| import streamlit as st |
| import pytesseract |
| import torch |
| from PIL import Image |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
|
|
| st.title(':blue[_SnapCode_]') |
| st.markdown("_Extract code blocks out of Screenshots and Images_") |
|
|
| with st.spinner('Code vs Natuaral language - Classification model is loading'): |
| model_id = "vishnun/codenlbert-tiny" |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForSequenceClassification.from_pretrained(model_id) |
|
|
| st.success('Model loaded') |
|
|
| 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 from which code needs to be extracted", type= ['png', 'jpeg', 'jpg']) |
|
|
| if uploaded_file is not None: |
| img = Image.open(uploaded_file) |
| ocr_list = [x for x in pytesseract.image_to_string(img).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].upper() == 'CODE': |
| idx.append(ocr_list[i]) |
|
|
|
|
| st.markdown('**Uploaded Image**') |
| st.image(img, caption='Uploaded Image') |
| st.markdown("**Retrieved Code Block**") |
| st.code(("\n").join(idx), language="python", line_numbers=False) |
