app.py

# app.py

import streamlit as st
from PIL import Image, ImageEnhance, ImageFilter
import easyocr
import numpy as np
from io import BytesIO
import matplotlib.pyplot as plt

# Set up the OCR reader for Nepali
reader = easyocr.Reader(['ne'])

# Function to enhance and apply scan-like effects to an image
def scan_effect(img):
    # Convert the image to grayscale
    gray_img = img.convert("L")
    # Apply contrast enhancement
    enhancer = ImageEnhance.Contrast(gray_img)
    contrast_img = enhancer.enhance(2.0)
    # Apply sharpness
    sharp_img = contrast_img.filter(ImageFilter.SHARPEN)
    return sharp_img

# Streamlit app layout
st.title("Nepali OCR Detector")

# File upload
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Display the uploaded image
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Image", use_column_width=True)
    
    # Apply scan effect to the image
    scanned_image = scan_effect(image)
    
    # Display scanned image
    st.image(scanned_image, caption="Scanned Image Effect", use_column_width=True)
    
    # Convert scanned image to bytes for OCR processing
    image_bytes = BytesIO()
    scanned_image.save(image_bytes, format="PNG")
    image_bytes = image_bytes.getvalue()
    
    # Run OCR on the scanned image
    output = reader.readtext(np.array(scanned_image))

    # Display OCR results
    st.write("### Detected Text:")
    for i, (bbox, text, confidence) in enumerate(output):
        st.write(f"{i+1}. {text} (Confidence: {confidence:.2f})")
    
    # Draw bounding boxes on the text
    fig, ax = plt.subplots()
    ax.imshow(scanned_image)
    for (bbox, text, _) in output:
        rect_x = [p[0] for p in bbox]
        rect_y = [p[1] for p in bbox]
        ax.plot(rect_x + [rect_x[0]], rect_y + [rect_y[0]], "r-", linewidth=2)
    st.pyplot(fig)
else:
    st.info("Please upload an image to start the OCR process.")