app.py

import os
from typing import Optional
from threading import Thread

import torch
import gradio as gr
from langchain.llms.base import LLM
from langchain.prompts import PromptTemplate
from langchain_community.vectorstores import Pinecone
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer, pipeline


os.environ["TOKENIZERS_PARALLELISM"] = "false"

def initialize_model_and_tokenizer(model_name="mistralai/Mistral-7B-Instruct-v0.2"):   
    quantization_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.float16,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_use_double_quant=True,
    )
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        trust_remote_code=True,
        torch_dtype=torch.float16,
        device_map='auto',
        quantization_config=quantization_config
    )
    model.eval()
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    tokenizer.pad_token = tokenizer.eos_token
    return model, tokenizer

def init_chain(model, tokenizer, db, embed, temp, max_new_tokens, top_p, top_k, r_penalty):
    class CustomLLM(LLM):

        """Streamer Object"""

        streamer: Optional[TextIteratorStreamer] = None

        def _call(self, prompt, stop=None, run_manager=None) -> str:
            self.streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, Timeout=5)
            inputs = tokenizer(prompt, return_tensors="pt")
            input_ids = inputs["input_ids"].to('cuda')
            generate_kwargs = dict(
                temperature=float(temp),
                max_new_tokens=int(max_new_tokens),
                top_p=float(top_p),
                top_k=int(top_k),
                repetition_penalty=float(r_penalty),
                do_sample=True
                )
            kwargs = dict(input_ids=input_ids, streamer=self.streamer, **generate_kwargs)
            thread = Thread(target=model.generate, kwargs=kwargs)
            thread.start()
            return ""

        @property
        def _llm_type(self) -> str:
            return "custom"

    llm = CustomLLM()
    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
    questionprompt = PromptTemplate.from_template(
        """<s>[INST] 
        Use the following pieces of context to answer the question at the end. 
        If you don't know the answer, just say that you don't know, don't try to make up an answer. 
        CONTEXT: {context} 
        CHAT HISTORY: {chat_history} 
        QUESTION: {question} 
        Helpful Answer: 
        [/INST]
        """
    )
    llm_chain = ConversationalRetrievalChain.from_llm(
      llm=llm,
      retriever=db.as_retriever(search_kwargs={"k": 5}),
      memory=memory,
      condense_question_prompt=questionprompt,
    )
    
    return llm_chain, llm

index_name = "resume-demo"

queries = [["Which masters degree Dmytro Kisil has?"],
           ["Which amount of salary does Dmytro Kisil is looking for?"],
           ["How long does Dmytro Kisil looking for a job?"],
           ["Why Dmytro Kisil moved to Netherlands?"],
           ["When Dmytro Kisil left Ukraine?"],
           ["Where Dmytro Kisil live now?"],
           ["How much years of working experience in total Dmytro Kisil has?"],
           ["How fast Dmytro Kisil can start working for my company?"]]

embed = HuggingFaceBgeEmbeddings(model_name='BAAI/bge-small-en-v1.5')

db = Pinecone.from_existing_index(index_name, embed)

model, tokenizer = initialize_model_and_tokenizer(model_name="mistralai/Mistral-7B-Instruct-v0.2")

with gr.Blocks() as demo:
    with gr.Column():
        chatbot = gr.Chatbot()
        with gr.Row():
            msg = gr.Textbox(scale=9)
            submit_b = gr.Button("Submit", scale=1)
        with gr.Row():
            retry_b = gr.Button("Retry")
            undo_b = gr.Button("Undo")
            clear_b = gr.Button("Clear")
    examples = gr.Examples(queries, msg)
    with gr.Accordion("Additional options", open=False):
        temp = gr.Slider(
            label="Temperature",
            value=0.01,
            minimum=0.01,
            maximum=1.00,
            step=0.01,
            interactive=True,
            info="Higher values produce more diverse outputs",
        )
        max_new_tokens = gr.Slider(
            label="Max new tokens",
            value=1024,
            minimum=64,
            maximum=8192,
            step=64,
            interactive=True,
            info="The maximum numbers of new tokens",
        )
        top_p = gr.Slider(
            label="Top-p (nucleus sampling)",
            value=0.95,
            minimum=0.00,
            maximum=1.00,
            step=0.01,
            interactive=True,
            info="Higher values sample more low-probability tokens",
        )
        top_k = gr.Slider(
            label="Top-k",
            value=40,
            minimum=0,
            maximum=100,
            step=1,
            interactive=True,
            info="select from top 0 tokens (because zero, relies on top_p)",
        )
        r_penalty = gr.Slider(
            label="Repetition penalty",
            value=1.15,
            minimum=1.0,
            maximum=2.0,
            step=0.01,
            interactive=True,
            info="Penalize repeated tokens",
        )

    def user(user_message, history):
        return "", history + [[user_message, None]]
    
    def undo(history):
        return history[:-1].copy()

    def retry(user_message, history):
        try:
            prev_user_message = history[-1][0]
        except:
            prev_user_message = ""
        return prev_user_message, history + [[prev_user_message, None]]

    def bot(history, temp, max_new_tokens, top_p, top_k, r_penalty):
        llm_chain, llm = init_chain(model, tokenizer, db, embed, temp, max_new_tokens, top_p, top_k, r_penalty)
        llm_chain.run(question=history[-1][0])
        history[-1][1] = ""
        for character in llm.streamer:
            history[-1][1] += character
            yield history
        llm_chain, llm = init_chain(model, tokenizer, db, embed, temp, max_new_tokens, top_p, top_k, r_penalty)  
    
    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, [chatbot, temp, max_new_tokens, top_p, top_k, r_penalty], chatbot)
    submit_b.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, [chatbot, temp, max_new_tokens, top_p, top_k, r_penalty], chatbot)
    retry_b.click(retry, [msg, chatbot], [msg, chatbot], queue=False).then(bot, [chatbot, temp, max_new_tokens, top_p, top_k, r_penalty], chatbot)
    clear_b.click(lambda: None, None, chatbot, queue=False)
    undo_b.click(undo, chatbot, chatbot, queue=False)
    
demo.queue()
demo.launch(share=True)