TestFlanT5 / app.py
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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()