app.py

import streamlit as st
from transformers import pipeline
from PIL import Image
import easyocr
# ghjklkjhgfghj
pipe = pipeline("text2text-generation", model="google/flan-t5-base")

st.title("Text Classification Model")
uploaded_file = st.file_uploader("Upload an image:")

if uploaded_file is not None:
    image = Image.open(uploaded_file)
    ocr_reader = easyocr.Reader(['en'])
    ocr_results = ocr_reader.readtext(image)
    extracted_text = " ".join([res[1] for res in ocr_results])
    st.markdown("**Extracted text:**")
    st.markdown(extracted_text)
    explanation = pipe(extracted_text, max_length=100, do_sample=True)[0]["generated_text"]
    st.markdown("**Explanation:**")
    st.markdown(explanation)

else:
    st.markdown("Please upload an image to extract text and get an explanation.")