Create app.py

app.py

@@ -0,0 +1,326 @@
+import gradio as gr
+import os
+from pathlib import Path
+import re
+from unidecode import unidecode
+import chromadb
+from langchain_community.vectorstores import FAISS, ScaNN, Milvus
+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
+
+api_token = os.getenv("HF_TOKEN")
+
+list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.3"]
+list_llm_simple = [os.path.basename(llm) for llm in list_llm]
+
+# Load PDF document and create doc splits
+def load_doc(list_file_path, chunk_size, chunk_overlap):
+    loaders = [PyPDFLoader(x) for x in list_file_path]
+    pages = []
+    for loader in loaders:
+        pages.extend(loader.load())
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+    doc_splits = text_splitter.split_documents(pages)
+    return doc_splits
+
+# Create vector database
+def create_db(splits, collection_name, db_type):
+    embedding = HuggingFaceEmbeddings()
+    
+    if db_type == "ChromaDB":
+        new_client = chromadb.EphemeralClient()
+        vectordb = Chroma.from_documents(
+            documents=splits,
+            embedding=embedding,
+            client=new_client,
+            collection_name=collection_name,
+        )
+    elif db_type == "FAISS":
+        vectordb = FAISS.from_documents(
+            documents=splits,
+            embedding=embedding
+        )
+    elif db_type == "ScaNN":
+        vectordb = ScaNN.from_documents(
+            documents=splits,
+            embedding=embedding
+        )
+    elif db_type == "Milvus":
+        vectordb = Milvus.from_documents(
+            documents=splits,
+            embedding=embedding,
+            collection_name=collection_name,
+        )
+    else:
+        raise ValueError(f"Unsupported vector database type: {db_type}")
+    
+    return vectordb
+
+# Initialize langchain LLM chain
+def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, initial_prompt, progress=gr.Progress()):
+    progress(0.1, desc="Initializing HF tokenizer...")
+    
+    progress(0.5, desc="Initializing HF Hub...")
+
+    llm = HuggingFaceEndpoint(
+        repo_id=llm_model,
+        huggingfacehub_api_token=api_token,
+        temperature=temperature,
+        max_new_tokens=max_tokens,
+        top_k=top_k,
+    )
+
+    progress(0.75, desc="Defining buffer memory...")
+    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",
+        memory=memory,
+        return_source_documents=True,
+        verbose=False,
+    )
+    qa_chain({"question": initial_prompt})  # Initialize with the initial prompt
+    progress(0.9, desc="Done!")
+    return qa_chain
+
+def initialize_llm_no_doc(llm_model, temperature, max_tokens, top_k, initial_prompt, progress=gr.Progress()):
+    progress(0.1, desc="Initializing HF tokenizer...")
+    progress(0.5, desc="Initializing HF Hub...")
+    llm = HuggingFaceEndpoint(
+        repo_id=llm_model,
+        huggingfacehub_api_token=api_token,
+        temperature=temperature,
+        max_new_tokens=max_tokens,
+        top_k=top_k,
+    )
+    progress(0.75, desc="Defining buffer memory...")
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key='answer',
+        return_messages=True
+    )
+    conversation_chain = ConversationChain(llm=llm, memory=memory, verbose=False)
+    conversation_chain({"question": initial_prompt})
+    progress(0.9, desc="Done!")
+    return conversation_chain
+
+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
+
+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 "Helpful Answer:" in response_answer:
+        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
+
+def conversation_no_doc(llm, message, history):
+    formatted_chat_history = format_chat_history(message, history)
+    response = llm({"question": message, "chat_history": formatted_chat_history})
+    response_answer = response["answer"]
+    new_history = history + [(message, response_answer)]
+    return llm, gr.update(value=""), new_history
+
+def upload_file(file_obj):
+    list_file_path = []
+    for file in file_obj:
+        list_file_path.append(file.name)
+    return list_file_path
+
+def initialize_database(list_file_obj, chunk_size, chunk_overlap, db_type, 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, db_type)
+    progress(0.9, desc="Done!")
+    return vector_db, collection_name, "Complete!"
