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# Gradio Chatbot with Gemma-3-1B-IT
from gradio import Interface, Chatbot
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer
model_name = 'google/gemma-3-1b-it'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Chatbot handler
def respond(message, chat_history):
inputs = tokenizer(message, return_tensors='pt')
outputs = model.generate(**inputs, max_new_tokens=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Create interface
chatbot = Chatbot()
iface = Interface(fn=respond, inputs=chatbot, outputs=chatbot)
# Launch app
if __name__ == '__main__':
iface.launch() |