app.py

import streamlit as st
import requests
import pandas as pd

st.set_page_config(page_title="MLB Prospect Pulse", layout="wide")

# Title and description
st.title("⚾ MLB Prospect Prediction Toolkit")
st.markdown("""
**Challenge 5 Solution**  
Predict prospect potential, compare players, and explore scenarios using MLB's GUMBO data.
""")

@st.cache_data
def get_schedule(season=2024):
    url = f"https://statsapi.mlb.com/api/v1/schedule?sportId=1&season={season}"
    return requests.get(url).json()

@st.cache_data
def get_roster(team_id=119, season=2024):  # Default: LA Dodgers
    url = f"https://statsapi.mlb.com/api/v1/teams/{team_id}/roster?season={season}"
    return requests.get(url).json()

@st.cache_data
def get_player_stats(player_id):
    url = f"https://statsapi.mlb.com/api/v1/people/{player_id}?hydrate=stats"
    return requests.get(url).json()


def prediction_section():
    st.header("🧠 Prospect Potential Prediction")
    
    try:
        # Get available teams with error handling
        schedule = get_schedule()
        teams = {}
        
        # Safely navigate through possible missing keys
        if 'dates' in schedule and len(schedule['dates']) > 0:
            for date in schedule['dates']:
                if 'games' in date:
                    for game in date['games']:
                        for team_type in ['home', 'away']:
                            team = game['teams'][team_type].get('team', {})
                            team_id = team.get('id')
                            team_name = team.get('name')
                            if team_id and team_name:
                                teams[team_id] = team_name
        
        if not teams:
            st.warning("No team data available. Using default teams.")
            teams = {119: "Los Angeles Dodgers"}  # Fallback
            
        col1, col2 = st.columns(2)
        with col1:
            team_id = st.selectbox("Select Team", options=teams.keys(), format_func=lambda x: teams[x])
        
        with col2:
            try:
                roster = get_roster(team_id).get('roster', [])
                players = {}
                for p in roster:
                    person = p.get('person', {})
                    players[person.get('id')] = person.get('fullName', 'Unknown Player')
                
                if not players:
                    st.error("No players found for this team")
                    return
                    
                player_id = st.selectbox("Select Prospect", options=players.keys(), format_func=lambda x: players[x])
            
            except Exception as e:
                st.error(f"Error loading roster: {str(e)}")
                return

        if st.button("Analyze Prospect"):
            try:
                stats = get_player_stats(player_id)
                # Add proper error handling for stats data
                if not stats:
                    st.error("No player stats available")
                    return
                
                # Placeholder for actual model prediction
                st.success(f"Predicted WAR in 3 years: 4.2 (Sample Output)")
                st.json(stats)  # Show raw data for inspection
                
            except Exception as e:
                st.error(f"Analysis failed: {str(e)}")

    except Exception as e:
        st.error(f"Failed to load schedule data: {str(e)}")


def comparison_tool():
    st.header("🔍 Player Comparison")
    
    # Get 2 players to compare
    roster = get_roster()['roster']
    players = {p['person']['id']: p['person']['fullName'] for p in roster}
    
    col1, col2 = st.columns(2)
    with col1:
        p1 = st.selectbox("Player 1", options=players.keys(), format_func=lambda x: players[x])
    with col2:
        p2 = st.selectbox("Player 2", options=players.keys(), format_func=lambda x: players[x])
    
    if p1 and p2:
        stats1 = get_player_stats(p1)
        stats2 = get_player_stats(p2)
        
        # Create comparison table (customize with actual metrics)
        comparison = pd.DataFrame({
            "Metric": ["Age", "BA", "HR", "SO"],
            players[p1]: [25, 0.285, 32, 110],  # Sample data
            players[p2]: [23, 0.265, 28, 95]
        }).set_index("Metric")
        
        st.dataframe(comparison, use_container_width=True)


def what_if_scenarios():
    st.header("🎮 What-If Analysis")
    
    # Interactive sliders
    col1, col2, col3 = st.columns(3)
    with col1:
        age = st.slider("Age", 18, 35, 22)
    with col2:
        ba = st.slider("Batting Average", 0.150, 0.400, 0.275)
    with col3:
        hr = st.slider("Projected HR", 0, 60, 25)
    
    # Add your model simulation here
    simulated_war = age * 0.1 + ba * 10 + hr * 0.2  # Example calculation
    st.metric("Simulated Future WAR", f"{simulated_war:.1f}")


def main():
    tabs = {
        "Prediction": prediction_section,
        "Comparison": comparison_tool,
        "What-If": what_if_scenarios
    }
    
    current_tab = st.sidebar.radio("Navigation", list(tabs.keys()))
    tabs[current_tab]()
    
if __name__ == "__main__":
    main()