Merge pull request #109 from arhaanarif/main

Food Delivery Time Prediction Model

Prediction Models/Food Delivery Time Prediction/main.py

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+import pickle
+import pandas as pd
+import streamlit as st
+import numpy as np
+from geopy.distance import geodesic
+from sklearn.preprocessing import LabelEncoder
+import requests
+
+
+with open('scaler.pkl', 'rb') as f:
+    scaler = pickle.load(f)
+
+with open('model.pkl', 'rb') as f:
+    model = pickle.load(f)
+
+
+
+def extract_column_value(df):
+    # #Extract time and convert to int
+    # df['Time_taken(min)'] = df['Time_taken(min)'].apply(lambda x: int(x.split(' ')[1].strip()))
+    #Extract Weather conditions
+    # df['Weather_conditions'] = df['Weather_conditions'].apply(lambda x: x.split(' ')[1].strip())
+    #Extract city code from Delivery person ID
+    df['City_code']=df['Delivery_person_ID'].str.split("RES", expand=True)[0]
+
+
+
+
+
+def extract_date_features(data):
+    data["day"] = data.Order_Date.dt.day
+    data["month"] = data.Order_Date.dt.month
+    data["quarter"] = data.Order_Date.dt.quarter
+    data["year"] = data.Order_Date.dt.year
+    data['day_of_week'] = data.Order_Date.dt.day_of_week.astype(int)
+    data["is_month_start"] = data.Order_Date.dt.is_month_start.astype(int)
+    data["is_month_end"] = data.Order_Date.dt.is_month_end.astype(int)
+    data["is_quarter_start"] = data.Order_Date.dt.is_quarter_start.astype(int)
+    data["is_quarter_end"] = data.Order_Date.dt.is_quarter_end.astype(int)
+    data["is_year_start"] = data.Order_Date.dt.is_year_start.astype(int)
+    data["is_year_end"] = data.Order_Date.dt.is_year_end.astype(int)
+    data['is_weekend'] = np.where(data['day_of_week'].isin([5,6]),1,0)
+
+
+def calculate_time_diff(df):
+    # Find the difference between ordered time & picked time
+    df['Time_Orderd'] = pd.to_timedelta(df['Time_Orderd'])
+
+    df['Time_Order_picked'] = pd.to_timedelta(df['Time_Order_picked'])
+
+    df['Time_Order_picked_formatted'] = df['Order_Date'] + np.where(df['Time_Order_picked'] < df['Time_Orderd'], pd.DateOffset(days=1), pd.DateOffset(days=0)) +     df['Time_Order_picked']
+    df['Time_Ordered_formatted'] = df['Order_Date'] + df['Time_Orderd']
+
+    df['Time_Order_picked_formatted']=pd.to_datetime(df['Time_Order_picked_formatted'])
+
+    df['order_prepare_time'] = (df['Time_Order_picked_formatted'] - df['Time_Ordered_formatted']).dt.total_seconds() / 60
+
+    # Handle null values by filling with the median
+    df['order_prepare_time'].fillna(df['order_prepare_time'].median(), inplace=True)
+
+    # Drop all the time & date related columns
+    df.drop(['Time_Orderd', 'Time_Order_picked', 'Time_Ordered_formatted', 'Time_Order_picked_formatted', 'Order_Date'], axis=1, inplace=True)
+
+
+
+#Calculate distance between restaurant location & delivery location
+def calculate_distance(df):
+    df['distance']=np.zeros(len(df))
+    restaurant_coordinates=df[['Restaurant_latitude','Restaurant_longitude']].to_numpy()
+    delivery_location_coordinates=df[['Delivery_location_latitude','Delivery_location_longitude']].to_numpy()
+    df['distance'] = np.array([geodesic(restaurant, delivery) for restaurant, delivery in zip(restaurant_coordinates, delivery_location_coordinates)])
+    df['distance']= df['distance'].astype("str").str.extract('(\d+)').astype("int64")
+
+def label_encoding(df):
+    categorical_columns = df.select_dtypes(include='object').columns
+    label_encoder = LabelEncoder()
+    df[categorical_columns] = df[categorical_columns].apply(lambda col: label_encoder.fit_transform(col))
+
+
+# Define Streamlit app
+st.title('Delivery Time Prediction')
+
+# User input for each feature
+delivery_person_id = st.text_input('Delivery Person ID', 'BANGRES19DEL01')
+age = st.number_input('Delivery Person Age', min_value=18, max_value=65, value=30)
+ratings = st.number_input('Delivery Person Ratings', min_value=1.0, max_value=5.0, value=4.5)
+order_date = st.date_input('Order Date')
+time_ordered = st.time_input('Time Ordered')
+time_order_picked = st.time_input('Time Order Picked')
