File size: 975 Bytes
7aa1147 a66e61b 7aa1147 a66e61b 7aa1147 a66e61b 7aa1147 a66e61b 7aa1147 a66e61b 7aa1147 a66e61b 7aa1147 a66e61b |
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 |
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the mT5-small model and tokenizer
model_name = "google/mt5-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Define the chatbot function for summarization and answering questions
def chatbot(user_input):
# Tokenize the user input
inputs = tokenizer(user_input, return_tensors="pt", max_length=512, truncation=True)
# Generate a response (you can customize max_length and num_beams for different outputs)
outputs = model.generate(inputs["input_ids"], max_length=150, num_beams=2, early_stopping=True)
# Decode and return the generated text
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Set up the Gradio interface
demo = gr.Interface(fn=chatbot, inputs="text", outputs="text", title="mT5-Small Chatbot")
# Launch the app
demo.launch()
|