Update app.py

app.py

@@ -1,55 +1,87 @@
 import cv2
+import dlib
 import numpy as np
-import torch
-import streamlit as st
-from PIL import Image  # Import Image from PIL
-from transformers import AutoModelForImageClassification, AutoFeatureExtractor
-
-# Load AI Model from Hugging Face
-model_name = "microsoft/resnet-50"
-model = AutoModelForImageClassification.from_pretrained(model_name)
-feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
-
-def process_frame(frame):
-    # Convert frame to tensor
-    inputs = feature_extractor(images=[frame], return_tensors="pt")
-    outputs = model(**inputs)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
-    return predictions
-
-def main():
-    st.title("Smart Mirror AI")
-    
-    # Option to run camera or upload image
-    run = st.checkbox("Run Camera")
-    uploaded_image = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
-    
-    if run:
-        cap = cv2.VideoCapture(0)
-        frame_placeholder = st.empty()
-        
-        while run:
-            success, frame = cap.read()
-            if not success:
-                st.write("Failed to capture video")
-                break
-            else:
-                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-                predictions = process_frame(frame)
-                frame_placeholder.image(frame, channels="RGB")
-        cap.release()
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from flask import Flask, render_template
+import requests
+
+# Initialize 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 = "your_api_key"
+    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
     
-    elif uploaded_image is not None:
-        # If an image is uploaded, process it
-        image = Image.open(uploaded_image)  # Use PIL.Image to open the image
-        image = np.array(image)
-        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-        predictions = process_frame(image)
-        st.image(image, channels="BGR")
-        st.write(f"Predictions: {predictions}")
+    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
     
-if __name__ == "__main__":
-    main()
+    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"
+
+@app.route('/')
+def home():
+    # Capture the video feed from webcam
+    cap = cv2.VideoCapture(0)
+    ret, frame = cap.read()
+    cap.release()
+
+    # Process the face for skin care condition
+    skin_condition = predict_skin_condition(frame)
+
+    # 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)
+
+if __name__ == '__main__':
+    app.run(debug=True)