Create app.py

app.py

@@ -0,0 +1,62 @@
+# app.py
+import pandas as pd
+from xgboost import XGBRegressor
+from sklearn.model_selection import train_test_split
+import requests
+import os
+
+# Fetch data from Facebook API
+def fetch_data_from_api(query, geo_locations):
+    url = f"https://graph.facebook.com/v17.0/act_597540533213624/targetingsearch"
+    params = {
+        "q": query,
+        "geo_locations[countries]": geo_locations,
+        "access_token": os.getenv('ACCESS_TOKEN')
+    }
+    response = requests.get(url, params=params)
+    if response.status_code == 200:
+        return response.json()
+    else:
+        raise Exception(f"Failed to fetch data from API. Status code: {response.status_code}")
+
+# Generate synthetic metrics
+def generate_synthetic_metrics(data):
+    IMPRESSION_RATE = 0.10  # 10% of audience sees the ad
+    CTR = 0.05  # 5% of impressions result in clicks
+    CONVERSION_RATE = 0.02  # 2% of clicks result in conversions
+    CPM = 5  # $5 per 1000 impressions
+    REVENUE_PER_CONVERSION = 50  # $50 per conversion
+
+    data['impressions'] = data['audience_size_lower_bound'] * IMPRESSION_RATE
+    data['clicks'] = data['impressions'] * CTR
+    data['conversions'] = data['clicks'] * CONVERSION_RATE
+    data['ad_spend'] = (data['impressions'] / 1000) * CPM
+    data['revenue'] = data['conversions'] * REVENUE_PER_CONVERSION
+    data['roi'] = (data['revenue'] - data['ad_spend']) / data['ad_spend']
+
+    return data
+
+# Train and save the model
+def train_and_save_model():
+    # Fetch data
+    response_data = fetch_data_from_api('Fitness', 'NG')
+    data = pd.DataFrame(response_data['data'])
+
+    # Generate synthetic metrics
+    data = generate_synthetic_metrics(data)
+
+    # Features and target
+    X = data[['ad_spend', 'impressions', 'clicks', 'conversions']]
+    y = data['roi']
+
+    # Train the model
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    model = XGBRegressor(n_estimators=100, max_depth=3, n_jobs=-1)
+    model.fit(X_train, y_train)
+
+    # Save the model
+    model.save_model('model.json')
+    print("Model saved to 'model.json'.")
+
+if __name__ == '__main__':
+    train_and_save_model()