Delete prediction.py

prediction.py

@@ -1,78 +0,0 @@
-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_fixed.keras')
-model_ethnicity = load_model('model_ethnicity_fixed.keras')
-model_age = load_model('model_age_fixed.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.")
-
-