app.py

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

# ✅ Use a small model that works on CPU
MODEL_NAME = "togethercomputer/RedPajama-INCITE-3B-v1"

print("Loading model. This may take a few moments…")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).to("cpu")
print("Model loaded!")

history = []

def chat_with_airi(user_msg):
    global history
    # build conversation prompt (last 5 exchanges)
    prompt = ""
    for u, a in history[-5:]:
        prompt += f"User: {u}\nAiri: {a}\n"
    prompt += f"User: {user_msg}\nAiri:"

    inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
    with torch.no_grad():
        output = model.generate(
            **inputs,
            max_new_tokens=100,       # can adjust for longer replies
            do_sample=True,
            top_p=0.9,
            temperature=0.8,
            pad_token_id=tokenizer.eos_token_id
        )

    reply = tokenizer.decode(output[0], skip_special_tokens=True)
    reply = reply.split("Airi:", 1)[-1].strip()

    history.append([user_msg, reply])
    return history, ""

with gr.Blocks() as demo:
    gr.HTML("<h2 style='text-align:center'>Airi — Mini Chat AI</h2>")
    gr.HTML("<p style='text-align:center;color:#666;'>Small, Fast & Public Model</p>")

    chat = gr.Chatbot()
    msg = gr.Textbox(label="Talk to Airi…", placeholder="Write here…")
    msg.submit(chat_with_airi, msg, [chat, msg])

demo.launch()