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import requests |
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from PIL import Image |
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import streamlit as st |
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from transformers import AutoTokenizer, AutoModelForImageTextToText |
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tokenizer = AutoTokenizer.from_pretrained("microsoft/trocr-large-printed") |
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model = AutoModelForImageTextToText.from_pretrained("microsoft/trocr-large-printed") |
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st.title("Duh!") |
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url = "https://parivahan.gov.in/rcdlstatus/DispplayCaptcha?txtp_cd=1&bkgp_cd=2&noise_cd=2&gimp_cd=3&txtp_length=5&pfdrid_c=true?1429026471&pfdrid_c=true" |
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image = Image.open(requests.get(url, stream=True).raw).convert("RGB") |
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col1, col2 = st.columns(2) |
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processor = TrOCRProcessor.from_pretrained('microsoft/trocr-large-printed') |
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model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-large-printed') |
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pixel_values = processor(images=image, return_tensors="pt").pixel_values |
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generated_ids = model.generate(pixel_values) |
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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col1.image(image, use_column_width=True) |
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col2.subheader(f"Detected Text: {generated_text}") |
