revertv

app.py

@@ -1,11 +1,25 @@
 import os
-
+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+import uvicorn
 import cv2
 import gradio as gr
 import mediapipe as mp
 import numpy as np
 from PIL import Image
 from gradio_client import Client, handle_file
+import io
+
+app = FastAPI()
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
 example_path = os.path.join(os.path.dirname(__file__), 'example')
 
@@ -132,6 +146,40 @@ def process_image(human_img_path, garm_img_path):
     return generated_image
 
 
+@app.post("/")
+async def try_on_api(human_image: UploadFile = File(...), garment_image: UploadFile = File(...)):
+    try:
+        # Read the uploaded files
+        human_content = await human_image.read()
+        garment_content = await garment_image.read()
+        
+        # Convert to PIL Image
+        human_img = Image.open(io.BytesIO(human_content))
+        garment_img = Image.open(io.BytesIO(garment_content))
+        
+        # Save temporarily to process
+        human_path = "temp_human.jpg"
+        garment_path = "temp_garment.jpg"
+        human_img.save(human_path)
+        garment_img.save(garment_path)
+        
+        # Process the images
+        result = process_image(human_path, garment_path)
+        
+        # Convert result to bytes
+        img_byte_arr = io.BytesIO()
+        result.save(img_byte_arr, format='PNG')
+        img_byte_arr = img_byte_arr.getvalue()
+        
+        # Clean up temporary files
+        os.remove(human_path)
+        os.remove(garment_path)
+        
+        return {"status": "success", "image": img_byte_arr}
+    except Exception as e:
+        return {"status": "error", "message": str(e)}
+
+# Create the Gradio interface
 image_blocks = gr.Blocks().queue()
 with image_blocks as demo:
     gr.HTML("<center><h1>Virtual Try-On</h1></center>")
@@ -160,4 +208,8 @@ with image_blocks as demo:
     # Linking the button to the processing function
     try_button.click(fn=process_image, inputs=[human_img, garm_img], outputs=image_out)
 
-image_blocks.launch(show_error=True)
+# Mount Gradio app
+app = gr.mount_gradio_app(app, demo, path="/gradio")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

@@ -4,4 +4,7 @@ opencv-contrib-python==4.11.0.86
 opencv-python==4.11.0.86
 gradio==5.23.3
 gradio_client==1.8.0
+fastapi==0.110.0
+uvicorn==0.27.1
+python-multipart==0.0.9