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import torch
import re
import warnings
import sys
import os
from transformers import AutoTokenizer, AutoModelForCausalLM
# Suppress all warnings including asyncio
warnings.filterwarnings("ignore")
os.environ['PYTHONWARNINGS'] = 'ignore'
# Redirect stderr to suppress asyncio exceptions
class SuppressStderr:
def __enter__(self):
self._original_stderr = sys.stderr
sys.stderr = open(os.devnull, 'w')
return self
def __exit__(self, exc_type, exc_val, exc_tb):
sys.stderr.close()
sys.stderr = self._original_stderr
# ======================
# Load model
# ======================
MODEL_ID = "google/gemma-3-270m"
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
print("Loading model...")
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.float32,
device_map="cpu"
)
print("Model loaded successfully!")
# ======================
# Clean output
# ======================
def clean_output(text):
text = text.strip()
# Remove repeated patterns
text = re.sub(r'(.{10,}?)\1+', r'\1', text)
# Find first complete sentence
sentences = re.split(r'[.!?]\s+', text)
if sentences:
return sentences[0] + ('.' if not sentences[0].endswith(('.', '!', '?')) else '')
return text
# ======================
# Chat function
# ======================
def chat(message, history):
if not message or not message.strip():
return "Please enter a message."
try:
prompt = f"<bos><start_of_turn>user\n{message}\n<end_of_turn>\n<start_of_turn>model\n"
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=150,
temperature=0.7,
top_p=0.9,
do_sample=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
repetition_penalty=1.2
)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract model response
if "model" in decoded:
reply = decoded.split("model")[-1].strip()
else:
reply = decoded.strip()
reply = clean_output(reply)
return reply if reply else "I couldn't generate a response. Please try again."
except Exception as e:
return f"Error generating response: {str(e)}"
# ======================
# UI with proper examples
# ======================
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🤖 Gemma3 270M Cloud Chat")
gr.Markdown("Gemma3 270M running on Hugging Face Spaces")
chatbot = gr.Chatbot(height=400)
msg = gr.Textbox(
label="Your message",
placeholder="Type your message here...",
lines=2
)
with gr.Row():
submit = gr.Button("Send", variant="primary")
clear = gr.Button("Clear")
gr.Markdown("### Try these examples:")
with gr.Row():
example1 = gr.Button("Hi, how are you?", size="sm")
example2 = gr.Button("What is AI?", size="sm")
example3 = gr.Button("Write hello world in Python", size="sm")
# Chat interaction
def respond(message, chat_history):
bot_message = chat(message, chat_history)
chat_history.append((message, bot_message))
return "", chat_history
msg.submit(respond, [msg, chatbot], [msg, chatbot])
submit.click(respond, [msg, chatbot], [msg, chatbot])
clear.click(lambda: None, None, chatbot, queue=False)
# Example buttons
example1.click(lambda: "Hi, how are you?", None, msg)
example2.click(lambda: "What is AI?", None, msg)
example3.click(lambda: "Write hello world in Python", None, msg)
if __name__ == "__main__":
import atexit
# Clean exit handler
def cleanup():
try:
import asyncio
loop = asyncio.get_event_loop()
if loop.is_running():
loop.stop()
except:
pass
atexit.register(cleanup)
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
quiet=True # Suppress Gradio startup messages
) |