app.py

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import os
from threading import Thread
import spaces

token = os.environ["HF_TOKEN"]
model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-7b-it", torch_dtype=torch.float16, token=token
)
tok = AutoTokenizer.from_pretrained("google/gemma-7b-it", token=token)
device = torch.device("cuda")
model = model.to(device)

with open("context.txt", "r") as f:
    # read content from the resume
    context = f.read()


def format_prompt(message):
    prompt = f"""your name is hafedh hichri and given the following prompt: 
  {message}
  you will reply directly without any extra info to the previous prompt given the following context: 
  {context}"""
    return prompt


@spaces.GPU
def chat(message, history):
    chat = []
    for item in history:
        chat.append({"role": "user", "content": item[0]})
        if item[1] is not None:
            chat.append({"role": "assistant", "content": item[1]})
    chat.append({"role": "user", "content": format_prompt(message)})
    messages = tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
    # Tokenize the messages string
    model_inputs = tok([messages], return_tensors="pt").to(device)
    streamer = TextIteratorStreamer(
        tok, timeout=10.0, skip_prompt=True, skip_special_tokens=True
    )
    generate_kwargs = dict(
        model_inputs,
        streamer=streamer,
        max_new_tokens=1024,
        do_sample=True,
        top_p=0.95,
        top_k=1000,
        temperature=0.75,
        num_beams=1,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    # Initialize an empty string to store the generated text
    partial_text = ""
    for new_text in streamer:
        # print(new_text)
        partial_text += new_text
        # Yield an empty string to cleanup the message textbox and the updated conversation history
        yield partial_text


description = """
A resume question-answering interface where a recruter can ask the user about their achievements and skills without the need to interact with them directly or the need to read a really long resume

"""
examples = [
    "what's your name?",
    "what's your email adress ?",
    "what did you study ?",
    "are you open for work?",
    "what are your skills ?",
    "what's your most recent experience ?",
]


demo = gr.ChatInterface(
    fn=chat,
    chatbot=gr.Chatbot(
        show_label=True,
        show_share_button=True,
        show_copy_button=True,
        likeable=True,
        layout="bubble",
        bubble_full_width=False,
    ),
    examples=examples,
    title="Resume QA",
    description=description,
    autofocus=False,
)
demo.launch()