Upload 8 files

Cluster_Demand_model_df.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cee74eb255f909c6ca9cfb31aba91815493b0f25099f1bbb82fcb08d81c4814b
+size 46901559

app.py

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+import io
+import folium
+import joblib
+import datetime
+import numpy as np
+import gradio as gr
+import pandas as pd
+from PIL import Image
+import matplotlib.pyplot as plt
+
+
+# Load model and scaler
+lgb_model = joblib.load("lgb_occupancy_model.pkl")
+scaler = joblib.load("scaler.pkl")
+
+# Load data for historical patterns (you may want to refactor this)
+model_df = pd.read_pickle("Cluster_Demand_model_df.pkl")
+holiday_dates = pd.read_json("holidays_2022_2025.json")['date']
+holiday_dates = pd.to_datetime(holiday_dates).dt.normalize()
+holiday_dates = set(holiday_dates)
+feature_columns = joblib.load('feature_columns.pkl')
+
+# Load properties data
+properties_df = pd.read_csv('CTVNS_Properties.csv')
+properties_cols_to_keep = ['Property Name','Property ID', 'Star Rating', 'Property Type', 'Distance from Center','Latitude','Longitude']
+properties_filtered_df = properties_df[properties_cols_to_keep].copy()
+
+# Create dropdown options with property name or ID
+property_options = properties_filtered_df['Property Name'].astype(str).tolist()
+
+property_type_mapping = {
+     'Hotel': 9,
+     'Homestay': 7,
+     'Guest House': 5,
+     'Resort': 11,
+     'Hostel': 8,
+     'BnB': 2,
+     'Villa': 12,
+     'Apartment': 1,
+     'Apart-hotel': 0,
+     'Holiday Home': 6,
+     'Cottage': 3,
+     'Lodge': 10,
+     'Farm House': 4
+    }
+
+def forecast_by_property(property_name,adr):
+    # Find the selected row
+    selected_row = properties_filtered_df[properties_filtered_df['Property Name'].astype(str) == property_name]
+    if selected_row.empty:
+        return pd.DataFrame({'Error': ['Property Name not found.']})
+    
+    star_rating = int(selected_row['Star Rating'].values[0])
+    property_type_str = selected_row['Property Type'].values[0]
+    property_type_cat = property_type_mapping.get(property_type_str, -1)
+    distance = float(selected_row['Distance from Center'].values[0])
+    lat = selected_row['Latitude']
+    lon = selected_row['Longitude']
+
+    # Call your original forecast function
+    return forecast(star_rating, property_type_cat, distance,lat,lon,property_name,adr)
+
+def forecast_segment_all_features(starRating, propertyType_cat, distanceFromCenter, model_df, cutoff_date, end_date, scaler, lgb_model, full_feature_cols, X_train, holiday_dates, tolerance=0.1):
+    """Forecasts occupancy for a given segment."""
+    cluster_hist = model_df[
+        (model_df['starRating'] == starRating) &
+        (model_df['propertyType_cat'] == propertyType_cat) &
+        (np.abs(model_df['distanceFromCenter'] - distanceFromCenter) <= tolerance) &
+        (model_df['date'] <= cutoff_date)
+    ].sort_values('date')
+
+    if cluster_hist.empty:
+        print(f"Warning: No historical data found for segment ({starRating}, {propertyType_cat}, {distanceFromCenter}) up to {cutoff_date}.")
+        return None
+
+    extended_series = pd.DataFrame({'date': pd.date_range(start=cluster_hist['date'].min(), end=end_date)})
+    extended_series = extended_series.merge(cluster_hist[['date', 'occupiedRooms']], on='date', how='left').rename(columns={'occupiedRooms': 'occupied'})
+
+    for i in range(len(extended_series)):
+        current_date = extended_series.at[i, 'date']
+        if current_date <= pd.to_datetime(cutoff_date):
+            continue
+
+        day_of_week = current_date.dayofweek
+        day_of_year = current_date.timetuple().tm_yday
+        month = current_date.month
+        year = current_date.year
+
