Update app.py

app.py

@@ -7,9 +7,19 @@ from flask import Flask, request, jsonify
 
 # Step 1: Data Collection
 def fetch_data():
-    api_key = 'YOUR_API_KEY'  # Replace with your SportsData.io API key
+    api_key = 'itku7CjwJv5bfrwAvGlwR3nYv'  # Replace with your SportsData.io API key
     response = requests.get(f"https://api.sportsdata.io/v3/nba/stats/json/PlayerSeasonStats/2023?key={api_key}")
+
+    # Check if the response is valid
+    if response.status_code != 200:
+        raise ValueError(f"Error fetching data: {response.status_code}, {response.text}")
+
     data = response.json()
+    
+    # Check if the response is a list of dictionaries
+    if not isinstance(data, list) or not all(isinstance(i, dict) for i in data):
+        raise ValueError("API response is not in the expected format (list of dictionaries)")
+
     return pd.DataFrame(data)
 
 # Step 2: Data Preprocessing
@@ -43,6 +53,33 @@ def train_model(df):
 # Step 5: Deployment with Flask
 app = Flask(__name__)
 
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.json
+    input_features = [data['RecentForm'], data['HomeAdvantage']]
+    prediction = model.predict([input_features])[0]
+    return jsonify({'prediction': prediction})
+
+if __name__ == '__main__':
+    # Fetch and preprocess data
+    df = fetch_data()
+    df = preprocess_data(df)
+    df = engineer_features(df)
+    
+    # Train the model
+    model = train_model(df)
+    
+    # Run the Flask app
+    app.run(debug=True, host='0.0.0.0', port=5000)
+    y_pred = model.predict(X_test)
+    mae = mean_absolute_error(y_test, y_pred)
+    print(f"Mean Absolute Error: {mae}")
+    
+    return model
+
+# Step 5: Deployment with Flask
+app = Flask(__name__)
+
 @app.route('/predict', methods=['POST'])
 def predict():
     data = request.json