app.py

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

MODEL_ID = "sakthi54321/power_NLP"

# Load model & tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto"
)

# Build pipeline (❌ no device arg here!)
generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer
)

# Chat function
def chat_fn(message, history):
    outputs = generator(
        message,
        max_new_tokens=1000,
        temperature=0.7,
        top_p=0.9,
        do_sample=True
    )
    reply = outputs[0]["generated_text"]
    return reply

# Gradio Chat UI
demo = gr.ChatInterface(
    fn=chat_fn,
    title="Power NLP - Qwen 0.5 Finetuned",
    description="Chat with my fine-tuned Qwen 0.5 model!"
)

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