app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

MODEL_NAME = "openpeerai/openpeerllm"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype="auto")
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

def chat(prompt, max_new_tokens=256, temperature=0.7):
    outputs = generator(
        prompt,
        max_new_tokens=max_new_tokens,
        temperature=temperature,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    return outputs[0]['generated_text'][len(prompt):].strip()
    MODEL_NAME = "openpeerai/openpeerllm"
iface = gr.Interface(
    fn=chat,
    inputs=[
        gr.Textbox(lines=4, label="Enter your prompt"),
        gr.Slider(16, 1024, value=256, step=16, label="Max new tokens"),
        gr.Slider(0.1, 1.5, value=0.7, step=0.05, label="Temperature")
    ],
    outputs="text",
    title="OpenPeerLLM Text Demo",
    description="Chat with OpenPeerLLM from HuggingFace!"
)

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