test.py

import streamlit as st
import pandas as pd
import numpy as np
import pickle
from sklearn.ensemble import RandomForestClassifier
import cv2
import tempfile
import os
import gspread
from google.oauth2.service_account import Credentials

CRE_dict = {
  "type": "service_account",
  "project_id": "aiprojectdatabase-433803",
  "private_key_id": "24a66978b0cf51999f68af9c3604d867374754ef",
  "private_key": "-----BEGIN PRIVATE KEY-----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\nd4hOzsRIupHXY4tdLgJmyd4=\n-----END PRIVATE KEY-----\n",
  "client_email": "sheetaccess@aiprojectdatabase-433803.iam.gserviceaccount.com",
  "client_id": "104019459891593823430",
  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
  "token_uri": "https://oauth2.googleapis.com/token",
  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/sheetaccess%40aiprojectdatabase-433803.iam.gserviceaccount.com",
  "universe_domain": "googleapis.com"
}

def db_access(Skin_Type,Skin_Col,CRE_dict):
    # Define the scope
    scope = ["https://www.googleapis.com/auth/spreadsheets", "https://www.googleapis.com/auth/drive"]

    # Provide the path to your service account key
    creds = Credentials.from_service_account_info(CRE_dict, scopes=scope)

    # Authorize the client
    client = gspread.authorize(creds)

    # Open the Google Sheet
    sheet = client.open("database").worksheet("SQL_test")  # Use .worksheet('sheet_name') for specific sheets

    # Update a specific cell
    sheet.update_cell(2,1,Skin_Type)
    print("Skin Type Import to DB")
    sheet.update_cell(2,2,Skin_Col)
    print("Skin Color Import to DB")

def Foundation_Access(CRE_dict):
    # Define the scope
    scope = ["https://www.googleapis.com/auth/spreadsheets", "https://www.googleapis.com/auth/drive"]

    # Provide the path to your service account key
    creds = Credentials.from_service_account_info(CRE_dict, scopes=scope)

    # Authorize the client
    client = gspread.authorize(creds)

    # Open the Google Sheet
    sheet = client.open("database").worksheet("SQL_test")  # Use .worksheet('sheet_name') for specific sheets

    # REad a specific cell
    
    #color_name = sheet.acell("Y4").value
    #print(f"Color cell name is {color_name}")
    #color_hex = sheet.acell("Y4").value
    #print(f"Color Hex Value is {color_hex}")

    #st.markdown("Foundation Suggestion Color")
    #st.markdown(f"""<div style="background-color:{color_hex}; padding: 20px; border-radius: 5px;">
    #<p style="color:white; text-align:center;">{color_name}</p></div>""", unsafe_allow_html=True)

    Foundation_Color = sheet.get("Q4:Q")
    Foundation_Hex = sheet.get("Y4:Y")
    flattened_color_data = [item for sublist in Foundation_Color for item in sublist]
    flattened_hex_data = [item for sublist in Foundation_Hex for item in sublist]
    for i,j in zip(flattened_color_data,flattened_hex_data):
        print(i)
        print(j)
        st.markdown(f"""<div style="background-color:{j}; padding: 20px; border-radius: 5px;">
        <p style="color:white; text-align:center;">{i}</p></div>""", unsafe_allow_html=True)    

def get_skin_color_from_face(image_path):
    # Load the image
    image = cv2.imread(image_path)
    # Convert the image from BGR to RGB
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    # Load the Haar cascade XML file
    face_cascade_path = cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
    face_cascade = cv2.CascadeClassifier(face_cascade_path)

    # Convert the image to grayscale for face detection
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # Detect faces in the image
    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
    
    if len(faces) == 0:
        raise ValueError("No face detected in the image.")

    # Assume the first detected face is the one we want
    x, y, w, h = faces[0]
    
    # Extract the face region from the image
    face_region = image_rgb[y:y+h, x:x+w]
    
    # Convert the face region to HSV color space
    face_hsv = cv2.cvtColor(face_region, cv2.COLOR_RGB2HSV)
    
    # Define skin color range in HSV
    lower_skin = np.array([0, 20, 70], dtype=np.uint8)
    upper_skin = np.array([20, 255, 255], dtype=np.uint8)
    
    # Create a mask for skin color
    skin_mask = cv2.inRange(face_hsv, lower_skin, upper_skin)
    
    # Apply the mask to the face region
    skin = cv2.bitwise_and(face_region, face_region, mask=skin_mask)
    
    # Find the average color of the skin
    mean_color = cv2.mean(face_region, mask=skin_mask)[:3]
    
    # Calculate the most frequent (mode) color in the skin region
    # Reshape the skin region to a 2D array of pixels
    pixels = skin[skin_mask > 0].reshape(-1, 3)
    
