Delete app.py

app.py

@@ -1,63 +0,0 @@
-# -*- coding: utf-8 -*-
-"""demo.ipynb
-Automatically generated by Colab.
-Original file is located at
-    https://colab.research.google.com/drive/10nAdNOzeCbnza9ZenZqOLtvBYn8BKlk5
-"""
-
-import streamlit as st
-
-from PIL import Image
-import numpy as np
-from joblib import load
-from skimage.transform import resize
-import os
-
-
-
-
-def load_model():
-    # Ensure the model path is correct and accessible from your current directory
-    model_path = 'svm_model3.joblib'  # Update this path to where you've saved your model
-    if os.path.exists(model_path):
-        model = load(model_path)
-        st.write("Model loaded successfully!")
-    else:
-        st.error("Model file not found.")
-    return model
-
-def classify_image(image, model):
-    image = np.array(image.convert('RGB'))
-    image_resized = resize(image, (128, 128), anti_aliasing=True)
-    image_reshaped = image_resized.reshape(-1, image_resized.shape[-1])
-    prediction = model.predict(image_reshaped)
-    return prediction[0]
-
-
-model = load_model()  # Load the model on startup
-
-# Mapping of Monk classes to colors
-class_colors= {
-    '1': [2, 3, 4],
-    '2': [5, 6],
-    '3': [7, 8],
-    '4': [9, 10],
-}
-
-
-# Function to display the Monk class color
-def display_monk_class_color(prediction):
-    color = monk_colors.get(prediction, 'gray')  # Default to gray if class not found
-    st.write(f"Monk Class: {prediction}")
-    st.markdown(f"<div style='width:100px; height:50px; background-color:{color};'></div>", unsafe_allow_html=True)
-
-st.title('Skin Tone Classification')
-
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-if uploaded_file is not None:
-    image = Image.open(uploaded_file)
-    st.image(image, caption='Uploaded Image.', use_column_width=True)
-
-    if st.button('Classify'):
-        prediction = classify_image(image, model)
-        #display_monk_class_color(prediction)