+        is_weekend = 1 if day_of_week in [4, 5] else 0
+        is_holiday = 1 if current_date in holiday_dates else 0
+
+        day_of_week_sin = np.sin(2 * np.pi * day_of_week / 7)
+        day_of_year_sin = np.sin(2 * np.pi * day_of_year / 365.25)
+        month_sin = np.sin(2 * np.pi * month / 12)
+        base_year = model_df['date'].dt.year.min()
+        year_scaled = year - base_year
+
+        lags = {}
+        for lag in [1, 7, 15]:
+            lags[f'lag_{lag}'] = extended_series.at[i - lag, 'occupied'] if i - lag >= 0 else np.nan
+
+        rolling_stats = {}
+        for window in [3, 7, 15]:
+            window_data = extended_series['occupied'].iloc[i - window:i] if i >= window else extended_series['occupied'].iloc[:i]
+            rolling_stats[f'rolling_{window}_mean'] = window_data.mean() if len(window_data) > 0 else np.nan
+            rolling_stats[f'rolling_{window}_std'] = window_data.std(ddof=0) if len(window_data) > 0 else np.nan
+
+        daily_change = extended_series.at[i, 'occupied'] - extended_series.at[i - 1, 'occupied'] if i > 0 and pd.notnull(extended_series.at[i - 1, 'occupied']) and pd.notnull(extended_series.at[i, 'occupied']) else np.nan
+
+        feature_vector = {
+            'starRating': starRating, 'distanceFromCenter': distanceFromCenter,
+            'day_of_week_sin': day_of_week_sin, 'day_of_year_sin': day_of_year_sin, 'month_sin': month_sin,
+            'year_scaled': year_scaled, 'is_weekend': is_weekend, 'is_holiday': is_holiday,
+            'lag_1': lags.get('lag_1', np.nan), 'lag_7': lags.get('lag_7', np.nan), 'lag_15': lags.get('lag_15', np.nan),
+            'rolling_3_mean': rolling_stats.get('rolling_3_mean', np.nan), 'rolling_3_std': rolling_stats.get('rolling_3_std', np.nan),
+            'rolling_7_mean': rolling_stats.get('rolling_7_mean', np.nan), 'rolling_7_std': rolling_stats.get('rolling_7_std', np.nan),
+            'rolling_15_mean': rolling_stats.get('rolling_15_mean', np.nan), 'rolling_15_std': rolling_stats.get('rolling_15_std', np.nan),
+            'daily_change': daily_change,
+        }
+        for j in range (10):
+            feature_vector[f'prop_type_{j}'] = 1 if propertyType_cat == j else 0
+
+        features = pd.DataFrame([feature_vector])
+        features = features.reindex(columns=feature_columns)
+        features.fillna(X_train.mean(numeric_only=True), inplace=True)  # or another imputation strategy
+        features_scaled = scaler.transform(features)
+        pred = lgb_model.predict(features_scaled)[0]
+        extended_series.at[i, 'occupied'] = pred
+        if i > 0:
+            extended_series.at[i, 'daily_change'] = pred - extended_series.at[i - 1, 'occupied']
+
+    future_df = extended_series[extended_series['date'] > pd.to_datetime(cutoff_date)].copy()
+    future_df['starRating'] = starRating
+    future_df['distanceFromCenter'] = distanceFromCenter
+    future_df['propertyType_cat'] = propertyType_cat
+    return future_df
+
+
+
+def forecast(starRating, propertyType_cat, distanceFromCenter,lat,lon,property_name,adr):
+    cutoff_date = datetime.datetime.today()
+    start_date = cutoff_date - pd.Timedelta(days=30)
+    end_date = cutoff_date + pd.Timedelta(days=30)
+
+    # Filter last 30 days of actuals from model_df
+    actual_df = model_df[
+        (model_df['starRating'] == starRating) &
+        (model_df['propertyType_cat'] == propertyType_cat) &
+        (model_df['distanceFromCenter'] == distanceFromCenter) &
+        (model_df['date'] >= start_date) &
+        (model_df['date'] <= cutoff_date)
+    ][['date', 'occupiedRooms']].copy()
+    actual_df.rename(columns={'occupiedRooms': 'occupied'}, inplace=True)
+    actual_df['source'] = 'Actual'
+    
+
