Update app.py

app.py

@@ -1,50 +1,49 @@
-import gradio as gr
-import numpy as np
-import cv2
-import joblib
-from sklearn.metrics.pairwise import euclidean_distances
-
-# Load model files
-mean_face = np.load("model/mean_face.npy")
-eigenfaces = np.load("model/eigenfaces.npy")
-projections = np.load("model/projections.npy")
-labels = np.load("model/labels.npy")
-label_names = np.load("model/label_names.npy")
-with open("model/img_shape.txt", "r") as f:
-    img_h, img_w = map(int, f.read().strip().split())
-
-# Distance threshold (tune based on performance)
-THRESHOLD = 8000
-
-def recognize_face_from_frame(frame):
-    # Convert to grayscale
-    img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    # Resize to training size
-    img = cv2.resize(img, (img_w, img_h))
-    # Flatten and normalize
-    img_flat = img.flatten()
-    img_centered = img_flat - mean_face
-    # Project to eigenface space
-    projected = np.dot(img_centered, eigenfaces.T)
-
-    # Compare with stored projections
-    dists = euclidean_distances([projected], projections)[0]
-    min_dist = np.min(dists)
-    min_index = np.argmin(dists)
-
-    if min_dist < THRESHOLD:
-        predicted_name = label_names[labels[min_index]]
-        return f"🧠 Predicted: {predicted_name} (distance: {min_dist:.2f})"
-    else:
-        return "😕 Unknown face"
-
-# Web UI
-iface = gr.Interface(
-    fn=recognize_face_from_frame,
-    inputs=gr.Image(source="webcam", streaming=False),
-    outputs="text",
-    title="🔍 EigenFace Recognition",
-    description="Scan your face. If you're in the dataset, the system will recognize you."
-)
-
-iface.launch()
+import gradio as gr
+import numpy as np
+import cv2
+from sklearn.metrics.pairwise import euclidean_distances
+
+# Load model files
+mean_face = np.load("model/mean_face.npy")
+eigenfaces = np.load("model/eigenfaces.npy")
+projections = np.load("model/projections.npy")
+labels = np.load("model/labels.npy")
+label_names = np.load("model/label_names.npy")
+with open("model/img_shape.txt", "r") as f:
+    img_h, img_w = map(int, f.read().strip().split())
+
+# Distance threshold (tune based on performance)
+THRESHOLD = 8000
+
+def recognize_face_from_frame(frame):
+    # Convert to grayscale
+    img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    # Resize to training size
+    img = cv2.resize(img, (img_w, img_h))
+    # Flatten and normalize
+    img_flat = img.flatten()
+    img_centered = img_flat - mean_face
+    # Project to eigenface space
+    projected = np.dot(img_centered, eigenfaces.T)
+
+    # Compare with stored projections
+    dists = euclidean_distances([projected], projections)[0]
+    min_dist = np.min(dists)
+    min_index = np.argmin(dists)
+
+    if min_dist < THRESHOLD:
+        predicted_name = label_names[labels[min_index]]
+        return f"🧠 Predicted: {predicted_name} (distance: {min_dist:.2f})"
+    else:
+        return "😕 Unknown face"
+
+# Web UI
+iface = gr.Interface(
+    fn=recognize_face_from_frame,
+    inputs=gr.Camera(),
+    outputs="text",
+    title="🔍 EigenFace Recognition",
+    description="Scan your face. If you're in the dataset, the system will recognize you."
+)
+
+iface.launch()