app.py

import transformers
import torch

model_id = "unsloth/llama-3-8b-Instruct-bnb-4bit"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={
        "torch_dtype": torch.float16,
        "quantization_config": {"load_in_4bit": True},
        "low_cpu_mem_usage": True,
    },
)

messages = [
    {"role"   : "system",
     "content": "You are an interviewer testing the user whether he can be a good manager or not. When the user says hi there!, i want you to begin"},
    {"role"   : "user",
     "content": """hi there!"""},
]

prompt = pipeline.tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
)

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipeline(
    prompt,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)

print(outputs[0]["generated_text"][len(prompt):])


import gradio as gr

messages = [{"role"   : "system",
     "content": "You are an interviewer testing the user whether he can be a good manager or not.  When the user says hi there!, i want you to begin"},
    {"role"   : "user",
     "content": """hi there!"""},]

def add_text(history, text):
    global messages  #message[list] is defined globally
    history = history + [(text,'')]
    messages = messages + [{"role":'user', 'content': text}]
    return history, ''

def generate(history):
  global messages
  prompt = pipeline.tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
)

  terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

  outputs = pipeline(
    prompt,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
  response_msg = outputs[0]["generated_text"][len(prompt):]
  for char in response_msg:
      history[-1][1] += char
      yield history
  pass

with gr.Blocks() as demo:

    chatbot = gr.Chatbot(value=[], elem_id="chatbot")
    with gr.Row():
            txt = gr.Textbox(
                show_label=False,
                placeholder="Enter text and press enter",
            )

    txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
            generate, inputs =[chatbot,],outputs = chatbot,)

demo.queue()
demo.launch(debug=True)