    # Find the mode of the pixels (most frequent color)
    mode_color = np.array([np.bincount(pixels[:, i]).argmax() for i in range(3)])
    
    # Find the midpoint between mean and mode colors
    midpoint_color = ((mean_color + mode_color) / 2).astype(int)
    
    # MODE Color Convert RGB to HEX
    hex_color = '#{:02x}{:02x}{:02x}'.format(mode_color[0], mode_color[1], mode_color[2])

    # MIDPOINT COLOR Convert RGB to HEX
    #hex_color = '#{:02x}{:02x}{:02x}'.format(midpoint_color[0], midpoint_color[1], midpoint_color[2])
    
    # MEAN COLOR Convert RGB to HEX
    #hex_color = '#{:02x}{:02x}{:02x}'.format(int(mean_color[0]), int(mean_color[1]), int(mean_color[2]))

    #Assign RGB Color code
    rgbcolor = mode_color

    return hex_color, image_rgb, (x, y, w, h), face_region, skin_mask, skin, rgbcolor

def user_input_features(r,g,b,SkinType):
                      
    #island = st.sidebar.selectbox('Island',('Biscoe','Dream','Torgersen'))
    #sex = st.sidebar.selectbox('Sex',('male','female'))
    #bill_length_mm = st.sidebar.slider('Bill length (mm)', 32.1,59.6,43.9)
    #bill_depth_mm = st.sidebar.slider('Bill depth (mm)', 13.1,21.5,17.2)
    #flipper_length_mm = st.sidebar.slider('Flipper length (mm)', 172.0,231.0,201.0)
    #body_mass_g = st.sidebar.slider('Body mass (g)', 2700.0,6300.0,4207.0)
    data = {'R': r,
            'G': g,
            'B': b,
            'Core_Skin_Type': SkinType}
    features = pd.DataFrame(data, index=[0])
    return features


st.set_page_config(page_title="Skin Color Checker", page_icon="📷")
st.markdown("<h1 style='text-align: center;'>Skin Color Checker?</h1>", unsafe_allow_html=True)
st.markdown("<h6 style='text-align: center;'>နောက်ခံပြောင်မှာ မျက်နှာ ကို ရှင်းရှင်းလင်းလင်းပေါ်ရင်‌ပိုကောင်းပါတယ်</h6>", unsafe_allow_html=True)
st.markdown("<h6 style='text-align: center;'>နေရောင်ရှိတဲ့ဘက်မျက်နှာမူပြီးရရိုက်ရင် ပိုကောင်းပါတယ်</h6>", unsafe_allow_html=True)
st.markdown("---")
st.markdown(f"""<div style="background-color:red; padding: 10px; border-radius: 1px;">
        <p style="color:white; text-align:center;">Upload photo က 2MB ထပ်ကျော်ရင် server down ပါတယ် </p></div>""", unsafe_allow_html=True)
st.markdown("---")

image_file = st.file_uploader("Upload Your Selfie to Color Check", type=["jpg", "png", "jpeg"])

st.markdown("<h6 style='text-align: center;'>Disclaimer: We do not store personal data and your picture only valid in this particular instance</h6>", unsafe_allow_html=True)

if image_file is not None:
  

    if 'show_help' not in st.session_state:
        st.session_state.show_help = False
    col1, col2 = st.columns([1, 1])

    #if not st.session_state.show_help:
    with col1:
        userskintype = st.selectbox("Select your skin type", options=("---","Oily","Dry","Sensitive","Combination"))
        print(userskintype)

    with col2:
        if st.button('Need Help with Your Skin Type ?'):
            st.session_state.show_help = not st.session_state.show_help

    if st.session_state.show_help:
        st.markdown("<h1 style='text-align: center;'>What is Your Skin Type ?</h1>", unsafe_allow_html=True)
        st.subheader("Oily Skin")
        st.markdown("""Oily skin is a skin type characterized by excess production of sebum, the natural oil produced by sebaceous glands in the skin. This excess oil can lead to a shiny or greasy appearance, enlarged pores, and an increased likelihood of acne and blackheads. People with oily skin often need to use specific skincare products to manage oil production and keep their skin balanced.""")
        st.image("pic/OilySkin2.jpg","Oily Skin Sample")

        st.subheader("Dry Skin")
        st.markdown("""Dry skin is a skin type characterized by a lack of moisture in the outer layer of the skin. This can lead to a rough, flaky, or scaly texture, a tight or uncomfortable feeling, and sometimes itching or irritation. Dry skin can be caused by environmental factors like cold weather, low humidity, or harsh soaps, as well as by underlying health conditions or aging. People with dry skin typically need to use moisturizing products to help restore and maintain hydration.""")
        st.image("pic/DrySkin.jpg","Dry Skin Sample")

