app.py

import streamlit as st
# pipelines will help to load the model from huggingface
from transformers import pipeline
# load_dotenv(find_dotenv())
st.title("Image to text generation App 🕵️‍♂️")  

uploaded_file = st.file_uploader("Choose a file..",type= ['png', 'jpg'])
if uploaded_file is not None:
    up_image=uploaded_file.name
    #st.image(up_image)
    st.image(up_image,caption="Uploaded Image",use_column_width=True)
    image_to_text=pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
    text=image_to_text(up_image)[0]["generated_text"]
    st.subheader(text)
    # #return text