Create app.py

app.py

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+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")
+    
+    # Get available teams
+    schedule = get_schedule()
+    teams = {team['team']['id']: team['team']['name'] 
+             for game in schedule['dates'][0]['games'] 
+             for team in [game['teams']['home']['team'], game['teams']['away']['team']]}
+    
+    col1, col2 = st.columns(2)
+    with col1:
+        team_id = st.selectbox("Select Team", options=teams.keys(), format_func=lambda x: teams[x])
+    
+    with col2:
+        roster = get_roster(team_id)['roster']
+        players = {p['person']['id']: p['person']['fullName'] for p in roster}
+        player_id = st.selectbox("Select Prospect", options=players.keys(), format_func=lambda x: players[x])
+    
+    if st.button("Analyze Prospect"):
+        stats = get_player_stats(player_id)
+        # Add your ML model here (placeholder example)
+        st.success(f"Predicted WAR in 3 years: 4.2 (Sample Output)")
+        st.json(stats)  # Show raw data for inspection
+
+
+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()