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import pandas as pd
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import requests
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import joblib
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from datetime import datetime
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API_KEY = 'a4f54718b17aa482e0b0a9f2e6220fc0'
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WEATHER_CACHE = {}
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SEASON_MAP = {1: 'Winter', 2: 'Winter', 12: 'Winter',
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3: 'Spring', 4: 'Spring', 5: 'Spring',
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6: 'Summer', 7: 'Summer', 8: 'Summer',
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9: 'Fall', 10: 'Fall', 11: 'Fall'}
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def fetch_weather(city, state, api_key=API_KEY):
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key = f"{city},{state}"
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if key in WEATHER_CACHE:
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return WEATHER_CACHE[key]
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geo_url = f"http://api.openweathermap.org/geo/1.0/direct?q={city},{state},US&limit=1&appid={api_key}"
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try:
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geo_resp = requests.get(geo_url)
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geo_resp.raise_for_status()
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geo_data = geo_resp.json()
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if not geo_data:
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return {'temperature': 20, 'humidity': 50, 'condition': 'Clear'}
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lat, lon = geo_data[0]['lat'], geo_data[0]['lon']
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weather_url = f"https://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={api_key}&units=metric"
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weather_resp = requests.get(weather_url)
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weather_resp.raise_for_status()
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data = weather_resp.json()
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weather = {
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'temperature': data['main']['temp'],
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'humidity': data['main']['humidity'],
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'condition': data['weather'][0]['main']
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}
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WEATHER_CACHE[key] = weather
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return weather
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except Exception as e:
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print(f"Weather fetch error for {city}, {state}: {e}")
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return {'temperature': 20, 'humidity': 50, 'condition': 'Clear'}
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def extract_season(month):
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return SEASON_MAP.get(month, 'Unknown')
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def predict_discount(product_serial, city, state, order_date, segment, ship_mode, df, model):
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product = df[df['Product ID'] == product_serial]
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if product.empty:
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print(f"Product serial {product_serial} not found.")
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return None
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weather = fetch_weather(city, state)
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try:
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order_dt = pd.to_datetime(order_date, dayfirst=True)
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except Exception:
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print("Invalid order date format. Use YYYY-MM-DD.")
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return None
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order_month = order_dt.month
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order_day_of_week = order_dt.dayofweek
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season = extract_season(order_month)
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X_pred = pd.DataFrame({
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'Category': [product.iloc[0]['Category']],
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'Sub-Category': [product.iloc[0]['Sub-Category']],
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'Product ID': [product_serial],
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'Sales': [product.iloc[0]['Sales']],
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'City': [city],
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'State': [state],
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'Segment': [segment],
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'Ship Mode': [ship_mode],
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'order_month': [order_month],
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'order_day_of_week': [order_day_of_week],
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'season': [season],
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'temperature': [weather['temperature']],
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'humidity': [weather['humidity']],
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'condition': [weather['condition']]
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})
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discount = model.predict(X_pred)[0]
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return max(0, round(discount, 2))
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if __name__ == "__main__":
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print("Loading model and data...")
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model = joblib.load('discount_model.joblib')
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df = pd.read_csv('train.csv')
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serial = input("Enter product serial (Product ID): ")
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city = input("Enter city: ")
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state = input("Enter state: ")
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order_date = input("Enter order date (YYYY-MM-DD): ")
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segment = input("Enter customer segment (e.g., Consumer, Corporate, Home Office): ")
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ship_mode = input("Enter ship mode (e.g., First Class, Second Class, Standard Class, Same Day): ")
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discount = predict_discount(serial, city, state, order_date, segment, ship_mode, df, model)
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if discount is not None:
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print(f"Predicted discount for product {serial} in {city}, {state} on {order_date}: {discount}%") |
