app.py

import gradio as gr
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("mrm8488/flan-t5-small-finetuned-samsum")

model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/flan-t5-small-finetuned-samsum")

class Input(BaseModel):
    text: str

    
def predict_sentiment(input: Input, words):
    input_ids = tokenizer(input.text, return_tensors="pt").input_ids 
    outputs = model.generate(input_ids, max_length=words)
    decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return f"{decoded_output}"

conversation = gr.Textbox(lines=2, placeholder="Conversations Here...")


iface = gr.Interface(fn=predict_sentiment, inputs=[Input(text=conversation), gr.Slider(10, 100)], outputs="text")

iface.launch()