Update app.py

app.py

@@ -1,78 +1,81 @@
-import face_recognition
-import cv2
 import numpy as np
+import cv2
+import face_recognition
+import sqlite3
+import gradio as gr
+from datetime import datetime
 
-# Load and encode known faces
+# Step 1: Initialize Face Recognition
+# Load known faces and names
 known_face_encodings = []
 known_face_names = []
 
-image_of_person = face_recognition.load_image_file("Ravinder.jpeg")
-encoding_of_person = face_recognition.face_encodings(image_of_person)[0]
-
-known_face_encodings.append(encoding_of_person)
-known_face_names.append("Person's Name")
-
-video_capture = cv2.VideoCapture(0)
-
-while True:
-    ret, frame = video_capture.read()
-    rgb_frame = frame[:, :, ::-1]
-    face_locations = face_recognition.face_locations(rgb_frame)
-    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
-
-    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
-        matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
-        name = "Unknown"
-        if True in matches:
-            match_index = matches.index(True)
-            name = known_face_names[match_index]
-        cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
-        cv2.putText(frame, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 255), 2)
-
-from deepface import DeepFace
-
-def detect_emotion(face_image):
-    result = DeepFace.analyze(face_image, actions=['emotion'], enforce_detection=False)
-    return result['dominant_emotion']
-import sqlite3
+# Example: Add known faces
+image_person1 = face_recognition.load_image_file("person1.jpg")
+encoding_person1 = face_recognition.face_encodings(image_person1)[0]
+known_face_encodings.append(encoding_person1)
+known_face_names.append("Person 1")
 
-connection = sqlite3.connect("attendance.db")
+# Step 2: Set Up Database
+connection = sqlite3.connect("attendance.db", check_same_thread=False)
 cursor = connection.cursor()
-cursor.execute('''CREATE TABLE IF NOT EXISTS Attendance
-                  (Name TEXT, Timestamp TEXT, Emotion TEXT)''')
-
-def log_attendance(name, emotion):
-    from datetime import datetime
+cursor.execute('''
+CREATE TABLE IF NOT EXISTS Attendance (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    name TEXT,
+    timestamp TEXT
+)
+''')
+
+# Function to log attendance
+def log_attendance(name):
     timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-    cursor.execute("INSERT INTO Attendance (Name, Timestamp, Emotion) VALUES (?, ?, ?)",
-                   (name, timestamp, emotion))
+    cursor.execute("INSERT INTO Attendance (name, timestamp) VALUES (?, ?)", (name, timestamp))
     connection.commit()
 
-while True:
-    ret, frame = video_capture.read()
-    rgb_frame = frame[:, :, ::-1]
-    face_locations = face_recognition.face_locations(rgb_frame)
-    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
-
+# Step 3: Detect and Recognize Faces
+def detect_and_mark_attendance(image):
+    image_np = np.array(image)
+    rgb_image = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
+    face_locations = face_recognition.face_locations(rgb_image)
+    face_encodings = face_recognition.face_encodings(rgb_image, face_locations)
+    
     for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
         matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
         name = "Unknown"
+
         if True in matches:
             match_index = matches.index(True)
             name = known_face_names[match_index]
-            face_image = frame[top:bottom, left:right]
-            emotion = detect_emotion(face_image)
-            log_attendance(name, emotion)
-
-        cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
-        cv2.putText(frame, f"{name} ({emotion})", (left, top - 10),
-                    cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 255), 2)
-
-    cv2.imshow('Video', frame)
-
-    if cv2.waitKey(1) & 0xFF == ord('q'):
-        break
+            log_attendance(name)  # Log to database
+
+        # Draw a rectangle around the face
+        cv2.rectangle(image_np, (left, top), (right, bottom), (255, 0, 0), 3)
+        cv2.putText(image_np, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+    return image_np
+
+# Step 4: Fetch Attendance Logs
+def fetch_attendance():
+    cursor.execute("SELECT * FROM Attendance")
+    rows = cursor.fetchall()
+    return rows
+
+# Step 5: Gradio Interface
+iface = gr.Interface(
+    fn=detect_and_mark_attendance,
+    inputs="image",
+    outputs="image",
+    title="Face Recognition and Attendance",
+    description="Upload an image to detect and mark attendance. Attendance will be saved to the database."
+)
+
+iface.add_component(
+    fn=fetch_attendance,
+    inputs=None,
+    outputs="dataframe",
+    label="Attendance Logs"
+)
+
+iface.launch()
 
-video_capture.release()
-cv2.destroyAllWindows()
-connection.close()