Update app.py

app.py

@@ -10,8 +10,8 @@ import numpy as np
 # Constants
 KNOWN_FOLDER = "known_faces"
 ESP32_SERVER_URL = "https://esp32-upload-server.onrender.com"
-MODEL_NAME = "ArcFace"  # Options: "VGG-Face", "Facenet", "ArcFace", etc.
-DETECTOR_BACKEND = "retinaface"  # Options: "opencv", "mtcnn", "dlib", "retinaface", etc.
+MODEL_NAME = "ArcFace"
+DETECTOR_BACKEND = "retinaface"
 
 # Streamlit setup
 st.set_page_config(page_title="Second Eye - Enhanced Recognition", layout="centered")
@@ -33,22 +33,17 @@ def get_latest_image():
 def preprocess_image(image_path):
     img = cv2.imread(image_path)
     if img is None:
-        return image_path  # Fallback
+        return image_path
 
-    # Convert to grayscale
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
-    # CLAHE for contrast enhancement
     clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
     enhanced = clahe.apply(gray)
 
-    # Gamma correction
     gamma = 1.5
     lookUpTable = np.array([((i / 255.0) ** gamma) * 255 for i in range(256)]).astype("uint8")
     gamma_corrected = cv2.LUT(enhanced, lookUpTable)
-
-    # Convert back to BGR
     final_img = cv2.cvtColor(gamma_corrected, cv2.COLOR_GRAY2BGR)
+
     output_path = "preprocessed.jpg"
     cv2.imwrite(output_path, final_img)
     return output_path
@@ -68,6 +63,8 @@ def is_face_detected(image_path):
 # Compare with known faces
 def compare_with_known_faces(unknown_img_path):
     for filename in os.listdir(KNOWN_FOLDER):
+        if not filename.lower().endswith((".jpg", ".jpeg", ".png")):
+            continue
         known_img_path = os.path.join(KNOWN_FOLDER, filename)
         try:
             result = DeepFace.verify(
@@ -113,4 +110,4 @@ if page == "Face Recognition":
             tts.save("result.mp3")
             st.audio("result.mp3", autoplay=True)
         else:
-            st.warning("No image found on ESP32 server")
+            st.warning("No image found on ESP32 server")