Added app.py file

app.py

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+from streamlit_webrtc import webrtc_streamer, RTCConfiguration
+import streamlit as st
+import av
+import cv2
+import numpy as np
+from deepface import DeepFace
+import time
+
+st.set_page_config(page_title="AI Facial Interview Monitor", layout="wide")
+
+st.title(":blue[MOCKVIEWER - Face Monitoring System]")
+
+# Load Haar Cascade models
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_eye.xml")
+
+# Upload reference image
+uploaded_image = st.file_uploader("Upload your reference face image", type=["jpg", "jpeg", "png"])
+if uploaded_image:
+    user_ref_img = cv2.imdecode(np.frombuffer(uploaded_image.read(), np.uint8), cv2.IMREAD_COLOR)
+    st.image(user_ref_img, caption="Reference Image", use_column_width=True)
+
+face_detected_time = time.time()
+last_verified_time = 0
+
+# Configs
+FACE_TIMEOUT = 60  # seconds of face absence before cancelling
+VERIFY_INTERVAL = 30  # seconds between face identity checks
+
+
+def is_confident_pose(face_roi):
+    """Very basic heuristic — head is upright, eyes are visible."""
+    gray = cv2.cvtColor(face_roi, cv2.COLOR_BGR2GRAY)
+    eyes = eye_cascade.detectMultiScale(gray, 1.1, 4)
+    return len(eyes) >= 1
+
+
+def match_identity(live_face, ref_img):
+    try:
+        result = DeepFace.verify(live_face, ref_img, enforce_detection=False, model_name='Facenet', detector_backend='opencv')
+        return result["verified"]
+    except Exception as e:
+        print("Verification error:", e)
+        return False
+
+
+class VideoProcessor:
+    def __init__(self):
+        self.last_check = time.time()
+        self.last_verified = time.time()
+        self.face_missing = False
+
+    def recv(self, frame):
+        global face_detected_time, last_verified_time
+        img = frame.to_ndarray(format="bgr24")
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+        faces = face_cascade.detectMultiScale(gray, 1.1, 4)
+
+        if len(faces) == 0:
+            if time.time() - face_detected_time > FACE_TIMEOUT:
+                cv2.putText(img, "❌ Interview Cancelled: Face not visible!", (30, 30),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
+            else:
+                cv2.putText(img, "⚠️ Face not visible. You have 60 seconds.", (30, 30),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)
+        else:
+            face_detected_time = time.time()
+            for (x, y, w, h) in faces:
+                face_roi = img[y:y+h, x:x+w]
+
+                # Eye and posture check
+                confident = is_confident_pose(face_roi)
+                status = "✅ Confident Pose" if confident else "⚠️ Look Straight!"
+                color = (0, 255, 0) if confident else (0, 255, 255)
+                cv2.putText(img, status, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)
+                cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
+
+                # Identity check every VERIFY_INTERVAL seconds
+                if uploaded_image and (time.time() - last_verified_time) > VERIFY_INTERVAL:
+                    matched = match_identity(face_roi, user_ref_img)
+                    last_verified_time = time.time()
+                    if not matched:
+                        cv2.putText(img, "❌ Identity mismatch!", (x, y + h + 30),
+                                    cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
+                    else:
+                        cv2.putText(img, "✅ Identity Verified", (x, y + h + 30),
+                                    cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+
+        return av.VideoFrame.from_ndarray(img, format="bgr24")
+
+
+# Streamlit WebRTC component
+webrtc_streamer(
+    key="monitor",
+    video_processor_factory=VideoProcessor,
+    rtc_configuration=RTCConfiguration(
+        {"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]}
+    )
+)