+weather = st.selectbox('Weather Conditions', ['Sunny', 'Cloudy', 'Rainy', 'Foggy'])
+traffic = st.selectbox('Road Traffic Density', ['Low', 'Medium', 'High', 'Jam'])
+vehicle_condition = st.number_input('Vehicle Condition', min_value=0, max_value=10, value=7)
+order_type = st.selectbox('Type of Order', ['Snack', 'Meal', 'Drinks', 'Buffet'])
+vehicle_type = st.selectbox('Type of Vehicle', ['Bike', 'Scooter', 'Car', 'Truck'])
+multiple_deliveries = st.number_input('Multiple Deliveries', min_value=0, max_value=5, value=0)
+festival = st.selectbox('Festival', ['No', 'Yes'])
+city = st.selectbox('City', ['Urban', 'Semi-Urban', 'Metropolitan'])
+
+restaurant_address = st.text_input('Restaurant Address')
+delivery_address = st.text_input('Delivery Address')
+
+
+def get_lat_long_opencage(address, api_key):
+    url = f"https://api.opencagedata.com/geocode/v1/json?q={address}&key={api_key}"
+    response = requests.get(url)
+
+    if response.status_code == 200:
+        data = response.json()
+        if data["results"]:
+            latitude = data["results"][0]["geometry"]["lat"]
+            longitude = data["results"][0]["geometry"]["lng"]
+            return latitude, longitude
+        else:
+            return None, None
+    else:
+        return None, None
+
+# Example usage
+api_key = "3e2ac5fd3c164874afdb2bd78e0d6268"  # Replace with your OpenCage API key
+
+restaurant_lat,restaurant_long = get_lat_long_opencage(restaurant_address, api_key)
+delivery_lat,delivery_long = get_lat_long_opencage(delivery_address, api_key)
+
+def set_background_image(image_url):
+    st.markdown(
+        f"""
+        <style>
+        .stApp {{
+            background: url({image_url});
+            background-size: cover;
+            background-repeat: no-repeat;
+            background-attachment: fixed;
+        }}
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+# Call function to set the background image
+# Replace 'your_image_url' with the URL of your image or path to your local image
+set_background_image('https://zeew.eu/wp-content/uploads/2024/06/Restaurant-Online-Food-Delivery-1.webp')
+
+if st.button("Get ETA for Delivery!"):
+    # Prepare input data for the model
+    input_data = pd.DataFrame({
+        'Delivery_person_ID': [delivery_person_id],
+        'Delivery_person_Age': [age],
+        'Delivery_person_Ratings': [ratings],
+        'Restaurant_latitude': [restaurant_lat],
+        'Restaurant_longitude': [restaurant_long],
+        'Delivery_location_latitude': [delivery_lat],
+        'Delivery_location_longitude': [delivery_long],
+        'Order_Date': [order_date],
+        'Time_Orderd': [time_ordered],
+        'Time_Order_picked': [time_order_picked],
+        'Weather_conditions': [weather],
+        'Road_traffic_density': [traffic],
+        'Vehicle_condition': [vehicle_condition],
+        'Type_of_order': [order_type],
+        'Type_of_vehicle': [vehicle_type],
+        'multiple_deliveries': [multiple_deliveries],
+        'Festival': [festival],
+        'City': [city]
+    })
+
+
+    input_data['Order_Date'] = pd.to_datetime(input_data['Order_Date'])
+    input_data['Order_Year'] = input_data['Order_Date'].dt.year
+    input_data['Order_Month'] = input_data['Order_Date'].dt.month
+    input_data['Order_Day'] = input_data['Order_Date'].dt.day
+    input_data['Time_Orderd'] = pd.to_datetime(input_data['Time_Orderd'], format='%H:%M:%S').dt.hour
+    input_data['Time_Order_picked'] = pd.to_datetime(input_data['Time_Order_picked'], format='%H:%M:%S').dt.hour
+
+
+
+    extract_column_value(input_data)
+    extract_date_features(input_data)
+    calculate_time_diff(input_data)
+    calculate_distance(input_data)
+    label_encoding(input_data)
+
+
+
+
+
+    input_data=input_data.drop(['Order_Year', 'Order_Month', 'Order_Day','Delivery_person_ID'],axis=1)
+
+    # Scale the input data using the loaded scaler
+    scaled_data = scaler.transform(input_data)
+
+    # Make prediction using the loaded model
+    prediction = model.predict(scaled_data)
+
+    # Display the prediction
+    st.write(f'Your Food will arrive in minutes): {prediction[0]}')
+
+
+
+