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# app.py
from flask import Flask, request, jsonify
from xgboost import XGBRegressor
import pandas as pd
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) |