app.py

import streamlit as st
import pickle
import numpy as np

# Load the saved SVC model
with open('svc_model.pkl', 'rb') as file:
    svc_model = pickle.load(file)

# Define a function to make predictions
def predict_shot_accuracy(inputs):
    # Reshape inputs for the model
    inputs = np.array(inputs).reshape(1, -1)
    # Predict probability of the shot being a basket
    probability = svc_model.predict_proba(inputs)
    return probability[0][1]  # Probability of the positive class (making the basket)

# Streamlit app
st.title('Kobe Shot Accuracy Predictor 🏀')
st.image("https://img.bleacherreport.net/img/images/photos/003/159/114/hi-res-179796f005257238c44afad0c64e1432_crop_north.jpg?1416296596&w=3072&h=2048")
st.write('The model is trained with LinearSVC on 25K shots of Kobe Bryant.')

# Input fields for user to fill in shot details
shot_distance = st.number_input('Shot Distance in feet', min_value=0, max_value=30, value=24)

# Dropdown for combined shot type
combined_shot_type = st.selectbox('Combined Shot Type', [
    'Bank Shot', 'Dunk', 'Hook Shot', 'Jump Shot', 'Layup', 'Tip Shot'
], index=3)  # Default to 'Jump Shot'

# Convert the combined shot type to binary features
combined_shot_type_features = {
    'Bank Shot': [True, False, False, False, False, False],
    'Dunk': [False, True, False, False, False, False],
    'Hook Shot': [False, False, True, False, False, False],
    'Jump Shot': [False, False, False, True, False, False],
    'Layup': [False, False, False, False, True, False],
    'Tip Shot': [False, False, False, False, False, True]
}[combined_shot_type]

# Dropdown for shot type
shot_type = st.selectbox('Shot Type', ['2PT Field Goal', '3PT Field Goal'], index=1)  # Default to '3PT Field Goal'

# Convert shot type to binary features
shot_type_2PT_Field_Goal = shot_type == '2PT Field Goal'
shot_type_3PT_Field_Goal = shot_type == '3PT Field Goal'

# Prepare the input data
input_data = [
    shot_distance,
    *combined_shot_type_features,
    shot_type_2PT_Field_Goal,
    shot_type_3PT_Field_Goal
]

# Make prediction when user submits
if st.button('Predict Shot Accuracy'):
    probability = predict_shot_accuracy(input_data)
    st.write(f'The probability of making the basket is {probability:.2f}%.')
    
    # Display a title for the images
    st.write("Here are some bonus data visualizations about Kobe's Shots ⛹🏾")
    
    # Display images
    for i in range(1, 11):
        st.image(f'{i}.png')