Update app.py

app.py

@@ -32,86 +32,92 @@ def home():
 
 @app.route("/analyze", methods=["POST"])
 def analyze():
-    if "file" not in request.files:
-        return jsonify({"success": False, "analysis": []}), 400
-
-    file = request.files["file"]
-    file_bytes = np.frombuffer(file.read(), np.uint8)
-    img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
-    if img is None:
-        return jsonify({"success": False, "error": "Invalid image"}), 400
-
-    img_h, img_w = img.shape[:2]
-
-    # -------------------- FACE DETECTION --------------------
-    rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    result = face_mesh.process(rgb_img)
-
-    face_polygon = None
-    face_area = 0
-
-    if result.multi_face_landmarks:
-        for landmarks in result.multi_face_landmarks:
-            points = np.array([
-                [int(lm.x * img_w), int(lm.y * img_h)]
-                for lm in landmarks.landmark
-            ])
-            hull = cv2.convexHull(points)
-            face_area = cv2.contourArea(hull)
-            face_polygon = Polygon(hull.reshape(-1, 2))
-            break  # only process the first face
-    else:
-        return jsonify({"success": False, "error": "No face detected"}), 400
-
-    # -------------------- RUN ALL MODELS --------------------
-    all_percentages = {}
-
-    for model_name, model in models.items():
-        # Confidence per model
-        conf = 0.03 if model_name == "newpig" else default_conf_threshold
-
-        results = model(img, conf=conf, imgsz=imgsz)
-        boxes_xy = results[0].boxes.xyxy.cpu().numpy()
-        boxes_cls = results[0].boxes.cls.cpu().numpy().astype(int)
-
-        class_polygons = defaultdict(list)
-
-        for i, cls_id in enumerate(boxes_cls):
-            cls_name = model.names.get(cls_id, str(cls_id))
-
-            # Skip pores from newpig
-            if model_name == "newpig" and cls_name.lower() == "pores":
-                continue
-
-            x1, y1, x2, y2 = boxes_xy[i].astype(int)
-            det_poly = shapely_box(x1, y1, x2, y2)
-            if face_polygon.intersects(det_poly):
-                intersection = det_poly.intersection(face_polygon)
-                if intersection.area > 0:
-                    class_polygons[cls_id].append(intersection)
-
-        # Pre-fill with 0 for all classes
-        skin_percentages = {
-            name: 0.0 for name in model.names.values()
-        }
-
-        # Remove pores from newpig
-        if model_name == "newpig" and "pores" in skin_percentages:
-            skin_percentages.pop("pores")
-
-        # Fill detected percentages
-        for cls_id, polys in class_polygons.items():
-            union_poly = unary_union(polys)
-            pixels = union_poly.area
-            percentage = (pixels / face_area) * 100 if face_area > 0 else 0.0
-            cls_name = model.names.get(cls_id, str(cls_id))
-            if model_name == "newpig" and cls_name.lower() == "pores":
-                continue
-            skin_percentages[cls_name] = round(percentage, 2)
-
-        all_percentages[model_name] = skin_percentages
-
-    return jsonify({"success": True, "percentages": all_percentages})
+    try:
+        if "file" not in request.files:
+            return jsonify({"success": False, "analysis": [], "error": "Server error."}), 400
+
+        file = request.files["file"]
+        file_bytes = np.frombuffer(file.read(), np.uint8)
+        img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+        if img is None:
+            return jsonify({"success": False, "analysis": [], "error": "Server error."}), 400
+
+        img_h, img_w = img.shape[:2]
+
+        # -------------------- FACE DETECTION --------------------
+        rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        result = face_mesh.process(rgb_img)
+
+        face_polygon = None
+        face_area = 0
+
+        if result.multi_face_landmarks:
+            for landmarks in result.multi_face_landmarks:
+                points = np.array([
+                    [int(lm.x * img_w), int(lm.y * img_h)]
+                    for lm in landmarks.landmark
+                ])
+                hull = cv2.convexHull(points)
+                face_area = cv2.contourArea(hull)
+                face_polygon = Polygon(hull.reshape(-1, 2))
+                break
+        else:
+            return jsonify({"success": False, "analysis": [], "error": "Server error."}), 400
+
+        # -------------------- RUN ALL MODELS --------------------
+        combined_percentages = {}
+
+        for model_name, model in models.items():
+            conf = 0.03 if model_name == "newpig" else default_conf_threshold
+
+            results = model(img, conf=conf, imgsz=imgsz)
+            boxes_xy = results[0].boxes.xyxy.cpu().numpy()
+            boxes_cls = results[0].boxes.cls.cpu().numpy().astype(int)
+
+            class_polygons = defaultdict(list)
+
+            for i, cls_id in enumerate(boxes_cls):
+                cls_name = model.names.get(cls_id, str(cls_id))
+
+                # Skip pores from newpig
+                if model_name == "newpig" and cls_name.lower() == "pores":
+                    continue
+
+                x1, y1, x2, y2 = boxes_xy[i].astype(int)
+                det_poly = shapely_box(x1, y1, x2, y2)
+                if face_polygon.intersects(det_poly):
+                    intersection = det_poly.intersection(face_polygon)
+                    if intersection.area > 0:
+                        class_polygons[cls_id].append(intersection)
+
+            # Pre-fill with 0 for all classes
+            skin_percentages = {name: 0.0 for name in model.names.values()}
+
+            if model_name == "newpig" and "pores" in skin_percentages:
+                skin_percentages.pop("pores")
+
+            # Fill detected percentages
+            for cls_id, polys in class_polygons.items():
+                union_poly = unary_union(polys)
+                pixels = union_poly.area
+                percentage = (pixels / face_area) * 100 if face_area > 0 else 0.0
+                cls_name = model.names.get(cls_id, str(cls_id))
+                if model_name == "newpig" and cls_name.lower() == "pores":
+                    continue
+                skin_percentages[cls_name] = round(percentage, 2)
+
+            combined_percentages.update(skin_percentages)
+
+        # -------------------- FORMAT INTO ANALYSIS STRING --------------------
+        analysis_str = "\n".join([
+            f"{cls_name.upper()}: {value}%" for cls_name, value in combined_percentages.items()
+        ])
+
+        return jsonify({"success": True, "analysis": [analysis_str]})
+
+    except Exception as e:
+        return jsonify({"success": False, "analysis": [], "error": "Server error."}), 500
+
 
 # -------------------- RUN --------------------
 if __name__ == "__main__":