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app.py

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+import streamlit as st
+from joblib import load
+
+# Load model
+Model = load('svm.joblib')
+
+# Prediction function
+def predict_note_authentication(values):
+    prediction = Model.predict([values])
+    return prediction[0]
+
+def main():
+    st.title("Mobile Price Classification ML App")
+    st.markdown("""
+        <div style="background-color:tomato;padding:10px">
+        <h2 style="color:white;text-align:center;">Predict the Price Range of a Mobile</h2>
+        </div>
+    """, unsafe_allow_html=True)
+    
+    # Collect inputs
+    battery_power = st.number_input('Battery Power (mAh)', min_value=500, max_value=2000, step=10)
+    
+    colour = st.selectbox('Colour', ['Blue', 'Other'])
+    colour = 1 if colour == 'Blue' else 0
+    
+    clock_speed = st.number_input('Clock Speed (GHz)', min_value=0.5, max_value=3.0, step=0.1)
+    
+    sim = st.selectbox('SIM Option', ['Dual', 'Single'])
+    sim = 1 if sim == 'Dual' else 0
+    
+    front_camera = st.number_input('Front Camera Resolution (MP)', min_value=0, max_value=20, step=1)
+    
+    network_4G = st.selectbox('Network  (4G)', ['yes', 'no'])
+    four_g = 1 if network_4G == '4G' else 0
+    
+    network_3G = st.selectbox('Network  (3G)', ['yes', 'no'])
+    three_g = 1 if network_3G == '3G' else 0
+    
+    int_memory = st.number_input('Internal Memory (GB)', min_value=2, max_value=64, step=1)
+    
+    mobile_depth = st.selectbox('Mobile Depth (cm)', [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])
+    
+    mobile_wt = st.number_input('Mobile Weight (grams)', min_value=80, max_value=200, step=1)
+    
+    n_cores = st.selectbox('Number of Cores', [1, 2, 3, 4, 5, 6, 7, 8])
+    
+    primary_camera = st.number_input('Primary Camera Resolution (MP)', min_value=0, max_value=20, step=1)
+    
+    px_height = st.number_input('Screen Pixel Height', min_value=0, max_value=2000, step=10)
+    px_width = st.number_input('Screen Pixel Width', min_value=100, max_value=2000, step=10)
+    
+    ram = st.number_input('RAM (MB)', min_value=256, max_value=4000, step=10)
+    
+    screen_height = st.number_input('Screen Height (cm)', min_value=0, max_value=20, step=1)
+    screen_width = st.number_input('Screen Width (cm)', min_value=0, max_value=20, step=1)
+    
+    talk_time = st.number_input('Talk Time (hours)', min_value=1, max_value=20, step=1)
+    
+    touch_screen = st.selectbox('Touch Screen', ['Yes', 'No'])
+    touch_screen = 1 if touch_screen == 'Yes' else 0
+    
+    wifi = st.selectbox('Wi-Fi Support', ['Yes', 'No'])
+    wifi = 1 if wifi == 'Yes' else 0
+
+    # Prepare input for the model
+    values = [
+        battery_power, colour, clock_speed, sim, front_camera, four_g, int_memory, mobile_depth, mobile_wt,
+        n_cores, primary_camera, px_height, px_width, ram, screen_height, screen_width, talk_time, three_g,
+        touch_screen, wifi
+    ]
+
+    # Predict and display result
+    if st.button("Predict"):
+        result = predict_note_authentication(values)
+        st.success(f'Predicted Price Class: {result}')
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+joblib==1.3.2
+matplotlib==3.8.3
+matplotlib-inline==0.1.6
+numpy==1.26.4
+pandas==2.2.
+scikit-learn==1.4.1.post1
+seaborn==0.13.2
+stack-data==0.6.3
+streamlit==1.32.2
+streamlit-chat==0.1.1
+

svm.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:65d3310193cb50dd365b92f14959ce9553c7fb5829159f691e6b51110b7e2d5a
+size 146981