Update main.py

main.py

@@ -1,128 +1,98 @@
-from flask import Flask
+from flask import Flask, request, jsonify, send_from_directory
+from gradio_client import Client, file
+from flask_cors import CORS
+import os
+import traceback
 import shutil
 import base64
-from gradio_client import Client, file
-
+import asyncio
+from quart import Quart, request, jsonify, send_from_directory
+from quart_cors import cors
+import aiofiles
 
+app = Quart(__name__)
+cors(app)
 
+client = Client("kadirnar/IDM-VTON")
 
-
-
-app = Flask(__name__)
-CORS(app)
 # Directory to save uploaded and processed files
-UPLOAD_FOLDER = tempfile.mkdtemp()
-RESULT_FOLDER = tempfile.mkdtemp()
+UPLOAD_FOLDER = 'static/uploads'
+RESULT_FOLDER = 'static/results'
+if not os.path.exists(UPLOAD_FOLDER):
+    os.makedirs(UPLOAD_FOLDER)
+if not os.path.exists(RESULT_FOLDER):
+    os.makedirs(RESULT_FOLDER)
 
 app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
 app.config['RESULT_FOLDER'] = RESULT_FOLDER
 
-
-
-
-def predict_with_timeout(model_image_path, product_image_url, timeout=600):
-    result = [None]  # Mutable object to store the result
-
-    def target():
-        try:
-
-            result[0] = client.predict(
-                dict({"background": file(model_image_path), "layers": [], "composite": None}),
-                garm_img=file(product_image_url),
-                seed=42,
-                api_name="/tryon"
-            )
-
-        except Exception as e:
-
-            result[0] = str(e)
-
-    thread = threading.Thread(target=target)
-    thread.start()
-    thread.join(timeout)
-
-    if thread.is_alive():
-        return "Prediction timed out after {} seconds".format(timeout)
-
-    if isinstance(result[0], Exception):
-        return str(result[0])  # Return the error message
-    return result[0]
-
-@app.route('/')
-def index():
-
-    return {'message': 'This is a wearon API'}
-
 @app.route('/process', methods=['POST'])
-def predict():
-
+async def predict():
     try:
         # Get the product image URL from the request
-        product_image_url = request.form.get('product_image_url')
-        if not product_image_url:
-
-            return jsonify(error='No product image URL provided'), 400
+        form = await request.form
+        product_image_url = form.get('product_image_url')
         
         # Handle the uploaded model image
         if 'model_image' not in request.files:
-
             return jsonify(error='No model image file provided'), 400
 
-        model_image = request.files['model_image']
+        model_image = await request.files['model_image']
         if model_image.filename == '':
-
             return jsonify(error='No selected file'), 400
 
         # Save the uploaded file to the upload directory
         filename = os.path.join(app.config['UPLOAD_FOLDER'], model_image.filename)
-        model_image.save(filename)
+        await model_image.save(filename)
 
-        full_filename = os.path.abspath(filename)
+        base_path = os.getcwd()
+        full_filename = os.path.normpath(os.path.join(base_path, filename))
 
-        print("Product image URL:", product_image_url)
-        print("Model image path:", full_filename)
+        print("Product image = ", product_image_url)
+        print("Model image = ", full_filename)
         
-        # Perform prediction with a timeout
-        result = predict_with_timeout(full_filename, product_image_url)
-        if isinstance(result, str):
-
-            return jsonify(error=result), 500
-
-        print("Prediction result:", result)
+        # Perform prediction
+        try:
+            result = await asyncio.to_thread(client.predict,
+                dict={"background": file(full_filename), "layers": [], "composite": None},
+                garm_img=file(product_image_url),
+                garment_des="Hello!!",
+                is_checked=True,
+                is_checked_crop=False,
+                denoise_steps=30,
+                seed=42,
+                api_name="/tryon"
+            )
+        except Exception as e:
+            traceback.print_exc()
+            raise
 
-        # Check if the result contains a valid path
+        print(result)
+        # Extract the path of the first output image
         output_image_path = result[0]
-        if not os.path.exists(output_image_path):
-            return jsonify(error='Output image file not found: {}'.format(output_image_path)), 500
-
 
         # Copy the output image to the RESULT_FOLDER
         output_image_filename = os.path.basename(output_image_path)
         local_output_path = os.path.join(app.config['RESULT_FOLDER'], output_image_filename)
-        shutil.copy(output_image_path, local_output_path)
-
+        await asyncio.to_thread(shutil.copy, output_image_path, local_output_path)
 
         # Remove the uploaded file after processing
-        os.remove(full_filename)
-
+        os.remove(filename)
 
         # Encode the output image in base64
-        with open(local_output_path, "rb") as image_file:
-            encoded_image = base64.b64encode(image_file.read()).decode('utf-8')
+        async with aiofiles.open(local_output_path, "rb") as image_file:
+            encoded_image = base64.b64encode(await image_file.read()).decode('utf-8')
 
         # Return the output image in JSON format
-
         return jsonify(image=encoded_image), 200
 
     except Exception as e:
-
         traceback.print_exc()
         return jsonify(error=str(e)), 500
 
 @app.route('/uploads/<filename>')
-def uploaded_file(filename):
-
-    return send_from_directory(app.config['UPLOAD_FOLDER'], filename)
+async def uploaded_file(filename):
+    return await send_from_directory(app.config['UPLOAD_FOLDER'], filename)
 
 if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=7860)
+    app.run(host='0.0.0.0', port=5000)