app.py

import gradio as gr
from transformers import (T5Tokenizer, 
                          T5ForConditionalGeneration, 
                          AddedToken,
)


tokenizer = T5Tokenizer.from_pretrained(f"google/flan-t5-base")
tokenizer.add_special_tokens({"additional_special_tokens": [AddedToken("\n")]})

# load model
model_cktp = "model_checkpoint"
model = T5ForConditionalGeneration.from_pretrained(model_cktp)

def predict(input):
    input_ids = tokenizer.encode(input, return_tensors="pt")
    outputs = model.generate(input_ids, 
                            max_length=200,
                            early_stopping=True)
    response = tokenizer.decode(outputs[0], skip_special_tokens=False)
    response = response.replace("<pad>", "").replace("</s>", "")
    return response


examples = [["""Service: telecom customer service.
Customer utterance : "I'm trying to find out when my tv service will be turn back on??????"|
Extract all strictly unnecessary sequences for the service provider to process the request/issue and then classify them using relational tags."""],
["""Service: airline customer service.
Customer utterance : "I need a ticket to Boston this Saturday, my son is graduating!"|
Extract all strictly unnecessary sequences for the service provider to process the request/issue and classify them using relational tags."""]
            ]

description = """This model detects and classifies relational strategies in customer service requests, using an instruction-based approach."""

demo = gr.Interface(fn=predict, 
                    inputs="text", 
                    outputs="text",
                    title="FLAN-T5: Detect and Classify Relational Strategies",
                    examples=examples,
                    description=description)

if __name__ == "__main__":
    demo.launch()