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ead9956 2b90e82 ead9956 ac7f408 ead9956 2b90e82 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | import os
import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
HF_TOKEN = os.getenv("HF_TOKEN")
tokenizer = AutoTokenizer.from_pretrained("google/gemma-3-270m", token=HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained("google/gemma-3-270m", token=HF_TOKEN)
def predict(message, history):
chat_history = []
for user, bot in history:
chat_history.append(user)
chat_history.append(bot)
chat_history.append(message)
input_ids = tokenizer(chat_history, padding="longest", return_tensors="pt")['input_ids']
chat_history_ids = model.generate(
input_ids,
max_length=512,
num_return_sequences=1, # Number of response sequences to generate
early_stopping=False,
do_sample=True,
)
response = tokenizer.decode(chat_history_ids[-1], skip_special_tokens=True)
yield response
if __name__=="__main__":
gr.ChatInterface(predict).queue().launch()
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