custom endpoint added

app.py

@@ -1,4 +1,6 @@
 import gradio as gr
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse
 import torch
 import torch.nn as nn
 import timm
@@ -49,7 +51,7 @@ def depth_to_normal(depth):
 # CORE PROCESSING FUNCTION
 # ===============================
 def _process_saree_core(base_image: Image.Image, pattern_image: Image.Image):
-    # (Your existing depth estimation + pattern blending logic unchanged)
+    # (Depth estimation + pattern blending logic — unchanged)
     img_pil = base_image.convert("RGB")
     img_np = np.array(img_pil)
 
@@ -160,16 +162,35 @@ def process_saree(data):
     return _process_saree_core(base_image, pattern_image)
 
 # ===============================
-# GRADIO INTERFACE
+# GRADIO + FASTAPI APP
 # ===============================
-iface = gr.Interface(
+gradio_iface = gr.Interface(
     fn=process_saree,
     inputs=gr.Dataframe(headers=["Base Blob", "Pattern Blob"], type="array"),
     outputs=gr.Image(type="pil", label="Final Saree Output"),
-    title="Saree Depth + Pattern Draping (Blob API Compatible)",
-    description="Send image blobs (bytes or base64) as array [base, pattern] or use UI for testing."
+    title="Saree Depth + Pattern Draping",
+    description="Blob or base64 API compatible"
 )
 
+app = FastAPI()
+
+# Mount Gradio UI at root
+app = gr.mount_gradio_app(app, gradio_iface, path="/")
+
+# Custom named API endpoint
+@app.post("/predict-saree")
+async def predict_saree(request: Request):
+    body = await request.json()
+    result_img = process_saree(body["data"])
+
+    # Convert output image to base64 PNG
+    buf = BytesIO()
+    result_img.save(buf, format="PNG")
+    base64_img = base64.b64encode(buf.getvalue()).decode("utf-8")
+
+    return JSONResponse(content={"image_base64": base64_img})
+
+# Run (Hugging Face will call uvicorn automatically)
 if __name__ == "__main__":
-    # Enable CORS for Vite
-    iface.launch(server_name="0.0.0.0", share=True)
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)