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| import streamlit as st | |
| from http import client | |
| import os,json | |
| import pandas as pd | |
| import requests | |
| from PIL import Image | |
| from transformers import TrOCRProcessor, VisionEncoderDecoderModel | |
| st.header("Snippet level OCR") | |
| processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-printed') | |
| model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-printed') | |
| def TrOCR_predict(pixel_values, processor, model): | |
| generated_ids = model.generate(pixel_values,output_scores=True,return_dict_in_generate=True, max_length = 64) | |
| predicted_text = processor.batch_decode(generated_ids[0], skip_special_tokens=True) | |
| return predicted_text | |
| uploaded_file = st.file_uploader("Choose a file") | |
| if uploaded_file is not None: | |
| content = uploaded_file.read() | |
| st.image(uploaded_file) | |
| image = Image.open(uploaded_file) | |
| if image.mode != "RGB": # Convert the image to RGB | |
| image = image.convert("RGB") | |
| pixel_values = processor(images=image, return_tensors="pt").pixel_values | |
| predicted_text = TrOCR_predict(pixel_values, processor, model)[0] | |
| texts = predicted_text | |
| st.write(texts) |