Update app.py

app.py

@@ -1,68 +1,26 @@
 # train_model.py
+from xgboost import XGBRegressor
 import pandas as pd
 from sklearn.model_selection import train_test_split
-from xgboost import XGBRegressor
-from sklearn.metrics import mean_squared_error
-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)
-
-    # Evaluate the model
-    predictions = model.predict(X_test)
-    mse = mean_squared_error(y_test, predictions)
-    print(f"Model trained successfully! Mean Squared Error: {mse}")
-
-    # Save the model
-    model.save_model('model.json')
-    print("Model saved to 'model.json'.")
 
-if __name__ == '__main__':
-    train_and_save_model()
+# Example synthetic data
+data = {
+    'ad_spend': [1000, 2000, 3000, 4000, 5000],
+    'impressions': [50000, 100000, 150000, 200000, 250000],
+    'clicks': [2500, 5000, 7500, 10000, 12500],
+    'conversions': [50, 100, 150, 200, 250],
+    'roi': [1.5, 2.0, 2.5, 3.0, 3.5]
+}
+df = pd.DataFrame(data)
+
+# Features and target
+X = df[['ad_spend', 'impressions', 'clicks', 'conversions']]
+y = df['roi']
+
+# Train the model
+model = XGBRegressor(n_estimators=100, max_depth=3, n_jobs=-1)
+model.fit(X, y)
+
+# Save the model
+model.save_model('model.json')
+print("Model saved to 'model.json'.")