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import gradio as gr | |
from transformers import T5Tokenizer, T5ForConditionalGeneration | |
# Load the tokenizer and model for flan-t5 | |
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base") | |
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base") | |
# Define the chatbot function | |
def chat_with_flan(input_text): | |
# Prepare the input for the model | |
input_ids = tokenizer.encode(input_text, return_tensors="pt") | |
# Generate the response from the model | |
outputs = model.generate(input_ids, max_length=200, num_return_sequences=1) | |
# Decode and return the response | |
response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
return response | |
# Set up the Gradio interface | |
interface = gr.Interface( | |
fn=chat_with_flan, | |
inputs=gr.Textbox(label="Chat with FLAN-T5"), | |
outputs=gr.Textbox(label="FLAN-T5's Response"), | |
title="FLAN-T5 Chatbot", | |
description="This is a simple chatbot powered by the FLAN-T5 model.", | |
) | |
# Launch the Gradio app | |
interface.launch() | |