Delete app.py

app.py

@@ -1,35 +0,0 @@
-import streamlit as st
-import pandas as pd
-import pickle
-
-model = pickle.load(open('C:\\dasprog well\\fp_ise\\model.pkl', 'rb'))
-
-# Replace 'your_model_name' with the name you want for your model
-repo_url = create_repo(name='Almond Classification', private=False)
-
-st.title('Almond Classification')
-st.write('This web app classifies almonds based on your input features.')
-
-
-# Input untuk setiap fitur
-length_major_axis = st.number_input('Length (major axis)', min_value=0.0)
-width_minor_axis = st.number_input('Width (minor axis)', min_value=0.0)
-thickness_depth = st.number_input('Thickness (depth)', min_value=0.0)
-area = st.number_input('Area', min_value=0.0)
-perimeter = st.number_input('Perimeter', min_value=0.0)
-roundness = st.slider('Roundness', min_value=0.0, max_value=1.0, step=0.01)
-solidity = st.slider('Solidity', min_value=0.0, max_value=1.0, step=0.01)
-compactness =  st.slider('Compactness', min_value=0.0, max_value=1.0, step=0.01)
-aspect_ratio =  st.slider('Aspect Ratio', min_value=0.0, max_value=5.0, step=0.01)
-eccentricity = st.slider('Eccentricity', min_value=0.0, max_value=1.0, step=0.01)
-extent = st.slider('Extent', min_value=0.0, max_value=1.0, step=0.01)
-convex_area = st.number_input('Convex hull (convex area)', min_value=0.0, step=0.01)
-
-
-# Tombol untuk memprediksi
-if st.button('Predict'):
-    input_features = [[length_major_axis, width_minor_axis, thickness_depth, area,
-                       perimeter, roundness, solidity, compactness, aspect_ratio,
-                       eccentricity, extent, convex_area]]
-    prediction = model.predict(input_features)
-    st.write(f'The predicted class is: {prediction[0]}')