app.py

import gradio as gr
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GenerationConfig

model_path = os.environ.get("HF_REPO_ID")
access_token = os.environ.get("HF_TOKEN")


tokenizer = AutoTokenizer.from_pretrained(model_path, token=access_token)

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    # load_in_8bit=use_8_bit,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=getattr(torch, "bfloat16"),
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(model_path, token=access_token,
                                             quantization_config=bnb_config,
                                             torch_dtype=torch.float16,
                                             # attn_implementation="flash_attention_2",
                                             device_map='auto')

if torch.cuda.is_available():
    device = "cuda"
else:
    device = "cpu"

def generate(
    question,
    context=None,
    temperature=0.7,
    top_p=0.7,
    top_k=40,
    num_beams=4,
    max_new_tokens=256,):
        prompt = f"### CONTEXT:\n{context}\n\n### QUESTION:\n{question}\n\n### ANSWER:"
        inputs = tokenizer(prompt, return_tensors="pt")
        input_ids = inputs["input_ids"].to(device)
        generation_config = GenerationConfig(
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            num_beams=num_beams,
        )
        # with torch.autocast("cuda"):
        with torch.no_grad():
            generation_output = model.generate(
                input_ids=input_ids,
                generation_config=generation_config,
                return_dict_in_generate=True,
                output_scores=True,
                max_new_tokens=max_new_tokens,
            )
        seq = generation_output.sequences[0]
        output = tokenizer.decode(seq)
        return output

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    context = ""
    for chat in history:
        context += f"রোগী: {chat[0]}\nথেরাপিস্ট: {chat[1]}\n"

    answer = generate(message, context, 
                      temperature=temperature,
                      top_p=top_p,
                      max_new_tokens=max_tokens).split('### ANSWER:')[1]

    if '</s>' in answer:
        answer = answer.split('</s>')[0].strip()

    return answer

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=256, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.7,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


if __name__ == "__main__":
    demo.launch()