import os import gradio as gr import boto3 from botocore import UNSIGNED from botocore.client import Config import torch from huggingface_hub import AsyncInferenceClient from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, pipeline from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.llms import HuggingFaceHub from langchain.embeddings import HuggingFaceHubEmbeddings from langchain.vectorstores import Chroma from langchain.chains import RetrievalQA from langchain.prompts import ChatPromptTemplate from langchain.document_loaders import WebBaseLoader from langchain.llms.huggingface_pipeline import HuggingFacePipeline from langchain.llms import CTransformers from transformers import AutoModel from typing import Iterator MAX_MAX_NEW_TOKENS = 2048 DEFAULT_MAX_NEW_TOKENS = 1024 MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) # text_splitter = RecursiveCharacterTextSplitter(chunk_size=350, chunk_overlap=10) embeddings = HuggingFaceHubEmbeddings() model_id = "TheBloke/zephyr-7B-beta-GGUF" # model_id = "HuggingFaceH4/zephyr-7b-beta" # model_id = "meta-llama/Llama-2-7b-chat-hf" # model = AutoModelForCausalLM.from_pretrained( # model_id, # device_map="auto", # low_cpu_mem_usage=True # ) # print( "initalized model") # tokenizer = AutoTokenizer.from_pretrained(model_id) # model = AutoModelForCausalLM.from_pretrained(model_id) # model = AutoModel.from_pretrained("TheBloke/zephyr-7B-beta-GGUF") device = "cpu" # llm_model = CTransformers( # model="TheBloke/zephyr-7B-beta-GGUF", # model_type="mistral", # max_new_tokens=4384, # temperature=0.2, # repetition_penalty=1.13, # device=device # Set the device explicitly during model initialization # ) # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta") model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta") # tokenizer = AutoTokenizer.from_pretrained(model_id) # model = AutoModelForCausalLM.from_pretrained(model_id) # pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10) # hf = HuggingFacePipeline(pipeline=pipe) print( "initalized model") # tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.use_default_system_prompt = False s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED)) s3.download_file('rad-rag-demos', 'vectorstores/chroma.sqlite3', './chroma_db/chroma.sqlite3') db = Chroma(persist_directory="./chroma_db", embedding_function=embeddings) db.get() retriever = db.as_retriever() global qa qa = RetrievalQA.from_chain_type(llm=llm_model, chain_type="stuff", retriever=retriever, return_source_documents=True) def generate( message: str, chat_history: list[tuple[str, str]], system_prompt: str, max_new_tokens: int = 1024, temperature: float = 0.6, top_p: float = 0.9, top_k: int = 50, repetition_penalty: float = 1.2, ) -> Iterator[str]: conversation = [] if system_prompt: conversation.append({"role": "system", "content": system_prompt}) for user, assistant in chat_history: conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) conversation.append({"role": "user", "content": message}) input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt") if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") input_ids = input_ids.to(model.device) streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( {"input_ids": input_ids}, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=True, top_p=top_p, top_k=top_k, temperature=temperature, num_beams=1, repetition_penalty=repetition_penalty, ) t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() outputs = [] for text in streamer: outputs.append(text) yield "".join(outputs) def add_text(history, text): history = history + [(text, None)] return history, "" def bot(history): response = infer(history[-1][0]) history[-1][1] = response['result'] return history def infer(question): query = question result = qa({"query": query}) return result css=""" #col-container {max-width: 700px; margin-left: auto; margin-right: auto;} """ title = """

Chat with PDF

Upload a .PDF from your computer, click the "Load PDF to LangChain" button,
when everything is ready, you can start asking questions about the pdf ;)

""" # with gr.Blocks(css=css) as demo: # with gr.Column(elem_id="col-container"): # gr.HTML(title) # chatbot = gr.Chatbot([], elem_id="chatbot") # with gr.Row(): # question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ") # question.submit(add_text, [chatbot, question], [chatbot, question]).then( # bot, chatbot, chatbot # ) chat_interface = gr.ChatInterface( fn=generate, additional_inputs=[ gr.Textbox(label="System prompt", lines=6), gr.Slider( label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS, ), gr.Slider( label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6, ), gr.Slider( label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9, ), gr.Slider( label="Top-k", minimum=1, maximum=1000, step=1, value=50, ), gr.Slider( label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2, ), ], stop_btn=None, examples=[ ["Hello there! How are you doing?"], ["Can you explain briefly to me what is the Python programming language?"], ["Explain the plot of Cinderella in a sentence."], ["How many hours does it take a man to eat a Helicopter?"], ["Write a 100-word article on 'Benefits of Open-Source in AI research'"], ], ) with gr.Blocks(css="style.css") as demo: # gr.Markdown(DESCRIPTION) # gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button") chat_interface.render() # gr.Markdown(LICENSE) #x = 0 if __name__ == "__main__": demo.launch()