Create app.py

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+import io
+
+# 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")
+