app.py

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

# Load model & tokenizer
model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
print("Loading model...")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
print("Model loaded.")

# Global state
chat_history_ids = None
chat_step = 0

# Chat function
def respond(message, history=[]):
    global chat_history_ids, chat_step

    # Encode user input
    new_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors="pt")

    # Append to chat history
    bot_input_ids = (
        torch.cat([chat_history_ids, new_input_ids], dim=-1)
        if chat_step > 0 else new_input_ids
    )

    # Generate response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_new_tokens=500,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True,
        top_k=50,
        top_p=0.95,
        temperature=0.8,
    )

    # Decode only the newly generated part
    reply = tokenizer.decode(
        chat_history_ids[:, bot_input_ids.shape[-1]:][0],
        skip_special_tokens=True
    )

    chat_step += 1
    return reply

# Launch Gradio interface
gr.ChatInterface(fn=respond, title="🧠 SmolLM Chatbot").launch(share=True)