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| import streamlit as st | |
| from PIL import Image | |
| import pandas as pd | |
| import numpy as np | |
| from invoice import extract_data, extract_tables, INVOICE | |
| def process_image(lang, to_be_extracted, input_image): | |
| data = extract_data(lang, to_be_extracted, input_image) | |
| return data | |
| def main(): | |
| st.title("Image Processing App") | |
| st.write("Upload an image and click the 'Process Image' button to process it.") | |
| uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png","webp"]) | |
| if uploaded_image is not None: | |
| # Display the uploaded image | |
| st.image(uploaded_image, caption="Uploaded Image", use_column_width=True) | |
| lang = st.selectbox("Select Language", ["french", "english", "arabic"]) | |
| pil_image = Image.open(uploaded_image).convert('RGB') | |
| numpy_image = np.array(pil_image) | |
| st.write("Add elements to extract:") | |
| extract_input = st.text_input("Add elements") | |
| extract_list = st.session_state.get("extract_list", INVOICE) | |
| if extract_input: | |
| extract_list.append(extract_input.strip()) | |
| st.session_state["extract_list"] = extract_list | |
| # Display the extract list as chips | |
| st.write("Elements to extract:") | |
| for item in extract_list: | |
| st.write(f"`{item}`", unsafe_allow_html=True) | |
| if st.button("Extract information"): | |
| image_info = process_image(lang, extract_list, numpy_image) | |
| df = pd.DataFrame(list(image_info.items()), columns=["Field", "Value"]) | |
| st.write("Extracted information:") | |
| st.dataframe(df) | |
| if st.button("Extract Tables"): | |
| csv = extract_tables(lang, numpy_image) | |
| st.download_button(label="Download CSV", data=csv, file_name='data.csv', mime='text/csv') | |
| else: | |
| st.session_state['extract_list'] = INVOICE | |
| if __name__ == "__main__": | |
| main() | |