Update app.py

app.py

@@ -1,87 +1,61 @@
+import os
 import cv2
-import mediapipe as mp
+import dlib
 import numpy as np
-from flask import Flask, render_template, request
 import tensorflow as tf
+import mediapipe as mp
+from flask import Flask, render_template, Response
 
-# Initialize the Flask app
+# Initialize Flask App
 app = Flask(__name__)
 
-# Initialize Mediapipe for face detection
-mp_face_detection = mp.solutions.face_detection
-mp_drawing = mp.solutions.drawing_utils
-
-# Load AI models for skin care, health, makeup, and fashion recommendations
-# You should have these models pre-trained and available
-# For simplicity, placeholders are used
-skin_care_model = tf.keras.models.load_model('skin_care_model.h5')  # Example placeholder
-makeup_model = tf.keras.models.load_model('makeup_model.h5')        # Example placeholder
-health_model = tf.keras.models.load_model('health_model.h5')        # Example placeholder
-fashion_model = tf.keras.models.load_model('fashion_model.h5')      # Example placeholder
-
-# Function to detect faces using Mediapipe
-def detect_faces(image):
-    with mp_face_detection.FaceDetection(min_detection_confidence=0.2) as face_detection:
-        # Convert the image to RGB for Mediapipe
-        rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-        results = face_detection.process(rgb_image)
-        if results.detections:
-            for detection in results.detections:
-                bboxC = detection.location_data.relative_bounding_box
-                ih, iw, _ = image.shape
-                x, y, w, h = int(bboxC.xmin * iw), int(bboxC.ymin * ih), int(bboxC.width * iw), int(bboxC.height * ih)
-                cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
-    return image
-
-# Placeholder recommendation functions (you should replace these with actual AI models)
-def get_skin_care_recommendation(face_image):
-    # Analyze the skin condition (dummy function for example)
-    return "Recommended product: Vitamin C Serum"
-
-def get_makeup_recommendation(face_image):
-    # Suggest makeup based on facial features (dummy function for example)
-    return "Suggested makeup: Natural look foundation"
+# Load Face Detector (Dlib)
+detector = dlib.get_frontal_face_detector()
+
+# Load Pretrained Model for Skin Analysis (Placeholder)
+model_path = "skin_model.h5"
+if os.path.exists(model_path):
+    skin_model = tf.keras.models.load_model(model_path)
+else:
+    skin_model = None
+
+# OpenCV Video Capture
+cap = cv2.VideoCapture(0)
+
+# Function to Analyze Skin
+def analyze_skin(frame):
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = detector(gray)
+    for face in faces:
+        x, y, w, h = face.left(), face.top(), face.width(), face.height()
+        face_crop = frame[y:y+h, x:x+w]
+        if skin_model:
+            face_crop = cv2.resize(face_crop, (224, 224)) / 255.0
+            prediction = skin_model.predict(np.expand_dims(face_crop, axis=0))
+            return f"Skin Condition Score: {prediction[0][0]:.2f}"
+    return "No face detected"
+
+# Video Stream Function
+def generate_frames():
+    while True:
+        success, frame = cap.read()
+        if not success:
+            break
+        else:
+            skin_result = analyze_skin(frame)
+            cv2.putText(frame, skin_result, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+            ret, buffer = cv2.imencode('.jpg', frame)
+            frame = buffer.tobytes()
+            yield (b'--frame\r\n'
+                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
 
-def get_health_recommendation(face_image):
-    # Analyze health metrics (dummy function for example)
-    return "Health alert: Normal blood pressure"
-
-def get_fashion_recommendation(face_image):
-    # Suggest outfits based on style and weather (dummy function for example)
-    return "Suggested outfit: Casual wear suitable for sunny weather"
-
-# Route to handle the display of the mirror and recommendations
 @app.route('/')
 def index():
-    return render_template('index.html')  # Add your HTML file here
-
-@app.route('/capture', methods=['POST'])
-def capture():
-    # Capture an image from the webcam
-    cap = cv2.VideoCapture(0)
-    ret, frame = cap.read()
-    if not ret:
-        return "Failed to capture image", 400
-
-    # Process the captured frame to detect faces and provide recommendations
-    frame = detect_faces(frame)
-
-    # Extract personalized recommendations (example placeholders)
-    skin_care = get_skin_care_recommendation(frame)
-    makeup = get_makeup_recommendation(frame)
-    health = get_health_recommendation(frame)
-    fashion = get_fashion_recommendation(frame)
-
-    # Return recommendations as response
-    recommendations = {
-        'skin_care': skin_care,
-        'makeup': makeup,
-        'health': health,
-        'fashion': fashion
-    }
+    return render_template('index.html')
 
-    cap.release()
-    return recommendations
+@app.route('/video_feed')
+def video_feed():
+    return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
 
 if __name__ == "__main__":
     app.run(debug=True)