat
-# https://python.langchain.com/docs/modules/memory/types/buffer/
-memory = ConversationBufferMemory(chat_memory = msgs, 
-                                  return_messages = True, 
-                                  memory_key = "chat_history", 
-                                  output_key = "output")
-
-# Verificação para limpar o histórico de mensagens ou iniciar a conversa
-if len(msgs.messages) == 0 or st.sidebar.button("Reset"):
-    msgs.clear()
-    msgs.add_ai_message("Como eu posso ajudar você?")
-    st.session_state.steps = {}
-
-# Definição de avatares para os participantes da conversa
-avatars = {"human": "user", "ai": "assistant"}
-
-# Loop para exibir mensagens no chat
-# Itera sobre cada mensagem no histórico de mensagens
-for idx, msg in enumerate(msgs.messages):  
-
-    # Cria uma mensagem no chat com o avatar correspondente ao tipo de usuário (humano ou IA)
-    with st.chat_message(avatars[msg.type]):  
-
-        # Itera sobre os passos armazenados para cada mensagem, se houver
-        for step in st.session_state.steps.get(str(idx), []):  
-
-            # Se o passo atual indica uma exceção, pula para o próximo passo
-            if step[0].tool == "_Exception":  
-                continue
-
-            # Cria um expander para cada ferramenta usada na resposta, mostrando o input
-            with st.expander(f"✅ **{step[0].tool}**: {step[0].tool_input}"): 
-
-                # Exibe o log de execução da ferramenta 
-                st.write(step[0].log)  
-
-                # Exibe o resultado da execução da ferramenta
-                st.write(f"**{step[1]}**")  
-
-        # Exibe o conteúdo da mensagem no chat
-        st.write(msg.content)  
-
-# Campo de entrada para novas mensagens do usuário
-if prompt := st.chat_input(placeholder = "Digite uma pergunta para começar!"):
-    st.chat_message("user").write(prompt)
-
-    # Verificação da chave de API
-    if not openai_api_key:
-        st.info("Adicione sua OpenAI API key para continuar.")
-        st.stop()
-
-    # Configuração do modelo de linguagem da OpenAI
-    # https://python.langchain.com/docs/integrations/chat/openai/
-    llm_dsa = ChatOpenAI(openai_api_key = openai_api_key, streaming = True)
-    
-    # Configuração da ferramenta de busca do agente
-    # https://api.python.langchain.com/en/latest/tools/langchain_community.tools.ddg_search.tool.DuckDuckGoSearchRun.html
-    mecanismo_busca = [DuckDuckGoSearchRun(name = "Search")]
+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_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.llms import HuggingFaceEndpoint
+import torch
+
+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]
+
+# Load and split PDF document
+def load_doc(list_file_path):
+    # 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 = 1024, 
+        chunk_overlap = 64 
+    )  
+    doc_splits = text_splitter.split_documents(pages)
+    return doc_splits
+
+# Create vector database
+def create_db(splits):
+    embeddings = HuggingFaceEmbeddings()
+    vectordb = FAISS.from_documents(splits, embeddings)
+    return vectordb
+
+
+# Initialize langchain LLM chain
+def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
+    if llm_model == "meta-llama/Meta-Llama-3-8B-Instruct":
+        llm = HuggingFaceEndpoint(
+            repo_id=llm_model,
+            huggingfacehub_api_token = api_token,
+            temperature = temperature,
+            max_new_tokens = max_tokens,
+            top_k = top_k,
+        )
+    else:
+        llm = HuggingFaceEndpoint(
+            huggingfacehub_api_token = api_token,
+            repo_id=llm_model, 
+            temperature = temperature,
+            max_new_tokens = max_tokens,
+            top_k = top_k,
+        )
     
-    # Criação do agente conversacional com a ferramenta de busca
-    # https://api.python.langchain.com/en/latest/agents/langchain.agents.conversational_chat.base.ConversationalChatAgent.html
-    chat_dsa_agent = ConversationalChatAgent.from_llm_and_tools(llm = llm_dsa, tools = mecanismo_busca)
+    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
+
+# Initialize database
+def initialize_database(list_file_obj, progress=gr.Progress()):
+    # Create a list of documents (when valid)
+    list_file_path = [x.name for x in list_file_obj if x is not None]
+    # Load document and create splits
+    doc_splits = load_doc(list_file_path)
+    # Create or load vector database
+    vector_db = create_db(doc_splits)
+    return vector_db, "Database created!"
+
+# Initialize 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)
+    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
+    return qa_chain, "QA chain initialized. Chatbot is ready!"
+
+
+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
     
-    # Executor para o agente, incluindo memória e tratamento de erros
-    # https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html
-    executor = AgentExecutor.from_agent_and_tools(agent = chat_dsa_agent,
-                                                  tools = mecanismo_busca,
-                                                  memory = memory,
-                                                  return_intermediate_steps = True,
-                                                  handle_parsing_errors = True)
 
-    # Exibição da resposta do assistente
-    with st.chat_message("assistant"):
-
-        # Callback para o Streamlit
-        # https://api.python.langchain.com/en/latest/callbacks/langchain_community.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html
-        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts = False)  
-        response = executor(prompt, callbacks = [st_cb])
-        st.write(response["output"])
-
-        # Armazenamento dos passos intermediários
-        st.session_state.steps[str(len(msgs.messages) - 1)] = response["intermediate_steps"]  
-
-# Fim
+def conversation(qa_chain, message, history):
+    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]
+    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
+    # Append user message and response to chat history
+    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 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)
+    return list_file_path
+
+
+def demo():
+    # with gr.Blocks(theme=gr.themes.Default(primary_hue="sky")) as 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. The app is hosted on Hugging Face Hub for the sole purpose of demonstration. \
+        <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>")
+                with gr.Row():
+                    document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
+                with gr.Row():
+                    db_btn = gr.Button("Create vector database")
+                with gr.Row():
+                        db_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Vector database status", 
+                gr.Markdown("<style>body { font-size: 16px; }</style><b>Select Large Language Model (LLM) and input parameters</b>")
+                with gr.Row():
+                    llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value = list_llm_simple[0], type="index") # info="Select LLM", show_label=False
+                with gr.Row():
+                    with gr.Accordion("LLM input parameters", open=False):
+                        with gr.Row():
+                            slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.5, step=0.1, label="Temperature", info="Controls randomness in token generation", interactive=True)
+                        with gr.Row():
+                            slider_maxtokens = gr.Slider(minimum = 128, maximum = 9192, value=4096, step=128, label="Max New Tokens", info="Maximum number of tokens to be generated",interactive=True)
+                        with gr.Row():
+                                slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k", info="Number of tokens to select the next token from", interactive=True)
+                with gr.Row():
+                    qachain_btn = gr.Button("Initialize Question Answering Chatbot")
+                with gr.Row():
+                        llm_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Chatbot status", 
+
+            with gr.Column(scale = 200):
+                gr.Markdown("<b>Step 2 - Chat with your Document</b>")
+                chatbot = gr.Chatbot(height=505)
+                with gr.Accordion("Relevent context from the source document", 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)
+                    with gr.Row():
+                        doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
+                        source2_page = gr.Number(label="Page", 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)
+                with gr.Row():
+                    msg = gr.Textbox(placeholder="Ask a question", container=True)
+                with gr.Row():
+                    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
+        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(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)
+        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()