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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import torch
from threading import Thread
# Load model and tokenizer
checkpoint = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")
torch.cuda.empty_cache()
def chat_response(message, history):
print(f"Received message: {message}")
print(f"History: {history}")
messages = []
for h in history:
messages.append(h) # Each h is already a dict with 'role' and 'content'
messages.append({"role": "user", "content": message})
# Generate response
input_text = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
# Setup streamer
streamer = TextIteratorStreamer(
tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
)
# Generate with streaming
generation_kwargs = dict(
inputs=inputs,
max_new_tokens=256,
temperature=0.2,
top_p=0.9,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
streamer=streamer,
)
# Create a thread to run the generation
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
# Stream the response
partial_message = ""
for new_token in streamer:
partial_message += new_token
yield partial_message
# Create and launch the Gradio interface
demo = gr.ChatInterface(
fn=chat_response,
type="messages",
title="SmolLM2 Chatbot",
description="A chatbot powered by SmolLM2-1.7B-Instruct model",
examples=[
"What is the capital of France?",
"How does photosynthesis work?",
"Write a short poem about autumn.",
],
cache_examples=True,
)
if __name__ == "__main__":
demo.launch()
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