Update app.py

app.py

@@ -1,26 +1,85 @@
-# train_model.py
+# app.py
+from flask import Flask, request, jsonify
 from xgboost import XGBRegressor
 import pandas as pd
-from sklearn.model_selection import train_test_split
-
-# 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'.")
+import requests
+import os
+
+app = Flask(__name__)
+
+# Load the pre-trained model
+model = XGBRegressor()
+model.load_model('model.json')
+
+# 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
+
+@app.route('/predict', methods=['GET'])
+def predict():
+    try:
+        # Get user input from query parameters
+        query = request.args.get('q', default='Fitness')  # Default query is 'Fitness'
+        geo_locations = request.args.get('geo_locations', default='NG')  # Default country is 'NG'
+
+        # Fetch data from Facebook API
+        response_data = fetch_data_from_api(query, geo_locations)
+
+        # Extract the list of dictionaries from the "data" key
+        if "data" in response_data and isinstance(response_data["data"], list):
+            data = pd.DataFrame(response_data["data"])
+
+            # Generate synthetic metrics
+            data = generate_synthetic_metrics(data)
+
+            # Use the first row of the data for prediction
+            input_data = data.iloc[0][['ad_spend', 'impressions', 'clicks', 'conversions']]
+
+            # Predict ROI
+            predicted_roi = model.predict([input_data])
+
+            # Return the prediction
+            return jsonify({
+                "ad_spend": input_data['ad_spend'],
+                "impressions": input_data['impressions'],
+                "clicks": input_data['clicks'],
+                "conversions": input_data['conversions'],
+                "predicted_roi": float(predicted_roi[0]),
+                "note": "These are recommendations based on real-world data. Actual results may vary."
+            })
+
+        else:
+            return jsonify({"error": "The 'data' key is missing or not a list in the API response."}), 400
+
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)