Update app.py

app.py

@@ -1,86 +1,89 @@
 import cv2
-import dlib
+import mediapipe as mp
 import numpy as np
+from flask import Flask, render_template, request
 import tensorflow as tf
-from tensorflow.keras.models import load_model
-from flask import Flask, render_template
-import requests
 
-# Initialize Flask app
+# Initialize the Flask app
 app = Flask(__name__)
 
-# Facial Recognition Setup
-detector = dlib.get_frontal_face_detector()
-predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
-
-# Load Skin Care Model (TensorFlow)
-skin_care_model = load_model('skin_condition_model.h5')
-
-# Makeup Image
-lipstick = cv2.imread("lipstick_image.png", cv2.IMREAD_UNCHANGED)
-
-def get_weather(location="your_city"):
-    # Fetch weather data from OpenWeatherMap API
-    api_key = "e835da1cfdcc7d7f97ba4158e97b518e"
-    url = f"http://api.openweathermap.org/data/2.5/weather?q={location}&appid={api_key}&units=metric"
-    response = requests.get(url)
-    weather_data = response.json()
-    return weather_data['weather'][0]['description']
-
-def suggest_outfit(weather):
-    if "rain" in weather:
-        return "Suggested Outfit: Raincoat and boots."
-    elif "clear" in weather:
-        return "Suggested Outfit: Light jacket and sunglasses."
-    else:
-        return "Suggested Outfit: Comfortable casuals."
-
-def apply_makeup(face_image, makeup_image):
-    y_offset = 50  # Example offset
-    x_offset = 100  # Example offset
-    
-    for c in range(0, 3):
-        face_image[y_offset:y_offset+makeup_image.shape[0], x_offset:x_offset+makeup_image.shape[1], c] = \
-            makeup_image[:, :, c] * (makeup_image[:, :, 3] / 255.0) + \
-            face_image[y_offset:y_offset+makeup_image.shape[0], x_offset:x_offset+makeup_image.shape[1], c] * \
-            (1.0 - makeup_image[:, :, 3] / 255.0)
-    return face_image
-
-def predict_skin_condition(image):
-    image = cv2.resize(image, (224, 224))  # Resize to model's input size
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
-    image = image / 255.0  # Normalize the image
-    
-    prediction = skin_care_model.predict(image)
-    if prediction[0] > 0.5:
-        return "Acne detected: Recommended product XYZ"
-    else:
-        return "Skin is clear: Recommended moisturizer ABC"
-
+# 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"
+
+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 home():
-    # Capture the video feed from webcam
+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()
-    cap.release()
-
-    # Process the face for skin care condition
-    skin_condition = predict_skin_condition(frame)
+    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
+    }
 
-    # Detect face and apply makeup
-    faces = detector(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY))
-    for face in faces:
-        landmarks = predictor(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), face)
-        frame = apply_makeup(frame, lipstick)
-
-    # Get weather and outfit suggestion
-    weather = get_weather()
-    outfit_suggestion = suggest_outfit(weather)
-
-    # Display results on the web page
-    return render_template('index.html', skin_condition=skin_condition, outfit_suggestion=outfit_suggestion)
+    cap.release()
+    return recommendations
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     app.run(debug=True)