Update app.py

app.py

@@ -1,28 +1,18 @@
-import gradio as gr
+from flask import Flask, request, jsonify, send_file
+from flask_cors import CORS
 import vtracer
-import os
-from fastapi import FastAPI, File, UploadFile, Form, HTTPException
-from fastapi.responses import JSONResponse
-from fastapi.middleware.cors import CORSMiddleware
 from PIL import Image
 import io
+import os
 import logging
+import gradio as gr
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Initialize FastAPI app
-app = FastAPI()
-
-# Configure CORS to allow requests from Figma plugin
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["https://www.figma.com", "*"],  # Allow Figma and local testing
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
+app = Flask(__name__)
+CORS(app, resources={r"/convert": {"origins": ["https://www.figma.com", "*"]}})
 
 # VTracer conversion function
 def convert_to_vector(
@@ -72,7 +62,7 @@ def convert_to_vector(
         return svg_content
     except Exception as e:
         logger.error(f"Error in convert_to_vector: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Conversion failed: {str(e)}")
+        raise Exception(f"Conversion failed: {str(e)}")
     finally:
         # Clean up temporary files
         for path in [input_path, output_path]:
@@ -83,27 +73,30 @@ def convert_to_vector(
                 except Exception as e:
                     logger.warning(f"Failed to remove {path}: {str(e)}")
 
-# FastAPI endpoint for vector conversion
-@app.post("/convert")
-async def convert_image(
-    file: UploadFile = File(...),
-    colormode: str = Form("color"),
-    hierarchical: str = Form("stacked"),
-    mode: str = Form("spline"),
-    filter_speckle: int = Form(4),
-    color_precision: int = Form(6),
-    layer_difference: int = Form(16),
-    corner_threshold: int = Form(60),
-    length_threshold: float = Form(4.0),
-    max_iterations: int = Form(10),
-    splice_threshold: int = Form(45),
-    path_precision: int = Form(3)
-):
+# Flask endpoint for vector conversion
+@app.route('/convert', methods=['POST'])
+def convert_image():
     try:
+        # Handle image upload
+        if 'file' not in request.files:
+            return jsonify({'error': 'No image file provided'}), 400
+        file = request.files['file']
+        image = Image.open(file).convert('RGB')
+
+        # Get parameters (with defaults)
+        colormode = request.form.get('colormode', 'color')
+        hierarchical = request.form.get('hierarchical', 'stacked')
+        mode = request.form.get('mode', 'spline')
+        filter_speckle = int(request.form.get('filter_speckle', 4))
+        color_precision = int(request.form.get('color_precision', 6))
+        layer_difference = int(request.form.get('layer_difference', 16))
+        corner_threshold = int(request.form.get('corner_threshold', 60))
+        length_threshold = float(request.form.get('length_threshold', 4.0))
+        max_iterations = int(request.form.get('max_iterations', 10))
+        splice_threshold = int(request.form.get('splice_threshold', 45))
+        path_precision = int(request.form.get('path_precision', 3))
+
         logger.info("Received request to /convert")
-        # Read the uploaded image
-        image_data = await file.read()
-        image = Image.open(io.BytesIO(image_data))
 
         # Convert to SVG
         svg_content = convert_to_vector(
@@ -116,27 +109,27 @@ async def convert_image(
             layer_difference=layer_difference,
             corner_threshold=corner_threshold,
             length_threshold=length_threshold,
-            max_iterations=max_iterations,  # Fixed syntax error
+            max_iterations=max_iterations,
             splice_threshold=splice_threshold,
             path_precision=path_precision
         )
 
-        return JSONResponse(content={"svg": svg_content})
+        # Return SVG as JSON
+        return jsonify({'svg': svg_content})
     except Exception as e:
         logger.error(f"Error in convert_image: {str(e)}")
-        return JSONResponse(content={"error": str(e)}, status_code=500)
+        return jsonify({'error': str(e)}), 500
 
 # Health check endpoint
-@app.get("/")
-async def health_check():
+@app.route('/')
+def health_check():
     logger.info("Health check requested")
-    return {"status": "healthy"}
+    return jsonify({'status': 'Image to Vector Converter API is running'})
 
-# Gradio interface
+# Optional Gradio interface (comment out if not needed)
 def handle_color_mode(value):
     return value
 
-# Check if examples directory exists, else use empty list
 examples_dir = "examples"
 examples = [
     os.path.join(examples_dir, f) for f in ["11.jpg", "02.jpg", "03.jpg"]
@@ -159,7 +152,6 @@ css = """
 }
 """
 
-# Define the Gradio interface
 with gr.Blocks(css=css) as gradio_app:
     with gr.Column(elem_id="col-container"):
         gr.HTML("""
@@ -208,11 +200,6 @@ with gr.Blocks(css=css) as gradio_app:
     hierarchical.change(handle_color_mode, inputs=hierarchical, outputs=output_text)
     mode.change(handle_color_mode, inputs=mode, outputs=output_text)
 
-    default_values = {
-        "color_precision": 6,
-        "layer_difference": 16
-    }
-
     def clear_inputs():
         return (
             gr.Image(value=None), gr.Radio(value="color"), gr.Radio(value="stacked"),
@@ -268,10 +255,14 @@ with gr.Blocks(css=css) as gradio_app:
         outputs=[html, svg_output]
     )
 
-# Mount Gradio app to FastAPI at /gradio
+# Mount Gradio app at /gradio (optional)
 try:
     from gradio import mount_gradio_app
+    from flask import Flask
     app = mount_gradio_app(app, gradio_app, path="/gradio")
     logger.info("Gradio app mounted successfully at /gradio")
 except Exception as e:
-    logger.error(f"Failed to mount Gradio app: {str(e)}")
+    logger.error(f"Failed to mount Gradio app: {str(e)}")
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)