Update app.py

app.py

@@ -7,9 +7,7 @@ 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
 
 from pathlib import Path
 import chromadb
@@ -18,22 +16,25 @@ from unidecode import unidecode
 from transformers import AutoTokenizer, pipeline
 import transformers
 import torch
-import tqdm 
-import accelerate
 import re
 
-# Lista de modelos gratuitos que não exigem chave de API
+# Lista de modelos 100% abertos e gratuitos
 list_llm = [
-    "mistralai/Mistral-7B-Instruct-v0.2", 
-    "mistralai/Mistral-7B-Instruct-v0.1", 
-    "google/flan-t5-xxl", 
-    "HuggingFaceH4/zephyr-7b-beta", 
-    "TinyLlama/TinyLlama-1.1B-Chat-v1.0", 
-    "microsoft/phi-2"
+    "google/flan-t5-xxl",          # Modelo para tarefas text-to-text
+    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",  # Modelo leve para diálogo
+    "microsoft/phi-2",             # Modelo para raciocínio lógico
+    "facebook/opt-1.3b",           # Modelo de geração de texto
+    "EleutherAI/gpt-neo-1.3B",     # Versão open-source do GPT-3
+    "bigscience/bloom-1b7",        # Modelo multilíngue
+    "RWKV/rwkv-4-169m-pile",       # Modelo eficiente em RAM
+    "gpt2-medium",                 # Clássico modelo de GPT-2
+    "databricks/dolly-v2-3b",      # Modelo para instruções
+    "mosaicml/mpt-7b-instruct"     # Modelo para instruções
 ]
+
 list_llm_simple = [os.path.basename(llm) for llm in list_llm]
 
-# Função para carregar o documento PDF e dividir em partes
+# Função para carregar documentos PDF
 def load_doc(list_file_path, chunk_size, chunk_overlap):
     loaders = [PyPDFLoader(x) for x in list_file_path]
     pages = []
@@ -43,29 +44,26 @@ def load_doc(list_file_path, chunk_size, chunk_overlap):
         chunk_size=chunk_size, 
         chunk_overlap=chunk_overlap
     )
-    doc_splits = text_splitter.split_documents(pages)
-    return doc_splits
+    return text_splitter.split_documents(pages)
 
-# Função para criar o banco de dados vetorial
+# Função para criar banco de dados vetorial
 def create_db(splits, collection_name):
     embedding = HuggingFaceEmbeddings()
-    new_client = chromadb.EphemeralClient()
-    vectordb = Chroma.from_documents(
+    return Chroma.from_documents(
         documents=splits,
         embedding=embedding,
-        client=new_client,
-        collection_name=collection_name,
+        client=chromadb.EphemeralClient(),
+        collection_name=collection_name
     )
-    return vectordb
 
-# Função para inicializar a cadeia de LLM
+# Função para inicializar 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...")
+    progress(0.1, desc="Carregando tokenizer...")
     
-    # Carregar o tokenizer e o pipeline do modelo
     tokenizer = AutoTokenizer.from_pretrained(llm_model)
-    progress(0.5, desc="Initializing HF pipeline...")
-    pipeline_model = transformers.pipeline(
+    
+    progress(0.4, desc="Inicializando pipeline...")
+    pipeline_obj = pipeline(
         "text-generation",
         model=llm_model,
         tokenizer=tokenizer,
@@ -74,166 +72,75 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
         max_new_tokens=max_tokens,
         do_sample=True,
         top_k=top_k,
-        temperature=temperature,
+        temperature=temperature
     )
-    llm = HuggingFacePipeline(pipeline=pipeline_model)
-
-    progress(0.75, desc="Defining buffer memory...")
+    
+    llm = HuggingFacePipeline(pipeline=pipeline_obj)
+    
+    progress(0.7, desc="Configurando memória...")
     memory = ConversationBufferMemory(
         memory_key="chat_history",
-        output_key='answer',
         return_messages=True
     )
-    retriever = vector_db.as_retriever()
-
-    progress(0.8, desc="Defining retrieval chain...")
-    qa_chain = ConversationalRetrievalChain.from_llm(
-        llm,
-        retriever=retriever,
-        chain_type="stuff", 
+    
+    progress(0.8, desc="Criando cadeia...")
+    return ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=vector_db.as_retriever(),
         memory=memory,
-        return_source_documents=True,
-        verbose=False,
+        chain_type="stuff",
+        return_source_documents=True
     )
-    progress(0.9, desc="Done!")
-    return qa_chain
-
-# Função para gerar o nome da coleção do banco de dados vetorial
-def create_collection_name(filepath):
-    collection_name = Path(filepath).stem
-    collection_name = collection_name.replace(" ", "-") 
-    collection_name = unidecode(collection_name)
-    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
-    collection_name = collection_name[:50]
-    if len(collection_name) < 3:
-        collection_name = collection_name + 'xyz'
-    if not collection_name[0].isalnum():
-        collection_name = 'A' + collection_name[1:]
-    if not collection_name[-1].isalnum():
-        collection_name = collection_name[:-1] + 'Z'
-    return collection_name
-
-# Função para inicializar o banco de dados
-def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
-    list_file_path = [x.name for x in list_file_obj if x is not None]
-    progress(0.1, desc="Creating collection name...")
-    collection_name = create_collection_name(list_file_path[0])
-    progress(0.25, desc="Loading document...")
-    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
-    progress(0.5, desc="Generating vector database...")
-    vector_db = create_db(doc_splits, collection_name)
-    progress(0.9, desc="Done!")
-    return vector_db, collection_name, "Complete!"
-
-# Função para inicializar a cadeia de QA
-def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
-    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, "Complete!"
-
-# 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}")
-    return formatted_chat_history
 
