app.py

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from fastapi import FastAPI
import gradio as gr

# Load model and tokenizer
model_name = "Vamsi/T5_Paraphrase_Paws"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Paraphrasing function
def paraphrase(text):
    input_text = "paraphrase: " + text + " </s>"
    inputs = tokenizer([input_text], return_tensors="pt", padding=True)
    outputs = model.generate(**inputs, max_length=100, num_beams=5, num_return_sequences=1)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Create Gradio interface
interface = gr.Interface(fn=paraphrase, inputs="text", outputs="text")

# Mount Gradio to FastAPI for API calls
app = FastAPI()
app = gr.mount_gradio_app(app, interface, path="/")  # now POST to / is accepted