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App.py

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+import joblib
+import gradio as gr
+from datetime import datetime, timedelta, timezone
+
+model = joblib.load('model.pkl')
+
+def preprocess_city(selected_city):
+    # Map the selected city to its one-hot encoded representation
+    city_mapping = {
+        'Indore': [1, 0, 0, 0, 0, 0, 0],
+        'Jaipur': [0, 1, 0, 0, 0, 0, 0],
+        'Mahabaleshwar': [0, 0, 1, 0, 0, 0, 0],
+        'Mussoorie': [0, 0, 0, 1, 0, 0, 0],
+        'Raipur': [0, 0, 0, 0, 1, 0, 0],
+        'Udaipur': [0, 0, 0, 0, 0, 1, 0],
+        'Varanasi': [0, 0, 0, 0, 0, 0, 1]
+    }
+    return city_mapping[selected_city]
+
+def preprocess_date(date_string):
+    # Parse the date string into a datetime object
+    date_obj = datetime.strptime(date_string, '%Y-%m-%d')
+    year = date_obj.year
+    month = date_obj.month
+    day = date_obj.day
+    return year, month, day
+
+def calculate_lead_time(checkin_date):
+    # Convert input date to datetime object
+    input_date = datetime.strptime(checkin_date, '%Y-%m-%d')
+    
+    # Get current date and time in GMT+5:30 timezone
+    current_date = datetime.now(timezone(timedelta(hours=5, minutes=30)))
+    
+    # Make current_date an aware datetime with the same timezone
+    current_date = current_date.replace(tzinfo=input_date.tzinfo)
+    
+    # Calculate lead time as difference in days
+    lead_time = (input_date - current_date).days
+    
+    return lead_time
+
+def is_weekend(checkin_date):
+    # Convert input date to datetime object
+    input_date = datetime.strptime(checkin_date, '%Y-%m-%d')
+    
+    # Calculate the day of the week (0=Monday, 6=Sunday)
+    day_of_week = input_date.weekday()
+    
+    # Check if the day is Friday (4) or Saturday (5)
+    return 1 if day_of_week == 4 or day_of_week == 5 else 0
+
+def predict(selected_city, checkin_date, star_rating, text_rating, season, additional_views, room_category):
+    # Preprocess user input
+    # Here, selected_city is the name of the city selected from the dropdown
+    # checkin_date is the date selected using the text input
+    # star_rating is the selected star rating from the dropdown
+    # text_rating is the numeric rating from the text box
+    # season is the selected option from the radio button (On Season or Off Season)
+    season_binary = 1 if season == 'On Season' else 0
+    # additional_views is the selected option from the radio button (Yes or No)
+    additional_views_binary = 1 if additional_views == 'Yes' else 0
+    
+    room_categories = ["Dorm", "Standard", "Deluxe", "Executive", "Suite"]
+    room_category_number = room_categories.index(room_category)
+    
+    # Preprocess the date
+    year, month, day = preprocess_date(checkin_date)
+    
+    # Preprocess the selected city
+    city_encoded = preprocess_city(selected_city)
+    
+    # Calculate lead time
+    lead_time = calculate_lead_time(checkin_date)
+    
+    # Calculate if the input date is a weekend (1) or weekday (0)
+    is_weekend_value = is_weekend(checkin_date)
+    
+    # Combine all the input features
+    input_data = [star_rating, text_rating, season_binary, day, month, year, is_weekend_value, lead_time,room_category_number, additional_views_binary]+city_encoded
+
+    # Make predictions using the model
+    prediction = model.predict([input_data])
+    return "{:.2f}".format(prediction[0])
+
+# Define input components
+city_dropdown = gr.components.Dropdown(choices=['Indore', 'Jaipur', 'Mahabaleshwar', 'Mussoorie', 'Raipur', 'Udaipur', 'Varanasi'], label='Select a City')
+date_input = gr.components.Textbox(label='Check-in Date (YYYY-MM-DD)')
+star_rating_dropdown = gr.components.Dropdown(choices=[1, 2, 3, 4, 5], label='Select Star Rating')
+text_rating_input = gr.components.Number(label='Enter Numeric Rating (1-5)')
+season_radio = gr.components.Radio(['On Season', 'Off Season'], label='Season')
+room_category_dropdown = gr.components.Dropdown(choices=["Dorm", "Standard", "Deluxe", "Executive", "Suite"], label='Select Room Category')
+additional_views_radio = gr.components.Radio(['Yes', 'No'], label='Additional Views')
+
+# Define output component
+output = gr.components.Textbox(label='Predicted Output')
+
+# Create the interface
+interface = gr.Interface(fn=predict, inputs=[city_dropdown, date_input, star_rating_dropdown, text_rating_input, season_radio, additional_views_radio, room_category_dropdown], outputs=output, title='Model Prediction Interface')
+
+# Launch the interface
+interface.launch()
+

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:01e42fa7ca9d6fd55b9eb98d8da9c9d2d24a958fa112cce01ce77e7ee2438290
+size 381227006