+
+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'
+    print('Filepath: ', filepath)
+    print('Collection name: ', collection_name)
+    return collection_name
+
+def demo():
+    with gr.Blocks(theme="base") as demo:
+        vector_db = gr.State()
+        qa_chain = gr.State()
+        collection_name = gr.State()
+        initial_prompt = gr.State("")
+        llm_no_doc = gr.State()
+
+        gr.Markdown(
+        """<center><h2>lucIAna</center></h2>
+        <h3>Olá, sou a 2. versão</h3>""")
+        gr.Markdown(
+        """<b>Note:</b> Esta é a lucIAna, primeira Versão da IA para seus PDF documentos. 
+        Este chatbot leva em consideração perguntas anteriores ao gerar respostas (por meio de memória conversacional) e inclui referências a documentos para fins de clareza.
+        """)
+
+        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)")
+        
+        with gr.Tab("Step 2 - Process document"):
+            with gr.Row():
+                db_type_radio = gr.Radio(["ChromaDB", "FAISS", "ScaNN", "Milvus"], label="Vector database type", value="ChromaDB", type="value", 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("Step 3 - Set Initial Prompt"):
+            with gr.Row():
+                prompt_input = gr.Textbox(label="Initial Prompt", lines=5, value="Você é um advogado sênior, onde seu papel é analisar e trazer as informações sem inventar, dando a sua melhor opinião sempre trazendo contexto e referência. Aprenda o que é jurisprudência.")
+            with gr.Row():
+                set_prompt_btn = gr.Button("Set Prompt")
+
+        with gr.Tab("Step 4 - 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 5 - Chatbot with document"):
+            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("Step 6 - Chatbot without document"):
+            with gr.Row():
+                llm_no_doc_btn = gr.Radio(list_llm_simple, 
+                    label="LLM models", value=list_llm_simple[0], type="index", info="Choose your LLM model for chatbot without document")
+            with gr.Accordion("Advanced options - LLM model", open=False):
+                with gr.Row():
+                    slider_temperature_no_doc = 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_no_doc = gr.Slider(minimum=224, maximum=4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
+                with gr.Row():
+                    slider_topk_no_doc = 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_no_doc_progress = gr.Textbox(value="None", label="LLM initialization for chatbot without document")
+            with gr.Row():
+                llm_no_doc_init_btn = gr.Button("Initialize LLM for Chatbot without document")
+            chatbot_no_doc = gr.Chatbot(height=300)
+            with gr.Row():
+                msg_no_doc = gr.Textbox(placeholder="Type message to chat with lucIAna", container=True)
+            with gr.Row():
+                submit_btn_no_doc = gr.Button("Submit message")
+                clear_btn_no_doc = gr.ClearButton([msg_no_doc, chatbot_no_doc], value="Clear conversation")
+
+        # Preprocessing events
+        db_btn.click(initialize_database, 
+            inputs=[document, slider_chunk_size, slider_chunk_overlap, db_type_radio], 
+            outputs=[vector_db, collection_name, db_progress])
+        set_prompt_btn.click(lambda prompt: gr.update(value=prompt), 
+                             inputs=prompt_input, 
+                             outputs=initial_prompt)
+        qachain_btn.click(initialize_llmchain, 
+            inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db, initial_prompt], 
+            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 with document
+        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)
+
+        # Initialize LLM without document for conversation
+        llm_no_doc_init_btn.click(initialize_llm_no_doc, 
+            inputs=[llm_no_doc_btn, slider_temperature_no_doc, slider_maxtokens_no_doc, slider_topk_no_doc, initial_prompt], 
+            outputs=[llm_no_doc, llm_no_doc_progress])
+
+        submit_btn_no_doc.click(conversation_no_doc, 
+            inputs=[llm_no_doc, msg_no_doc, chatbot_no_doc], 
+            outputs=[llm_no_doc, msg_no_doc, chatbot_no_doc], 
+            queue=False)
+        clear_btn_no_doc.click(lambda:[None,""], 
+            inputs=None, 
+            outputs=[chatbot_no_doc, msg_no_doc], 
+            queue=False)
+
+    demo.queue().launch(debug=True, share=True)
+
+if __name__ == "__main__":
+    demo()