        st.subheader("Sensitive Skin")
        st.markdown("""Sensitive skin is a skin type that reacts easily to various products, environmental factors, or even touch. It can become red, itchy, or irritated when exposed to things like harsh chemicals, fragrances, or extreme temperatures. People with sensitive skin need to be careful with the products they use to avoid triggering these reactions.""")
        st.image("pic/SenSkin.jpg","Sensitive Skin Sample")

        st.subheader("Combination Skin")
        st.markdown("""Combination skin is characterized by having different skin types in various areas of the face. Typically, the T-zone, which includes the forehead, nose, and chin, is oilier and may have larger pores. This area often experiences excess shine, blackheads, or acne due to the increased oil production. In contrast, the cheeks and sometimes other parts of the face, like the jawline or around the eyes, may be drier or normal. These areas can feel tight, flaky, or less oily. Managing combination skin often requires using different products or skincare routines for each area: a mattifying treatment for the oily T-zone and a hydrating product for the drier regions.""")
        st.image("pic/CombineSkin1.jpg","Combination Skin Sample")
        
        # Show close help button
        if st.button('Close Help'):
            st.session_state.show_help = False


    if userskintype != "---":

        with tempfile.NamedTemporaryFile(delete=False) as temp_file:
            temp_file.write(image_file.read())
            temp_file_path = temp_file.name

        submitbtm = st.button("Start Analyze")
        
        if submitbtm:

            clearbtm = st.button("Clear Data")

            try:
                skin_hex, image_rgb, face_rect, face_region, skin_mask, skin_region, rgbcolor = get_skin_color_from_face(temp_file_path)
                st.markdown(f"""<div style="background-color:{skin_hex}; padding: 20px; border-radius: 5px;">
            <p style="color:white; text-align:center;">This is your color tone {skin_hex}!</p></div>""", unsafe_allow_html=True)
                st.markdown("---")
                # Display images
                st.image(image_rgb, caption="Original Image")
                st.markdown("---")

                x, y, w, h = face_rect
                cv2.rectangle(image_rgb, (x, y), (x+w, y+h), (0, 255, 0), 2)
                st.image(image_rgb, caption="Detected Face")
                st.markdown("---")

                st.image(face_region, caption="Face Region")
                st.markdown("---")

                st.image(skin_mask, caption="Skin Mask")
                st.markdown("---")

                st.image(skin_region, caption="Skin Region")
                st.markdown("---")

                st.markdown(f"""<div style="background-color:{skin_hex}; padding: 20px; border-radius: 5px;">
            <p style="color:white; text-align:center;">{skin_hex}</p></div>""", unsafe_allow_html=True)
                st.markdown(f"""<div style="background-color:{skin_hex}; padding: 20px; border-radius: 5px;">
            <p style="color:white; text-align:center;">R:{rgbcolor[0]}, G:{rgbcolor[1]}, B:{rgbcolor[2]}</p></div>""", unsafe_allow_html=True)
                
                red = rgbcolor[0]
                green = rgbcolor[1]
                blue = rgbcolor[2]
                input_df = user_input_features(red,green,blue,userskintype)

                #Model Releated Section
                makeupdataset_clean = pd.read_csv("https://raw.githubusercontent.com/htetlwink/AIProjectTest/main/dataset/makeupdatasetclean.csv")
                makeupdataset = makeupdataset_clean.drop(columns=['Core_Color'])
                df = pd.concat([input_df,makeupdataset],axis=0)
                
                #Encode the skintype
                encode = ['Core_Skin_Type']
                for col in encode:
                    dummy = pd.get_dummies(df[col], prefix=col)
                    df = pd.concat([df,dummy], axis=1)
                    del df[col]
                df = df[:1]

                #Model Loaded
                load_clf = pickle.load(open('model/TrainedModel.pkl', 'rb'))

                #Model Prediction
                prediction = load_clf.predict(df)

                st.subheader('You Should Use This MakeUp Color')
                CoreColor_Suggest = np.array(['Pale','Natural','Golden','Mocha'])
                st.write(CoreColor_Suggest[prediction][0])

                #Save the predict Color
                ColorPredit = CoreColor_Suggest[prediction][0]

                #Import Data into sheet
                db_access(userskintype, ColorPredit,CRE_dict)

                #Display Foundation Color Suggestion
                Foundation_Access(CRE_dict)
                
            except ValueError as e:
                st.error(str(e))
            finally:
                os.remove(temp_file_path)
            if clearbtm:
                image_file is None