Updated some files

app.py

@@ -1,7 +1,82 @@
-import gradio as gr
+from flask import Flask, request, jsonify
+import cv2
+import numpy as np
+import os
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Optional: FCM (uncomment and configure if you want Python to send push directly)
+# from pyfcm import FCMNotification
+# push_service = FCMNotification(api_key="YOUR_FCM_SERVER_KEY_HERE")
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+app = Flask(__name__)
+
+# Load YuNet face detector
+model_path = "face_detection_yunet_2023mar.onnx"
+if not os.path.exists(model_path):
+    raise FileNotFoundError(f"YuNet model not found: {model_path}")
+
+face_detector = cv2.FaceDetectorYN_create(
+    model_path,
+    "",
+    (320, 320),          # Input size (can adjust)
+    0.87,                # Score threshold
+    0.3,                 # NMS threshold
+    5000                 # Top K
+)
+
+@app.route('/detect', methods=['POST'])
+def detect_faces():
+    if 'image' not in request.files:
+        return jsonify({"error": "No image file provided"}), 400
+
+    file = request.files['image']
+    filestr = file.read()
+    npimg = np.frombuffer(filestr, np.uint8)
+    img = cv2.imdecode(npimg, cv2.IMREAD_COLOR)
+
+    if img is None:
+        return jsonify({"error": "Invalid image"}), 400
+
+    height, width, _ = img.shape
+    face_detector.setInputSize((width, height))
+
+    _, faces = face_detector.detect(img)
+
+    result = {
+        "faces_detected": 0,
+        "faces": []
+    }
+
+    if faces is not None:
+        result["faces_detected"] = len(faces)
+        for face in faces:
+            # face: [x, y, w, h, confidence, ... landmarks]
+            x, y, w, h = list(map(int, face[:4]))
+            conf = float(face[4])
+            result["faces"].append({
+                "box": [x, y, w, h],
+                "confidence": round(conf, 3)
+                # "landmarks": [...]  # if you want eyes/nose etc.
+            })
+
+            # Optional: Draw rectangle (for debug, save image locally if needed)
+            # cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
+
+    # Optional: Send FCM if face detected
+    if result["faces_detected"] > 0:
+        try:
+            # Example with pyfcm - replace with your logic
+            # data_message = {"title": "Face Detected!", "body": f"{result['faces_detected']} face(s) seen"}
+            # push_service.notify_topic_subscribers(topic_name="uncle_alerts", data_message=data_message)
+            print("FCM would be sent here - face detected")
+        except Exception as e:
+            print(f"FCM error: {e}")
+
+    return jsonify(result)
+
+if __name__ == '__main__':
+    # For local testing
+    app.run(host='0.0.0.0', port=7860, debug=True)
+else:
+    # For gunicorn in production (Hugging Face)
+    # The port 7860 is what HF expects
+    pass

packages.txt

@@ -0,0 +1,2 @@
+libgl1-mesa-glx
+libglib2.0-0

requirements.txt

@@ -1 +1,6 @@
-gradio
+flask==3.0.3
+gunicorn==23.0.0
+opencv-python-headless==4.10.0.84   # or latest 4.x version
+numpy==1.26.4
+pyfcm==1.5.4                        # optional - for FCM from Python
+# firebase-admin==6.5.0             # alternative to pyfcm, more modern