Update app.py

app.py

@@ -1,11 +1,12 @@
 import gradio as gr
 import numpy as np
-import cv2
 from ultralytics import YOLO
 import insightface
+from fastapi import FastAPI
+from fastapi.responses import JSONResponse
 
 # ----------------------------
-# Load Models (CPU mode)
+# Load models (CPU)
 # ----------------------------
 
 yolo = YOLO("yolov8n.pt")
@@ -22,14 +23,12 @@ def normalize(vec):
     return (vec / norm).tolist()
 
 
-def process_image(image):
-    image_np = np.array(image)
+def process_image_np(image_np):
     results = yolo(image_np)
     faces_output = []
 
     for r in results:
         boxes = r.boxes
-
         for box, cls, conf in zip(boxes.xyxy, boxes.cls, boxes.conf):
             if int(cls) != 0:
                 continue
@@ -71,18 +70,33 @@ def process_image(image):
     return faces_output
 
 
-iface = gr.Interface(
-    fn=process_image,
-    inputs=gr.Image(type="pil"),
-    outputs="json"
-)
+# ----------------------------------------
+# REAL FASTAPI APP (NO QUEUE)
+# ----------------------------------------
+
+app = FastAPI()
+
+@app.post("/detect")
+async def detect(file: bytes):
+    import cv2
+    import numpy as np
+
+    np_arr = np.frombuffer(file, np.uint8)
+    image_np = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+
+    if image_np is None:
+        return JSONResponse({"error": "Invalid image"}, status_code=400)
+
+    result = process_image_np(image_np)
+    return result
+
+
+# ----------------------------------------
+# Gradio UI (optional)
+# ----------------------------------------
 
-# 🔥 CRITICAL PART
-iface.queue(False)
+with gr.Blocks() as demo:
+    gr.Markdown("Face Processing API Running")
 
-iface.launch(
-    server_name="0.0.0.0",
-    server_port=7860,
-    ssr_mode=False,
-    share=False
-)
+demo.queue(False)
+demo.launch(server_name="0.0.0.0", server_port=7860)