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