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model_age.keras

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+oid sha256:3cdea63b1fde6c352fd0c827c6bfee2fb1146a8c5a37a71b50f681365ca37954
+size 3870295

model_ethnicity.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9685cab5fd9a4e6c211a3e894f430bd7771ba78c31fa80e8685116b76dfdf49f
+size 3870294

model_gender.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:78aa4430f3fea9d61a918257df06f0026a6646db28a756d1f79174eb8261934e
+size 489293

prediction.py

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+from PIL import Image
+import requests
+from io import BytesIO
+import numpy as np
+import streamlit as st
+from tensorflow.keras.models import load_model
+import io
+
+model_gender = load_model('model_gender.keras')
+model_ethnicity = load_model('model_ethnicity.keras')
+model_age = load_model('model_age.keras')
+
+
+# Mengubah ukuran gambar dan mengonversinya ke mode grayscale
+def preprocess_image(img,):
+    img = img.resize((48, 48))
+    img_array = np.array(img.convert("L"))  # Convert ke mode grayscale
+    img_array = img_array.reshape((*(48, 48), 1))  # Menambahkan dimensi kedalam
+    img_array = img_array / 255.0  # Normalisasi
+    input_image = np.expand_dims(img_array, axis=0)
+    classes = model_gender.predict(input_image)
+    classes1 = model_ethnicity.predict(input_image)
+    classes2 = model_age.predict(input_image)
+    idx = np.where(classes >= 0.32, 1, 0).item()
+    idx1 = np.argmax(classes1, axis=1).item()
+    idx2 = np.argmax(classes2, axis=1).item()
+    label = ['Man','Woman']
+    label1 = ['White', 'Black', 'Asian', 'Indian', 'Others']
+    label2 = ['0-2','3-12', '13-18', '19-60', '60-116']
+    return st.write(f'''
+            Prediction Gender is a : {label[idx]} 
+
+            Prediction Ethnicity is a : {label1[idx1]} 
+
+            Prediction Age is a : {label2[idx2]}
+            ''')
+
+def run():
+    st.image('https://biology.missouri.edu/sites/default/files/icons/2020-10/noun_community_2739772_1.png')
+    st.markdown("<h1 style='text-align: center;'>Welcome to Gender, Ethnicity, and Age Prediction Models</h1>", unsafe_allow_html=True)
+    st.markdown("========================================================================================")
+    st.write('')       
+        
+    option = st.radio("Choose an option:", ["Upload Image", "Use Image URL"])
+
+    if option == "Upload Image":
+        # Unggah gambar dari widget Streamlit
+        uploaded_file = st.file_uploader("Choose a file", type=["jpg", "jpeg", "png"])
+        
+        if uploaded_file is not None:
+            img = Image.open(uploaded_file)
+            preprocess_image(img)
+            
+            image = Image.open(uploaded_file)
+            st.image(image, caption="Uploaded Image", use_column_width=True)
+            
+
+    elif option == "Use Image URL":
+        
+        # Masukkan URL gambar menggunakan widget input
+        image_url = st.text_input("Enter the URL of the Image:")
+        
+        if st.button("Image Prediction"):
+            if image_url:
+                # Coba memuat dan memproses gambar dari URL
+                try:
+                    response = requests.get(image_url)
+                    img = Image.open(BytesIO(response.content))
+                    preprocess_image(img)
+                    
+                    image = Image.open(io.BytesIO(requests.get(image_url).content))
+                    st.image(image, caption="Image from URL", use_column_width=True)
+
+                except Exception as e:
+                    st.error(f"Error: {e}")
+            else:
+                st.warning("Enter the image URL first.")
+
+