app.py

import gradio as gr
from transformers import pipeline

import warnings
import logging
warnings.simplefilter('ignore')
logging.disable(logging.WARNING)

model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment"


def predict(image):
    cap = pipeline('image-to-text')
    genereated_dict = cap(image)

    final = str(genereated_dict)
    def sentiment_analysis(phrase):
      pipe = pipeline('text-classification')
      sentiment = pipe(phrase)
      return str(senti)
    senti = sentiment_analysis(final)
    output = final[20:-2]
    return output


input = gr.inputs.Image(
    label="Upload your Image and wait for 8-12 seconds!", type='pil', optional=False)
output = gr.outputs.Textbox(label="Captions")

title = "Content ModX UI "

interface = gr.Interface(
    fn=predict,
    inputs=input,
    theme="grass",
    outputs= output,
    title=title,

)
interface.launch()