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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
# Model Info
# model_path_local = '/Users/heykalsayid/Desktop/skill-academy/projects/ai-porto/deployment/app/model/eleutherai-finetuned'
model_path_hf = 'paacamo/EleutherAI-pythia-1b-finetuned-nvidia-faq' # model from hugging face
# set tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_path_hf)
model = AutoModelForCausalLM.from_pretrained(model_path_hf)
# set pipeline for text generation
text_generation = pipeline('text-generation',
model=model,
tokenizer=tokenizer,
device=0 if torch.cuda.is_available() else -1)
def respond_chat(message):
# prompt from model template for better response
prompt = f"###Question: {message} \n###Answer:"
# response from the model
response = text_generation(prompt, max_new_tokens=100, do_sample=True)
return response[0]['generated_text'].split('###Answer:')[1]
# start gradio interface
demo = gr.Interface(
fn=respond_chat,
inputs='text',
outputs='text',
title="NVIDIA FAQ Chatbot",
description="Ask your question about NVIDIA products and services."
)
# main function to launch the app
demo.launch(debug=True)
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