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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()
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