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app.py

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+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]}')

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:42efa29ecddf7b1302f818f15f6041c5ddec94949e11a7ce19716ee367486bf1
+size 241

requirements.txt

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+pandas
+streamlit
+pickle
+numpy
+seaborn
+matplotlib