Update app.py

app.py

@@ -6,66 +6,68 @@ import os
 from keras.models import load_model
 from PIL import Image
 import mediapipe as mp
+import requests
 import tempfile
 
-# Larger title
-st.markdown("<h1 style='text-align: center;'>Emotion Detection with Face Recognition</h1>", unsafe_allow_html=True)
+# API endpoint for sending data
+API_ENDPOINT = "https://huggingface.glitch.me/update"
 
-# Smaller subtitle
+# Title
+st.markdown("<h1 style='text-align: center;'>Emotion Detection with Face Recognition</h1>", unsafe_allow_html=True)
 st.markdown("<h3 style='text-align: center;'>angry, fear, happy, neutral, sad, surprise</h3>", unsafe_allow_html=True)
 
-# Start time for measuring performance
-start = time.time()
-
-# Load the emotion detection model
+# Load emotion detection model
 @st.cache_resource
 def load_emotion_model():
-    model = load_model('CNN_Model_acc_75.h5')  # Ensure this file is in your Space
+    model = load_model('CNN_Model_acc_75.h5')
     return model
 
 model = load_emotion_model()
-print("Time taken to load model: ", time.time() - start)
-
-# Emotion labels
 emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']
 
-# Load known faces and names
+# Load known faces
 known_faces = []
 known_names = []
 face_recognizer = cv2.face.LBPHFaceRecognizer_create()
 
 def load_known_faces():
-    folder_path = "known_faces"  # Folder containing known face images
+    folder_path = "known_faces"
     for image_name in os.listdir(folder_path):
         if image_name.endswith(('.jpg', '.jpeg', '.png')):
             image_path = os.path.join(folder_path, image_name)
             image = cv2.imread(image_path)
             gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-            # Detect face in the image
             faces = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml').detectMultiScale(
                 gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)
             )
-
             for (x, y, w, h) in faces:
                 roi_gray = gray[y:y+h, x:x+w]
                 known_faces.append(roi_gray)
-                known_names.append(image_name.split('.')[0])  # Assuming file name is the person's name
-
-    # Train the recognizer with the known faces
+                known_names.append(image_name.split('.')[0])
     face_recognizer.train(known_faces, np.array([i for i in range(len(known_faces))]))
 
 load_known_faces()
 
-# Mediapipe face detection
 mp_face_detection = mp.solutions.face_detection.FaceDetection(min_detection_confidence=0.5)
 
+# Function to send data to API
+def send_data_to_api(name, emotion):
+    try:
+        data = {'name': name, 'emotion': emotion}
+        response = requests.post(API_ENDPOINT, data=data)
+        if response.status_code == 200:
+            st.success(f"Data sent successfully: {response.json()}")
+        else:
+            st.error(f"Failed to send data: {response.text}")
+    except Exception as e:
+        st.error(f"Error sending data to API: {str(e)}")
+
 # Process a single frame
 def process_frame(frame):
     rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
     results = mp_face_detection.process(rgb_frame)
 
-    result_text = ""  # Initialize result text
-
+    result_text = ""
     if results.detections:
         for detection in results.detections:
             bboxC = detection.location_data.relative_bounding_box
@@ -80,22 +82,20 @@ def process_frame(frame):
             roi_gray = cv2.cvtColor(roi_color, cv2.COLOR_BGR2GRAY)
             face_roi = cv2.resize(roi_color, (48, 48))
             face_roi = cv2.cvtColor(face_roi, cv2.COLOR_BGR2RGB)
-            face_roi = np.expand_dims(face_roi, axis=0) / 255.0  # Normalize
+            face_roi = np.expand_dims(face_roi, axis=0) / 255.0
 
-            # Emotion detection
             predictions = model.predict(face_roi)
             emotion = emotion_labels[np.argmax(predictions[0])]
 
-            # Face recognition
             name = "Unknown"
             label, confidence = face_recognizer.predict(roi_gray)
             if confidence < 100:
                 name = known_names[label]
 
-            # Format result text
             result_text = f"{name} is feeling {emotion}"
+            if name != "Unknown":
+                send_data_to_api(name, emotion)
 
-            # Draw bounding box and label
             cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
             cv2.putText(frame, result_text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
 
@@ -103,8 +103,8 @@ def process_frame(frame):
 
 # Video feed display
 def video_feed(video_source):
-    frame_placeholder = st.empty()  # Placeholder for displaying video frames
-    text_placeholder = st.empty()  # Placeholder for displaying result text
+    frame_placeholder = st.empty()
+    text_placeholder = st.empty()
 
     while True:
         ret, frame = video_source.read()
@@ -112,8 +112,6 @@ def video_feed(video_source):
             break
 
         frame, result_text = process_frame(frame)
-
-        # Display frame and result text
         frame_placeholder.image(frame, channels="BGR", use_column_width=True)
         text_placeholder.markdown(f"<h3 style='text-align: center;'>{result_text}</h3>", unsafe_allow_html=True)
 
@@ -148,4 +146,4 @@ elif upload_choice == "Upload Video":
             video_source = cv2.VideoCapture(tfile.name)
             video_feed(video_source)
 
-st.sidebar.write("Emotion Labels: Angry, Fear, Happy, Neutral, Sad, Surprise")
+st.sidebar.write("Emotion Labels: Angry, Fear, Happy, Neutral, Sad, Surprise")