app.py

import streamlit as st
import pytesseract
import torch
from PIL import Image
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("usvsnsp/code-vs-nl")
model = AutoModelForSequenceClassification.from_pretrained("usvsnsp/code-vs-nl")

def classify_text(text):
    input_ids = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        logits = model(**input_ids).logits

    predicted_class_id = logits.argmax().item()
    
    return model.config.id2label[predicted_class_id]

uploaded_file = st.file_uploader("Upload Image", type= ['png', 'jpg']) 

if uploaded_file is not None:
    ocr_list = [x for x in pytesseract.image_to_string(uploaded_file).split("\n") if x != '']
    ocr_class = [classify_text(x) for x in ocr_list]
    idx = []
    for i in range(len(ocr_class)):
        if ocr_class[i] == 'Code':
            idx.append(ocr_list[i])
    

    st.text(("\n").join(idx))