Update app.py

app.py

@@ -1,63 +1,395 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+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_huggingface 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 langchain_huggingface import HuggingFaceEndpoint
+
+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", \
+            "google/flan-t5-xxl"
+            ]
+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):
+    # 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)
+    doc_splits = text_splitter.split_documents(pages)
+    return doc_splits
+
+
+# Create vector database
+def create_db(splits, collection_name):
+    embedding = HuggingFaceEmbeddings()
+    #new_client = chromadb.EphemeralClient()
+    new_client = chromadb.PersistentClient()
+    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
+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
+    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,
+        )
+    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",
+        )
+    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,
+        )
+    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,
+        )
+    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,
+        )
+
+    progress(0.75, desc="Defining buffer memory...")
+    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...")
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm,
+        retriever=retriever,
+        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!")
+    return qa_chain
+
+
+# Generate collection name for vector database
+#  - Use filepath as input, ensuring unicode text
+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 = 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)
+    return collection_name
+
+
+# Initialize database
+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...")
+    collection_name = create_collection_name(list_file_path[0])
+    progress(0.25, desc="Loading document...")
+    # Load document and create splits
+    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
+    vector_db = create_db(doc_splits, collection_name)
+    progress(0.9, desc="Done!")
+    return vector_db, collection_name, "Complete!"
+
+
+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, "Complete!"
+
+
+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)
+    # 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]
+    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
+
+
+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
+
+
+def demo():
+    with gr.Blocks(theme="base") 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)")
+                # upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1)
+
+        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("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")
+
+        # Preprocessing events
+        # 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(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.launch()
+    demo()