-# Função para gerar a conversa
-def conversation(qa_chain, message, history):
-    formatted_chat_history = format_chat_history(message, history)
-    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]
-    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
-
-# Função principal para rodar a interface
+# Interface Gradio
 def demo():
-    with gr.Blocks(theme="base") as demo:
+    with gr.Blocks(theme=gr.themes.Soft()) as demo:
         vector_db = gr.State()
         qa_chain = gr.State()
-        collection_name = gr.State()
-        
-        gr.Markdown(
-        """<center><h2>PDF-based chatbot</center></h2>
-        <h3>Ask any questions about your PDF documents</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 explicitely 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.
-        """)
         
-        with gr.Tab("Step 1 - Upload 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)")
+        gr.Markdown("## 🤖 Chatbot para PDFs com Modelos Gratuitos")
         
-        with gr.Tab("Step 2 - Process document"):
-            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):
-                with gr.Row():
-                    slider_chunk_size = gr.Slider(minimum=100, maximum=1000, value=600, step=20, label="Chunk size", info="Chunk size", 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)
-            with gr.Row():
-                db_progress = gr.Textbox(label="Vector database initialization", value="None")
-            with gr.Row():
-                db_btn = gr.Button("Generate vector database")
+        with gr.Tab("📤 Upload PDF"):
+            pdf_input = gr.Files(label="Selecione seus PDFs", file_types=[".pdf"])
             
-        with gr.Tab("Step 3 - Initialize QA chain"):
-            with gr.Row():
-                llm_btn = gr.Radio(list_llm_simple, label="LLM models", value=list_llm_simple[0], type="index", info="Choose your LLM model")
-            with gr.Accordion("Advanced options - LLM model", 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)
-                with gr.Row():
-                    slider_maxtokens = gr.Slider(minimum=224, maximum=4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", 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)
-            with gr.Row():
-                llm_progress = gr.Textbox(value="None", label="QA chain initialization")
-            with gr.Row():
-                qachain_btn = gr.Button("Initialize Question Answering chain")
-
-        with gr.Tab("Step 4 - Chatbot"):
-            chatbot = gr.Chatbot(height=300)
-            with gr.Accordion("Advanced - Document references", 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="Type message (e.g. 'What is this document about?')", container=True)
-            with gr.Row():
-                submit_btn = gr.Button("Submit message")
-                clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
+        with gr.Tab("⚙️ Processamento"):
+            chunk_size = gr.Slider(100, 1000, value=500, label="Tamanho dos Chunks")
+            chunk_overlap = gr.Slider(0, 200, value=50, label="Sobreposição")
+            process_btn = gr.Button("Processar PDFs")
             
-        # Eventos de pré-processamento
-        db_btn.click(initialize_database, inputs=[document, slider_chunk_size, slider_chunk_overlap], outputs=[vector_db, collection_name, 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)
-
-        # 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(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)
+        with gr.Tab("🧠 Modelo"):
+            model_selector = gr.Dropdown(list_llm_simple, label="Selecione o Modelo", value=list_llm_simple[0])
+            temperature = gr.Slider(0, 1, value=0.7, label="Criatividade")
+            load_model_btn = gr.Button("Carregar Modelo")
+            
+        with gr.Tab("💬 Chat"):
+            chatbot = gr.Chatbot(height=400)
+            msg = gr.Textbox(label="Sua mensagem")
+            clear_btn = gr.ClearButton([msg, chatbot])
+            
+        # Eventos
+        process_btn.click(
+            lambda files, cs, co: create_db(load_doc([f.name for f in files], cs, co), "docs"),
+            inputs=[pdf_input, chunk_size, chunk_overlap],
+            outputs=vector_db
+        )
+        
+        load_model_btn.click(
+            lambda model, temp: initialize_llmchain(list_llm[list_llm_simple.index(model)], temp, 512, 3, vector_db.value),
+            inputs=[model_selector, temperature],
+            outputs=qa_chain
+        )
+        
+        def respond(message, chat_history):
+            result = qa_chain.value({"question": message, "chat_history": chat_history})
+            response = result["answer"]
+            sources = "\n".join([f"📄 Página {doc.metadata['page']+1}: {doc.page_content[:50]}..." 
+                               for doc in result["source_documents"][:2]])
+            return f"{response}\n\n🔍 Fontes:\n{sources}"
+        
+        msg.submit(respond, [msg, chatbot], chatbot)
+        
+    demo.launch()
 
 if __name__ == "__main__":
     demo()