app.py

import gradio as ui
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread

# Model PALING KECIL BANGET & PALING KENCENG di CPU Space
model_id = "HuggingFaceTB/SmolLM2-135M-Instruct"

print("Memuat Model Paling Kecil di Dunia (SmolLM2-135M)...")
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.float32,
    device_map="cpu"
)
print("Model Terkecil Siap Beraksi Tanpa Delay!")

def chat_smol(message, history):
    conversation = []
    
    # Masukkan riwayat chat
    for user_msg, ai_msg in history:
        conversation.append({"role": "user", "content": user_msg})
        conversation.append({"role": "assistant", "content": ai_msg})
        
    conversation.append({"role": "user", "content": message})
    
    input_ids = tokenizer.apply_chat_template(
        conversation, 
        tokenize=True, 
        add_generation_prompt=True, 
        return_tensors="pt"
    ).to("cpu")
    
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    
    generation_kwargs = dict(
        input_ids=input_ids,
        streamer=streamer,
        max_new_tokens=250, # Dibatasi biar makin instan jawabannya
        temperature=0.6,
        top_p=0.9
    )
    
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    
    partial_text = ""
    for new_text in streamer:
        partial_text += new_text
        yield partial_text

# Tampilan UI Chatbot Gradio
demo = ui.ChatInterface(
    fn=chat_smol,
    title="⚡ Ultra Micro Chatbot (SmolLM2)",
    description="Menggunakan model 135M Parameter. Ini adalah spek paling ringan, dijamin langsung merespon secepat kilat tanpa loading lama!"
)

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