app.py

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

model_id = "MahiH/dialogpt-finetuned-chatbot"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
model.eval()

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

def chat(prompt):
    input_text = f"Human: {prompt}\nAssistant: "
    inputs = tokenizer(input_text, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=100,
            do_sample=True,
            top_p=0.95,
            temperature=0.8,
            pad_token_id=tokenizer.eos_token_id
        )
    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return decoded.split("Assistant:")[-1].strip()

# Create Interface
demo = gr.Interface(fn=chat, inputs="text", outputs="text")

# Enable queuing to support the REST API endpoint
demo.queue()

# Launch (no extra args needed)
demo.launch()