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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() | |