app.py

import streamlit as st
import cv2
import numpy as np
from PIL import Image
import io


title_and_description = """
# PDF to Word and Word to PDF converter
Created by [@pralayasimha](https://pralayasimha.in)
"""

# Function to convert PIL image to OpenCV format
def pil_to_cv(image):
    return cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)

# Function to process the image and return edges
def process_image(image):
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blurred_image = cv2.GaussianBlur(gray_image, (3, 3), 0)
    edges = cv2.Canny(blurred_image, threshold1=30, threshold2=100)
    return edges

# Function to convert edges to a simple SVG - this is a very basic example
def edges_to_svg(edges):
    # This is a placeholder for a real conversion process
    svg_data = "<svg width='100%' height='100%' xmlns='http://www.w3.org/2000/svg'>"
    # Simple example: draw a line for each edge point (not efficient or accurate)
    for y in range(edges.shape[0]):
        for x in range(edges.shape[1]):
            if edges[y, x] != 0:  # If edge is detected
                svg_data += f"<circle cx='{x}' cy='{y}' r='0.5' fill='black' />"
    svg_data += "</svg>"
    return svg_data

# Streamlit UI
st.title('Image to SVG Converter')

uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
    image = Image.open(uploaded_file)
    st.image(image, caption='Uploaded Image', use_column_width=True)
    st.write("Processing...")

    cv_image = pil_to_cv(image)
    edges = process_image(cv_image)

    st.image(edges, caption='Edge Detection Result', use_column_width=True)
    
    svg_result = edges_to_svg(edges)
    st.download_button(label="Download SVG",
                       data=svg_result,
                       file_name="result.svg",
                       mime="image/svg+xml")