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| import os | |
| # Redirect Streamlit config directory to a writable local folder | |
| os.environ["STREAMLIT_CONFIG_DIR"] = ".streamlit" | |
| os.makedirs(".streamlit", exist_ok=True) | |
| with open(".streamlit/config.toml", "w") as f: | |
| f.write("[browser]\ngatherUsageStats = false\n") | |
| import os | |
| # 🔧 Fix permission error by redirecting Streamlit config to a safe directory | |
| os.environ["STREAMLIT_CONFIG_DIR"] = ".streamlit" | |
| os.makedirs(".streamlit", exist_ok=True) | |
| with open(".streamlit/config.toml", "w") as f: | |
| f.write("[browser]\ngatherUsageStats = false\n") | |
| import streamlit as st | |
| from PIL import Image, ImageOps | |
| import torch | |
| from transformers import TrOCRProcessor, VisionEncoderDecoderModel | |
| # 🧠 Load model and processor | |
| def load_model(): | |
| processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") | |
| model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten") | |
| return processor, model | |
| processor, model = load_model() | |
| # 🖼️ App layout | |
| st.set_page_config(page_title="✍️ Handwritten OCR", layout="centered") | |
| st.title("🖋️ Handwritten Image to Text") | |
| st.markdown("Upload an image of **handwritten text**, and this app will extract the text using TrOCR.") | |
| # 📤 File uploader | |
| uploaded_file = st.file_uploader("Upload a handwritten image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file: | |
| image = Image.open(uploaded_file).convert("RGB") | |
| st.image(image, caption="Uploaded Image", use_column_width=True) | |
| with st.spinner("🕵️ Extracting text..."): | |
| # Preprocess and predict | |
| pixel_values = processor(images=image, return_tensors="pt").pixel_values | |
| generated_ids = model.generate(pixel_values) | |
| extracted_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| st.success("✅ Text Extracted!") | |
| st.text_area("📝 Extracted Text", extracted_text, height=200) | |