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dbbd0f0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | import joblib
import streamlit as st
import pandas as pd
# Load the pipeline
pipeline = joblib.load('src/mobile_price_classification.pkl')
# Streamlit app
st.title(":iphone: Mobile Price Classification")
st.write("Predict the price range of a mobile phone based on its features.")
st.image("https://img.freepik.com/premium-vector/cartoon-hand-holding-mobile-smart-phone-with-celebratory-confetti-flying-around-winner-concept_3482-5775.jpg", width='stretch')
# Class legend
CLASS_LABELS = {
0: "0 (low cost)",
1: "1 (medium cost)",
2: "2 (high cost)",
3: "3 (very high cost)",
}
# Create input form
col1, col2 = st.columns(2)
with col1:
battery_power = st.number_input(
"Battery Power (mAh)", min_value=500, max_value=5000, value=1500, step=50
)
int_memory = st.number_input(
"Internal Memory (GB)", min_value=2, max_value=512, value=32, step=2
)
mobile_wt = st.number_input(
"Mobile Weight (g)", min_value=80, max_value=300, value=150, step=1
)
px_height = st.number_input(
"Pixel Height", min_value=0, max_value=2000, value=800, step=10
)
with col2:
px_width = st.number_input(
"Pixel Width", min_value=0, max_value=2500, value=1280, step=10
)
ram = st.number_input(
"RAM (MB)", min_value=256, max_value=8192, value=4096, step=128
)
talk_time = st.number_input(
"Talk Time (hours)", min_value=2, max_value=24, value=15, step=1
)
touch_screen = st.selectbox(
"Touch Screen", options=[0, 1], index=1, help="1 = Yes, 0 = No"
)
# Create a button to predict the price range
if st.button("Predict Price Range", type="primary", use_container_width=True):
# Create a DataFrame with the input features
input_data = pd.DataFrame({
'battery_power': [battery_power],
'int_memory': [int_memory],
'mobile_wt': [mobile_wt],
'px_height': [px_height],
'px_width': [px_width],
'ram': [ram],
'talk_time': [talk_time],
'touch_screen': [touch_screen]
})
# Predict the price range
prediction = pipeline.predict(input_data)
predicted_label = int(prediction[0])
predicted_text = CLASS_LABELS.get(predicted_label, str(predicted_label))
st.subheader("Prediction")
st.success(f":moneybag: **{predicted_text}**")
with st.expander("Class legend", expanded=True):
st.markdown("""
- 0 (low cost)
- 1 (medium cost)
- 2 (high cost)
- 3 (very high cost)
""")
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