+    # Forecast next 30 days
+    future_df = forecast_segment_all_features(
+        starRating=starRating,
+        propertyType_cat=propertyType_cat,
+        distanceFromCenter=distanceFromCenter,
+        model_df=model_df,
+        cutoff_date=cutoff_date,
+        end_date=end_date,
+        scaler=scaler,
+        lgb_model=lgb_model,
+        full_feature_cols=None,
+        X_train=model_df,
+        holiday_dates=holiday_dates,
+        tolerance=0.1
+    )
+
+    if future_df is None:
+        return None, pd.DataFrame(columns=['date', 'occupied'])
+
+    future_df = future_df[['date', 'occupied']].copy()
+    future_df['occupied'] = np.ceil(future_df['occupied'])
+    future_df['source'] = 'Forecast'
+    
+    forecasted_rns = int(future_df['occupied'].sum())
+    forecasted_revenue = int(forecasted_rns * adr)
+    
+    # Combine actual and forecast
+    combined_df = pd.concat([actual_df, future_df], ignore_index=True)
+    # Plot
+    plt.figure(figsize=(10, 4))
+    for label, df in combined_df.groupby('source'):
+        plt.plot(df['date'], df['occupied'], label=label, marker='o')
+
+    plt.xticks(rotation=45)
+    plt.xlabel("Date")
+    plt.ylabel("Occupancy")
+    plt.title("Hotel Occupancy: Last 30 Days (Actual) + Next 30 Days (Forecast)")
+    plt.grid(True)
+    plt.legend()
+
+    # Save plot to image buffer
+    buf = io.BytesIO()
+    plt.tight_layout()
+    plt.savefig(buf, format='png')
+    plt.close()
+    buf.seek(0)
+    image = Image.open(buf)
+    
+    folium_map = folium.Map(location=[lat, lon], zoom_start=15)
+    folium.Marker([lat, lon], tooltip=property_name).add_to(folium_map)
+    map_html = folium_map._repr_html_()
+
+#     return image, future_df[['date', 'occupied']],map_html
+    return image,forecasted_rns,forecasted_revenue,map_html
+
+
+model_df.columns
+
+demo = gr.Interface(
+    fn=forecast_by_property,
+    inputs=[
+        gr.Dropdown(
+            choices=property_options,
+            label="Select Property",
+            info="Choose from the list of properties (searchable)",
+            interactive=True
+        ),
+        gr.Number(
+            label="Average Daily Rate (ADR)",
+            info="Enter the expected ADR in your currency",
+            interactive=True
+        )
+    ],
+    outputs=[
+        gr.Image(type="pil", label="Forecast Plot"),
+        gr.Number(label="Total Forecasted Room Nights", precision=0),
+        gr.Number(label="Total Forecasted Revenue", precision=0),
+        gr.HTML(label="Map")
+    ],
+    title="Hotel Occupancy Segment Forecast",
+    description="Forecasts the next 30 days of occupancy for a selected hotel segment.",
+    flagging_mode='never'
+)
+
+if __name__ == "__main__":
+    demo.launch(share=True)

feature_columns.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:932904e8507793889143b8a0d22078a9c67d64ed7db51a2b163226d18b38cf56
+size 404

holidays_2022_2025.json

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+[
+    {
+        "date": "2022-01-01",
+        "name": "New Year's Day (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-01-09",
+        "name": "Guru Gobind Singh's Birthday (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-01-13",
+        "name": "Lohri (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-01-14",
+        "name": "Makar Sankranti/ Magha Bihu/ Pongal (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-01-26",
+        "name": "Republic Day (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-02-05",
+        "name": "Basant Panchami / Sri Panchami  (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-02-15",
+        "name": "Hazarat Ali's Birthday (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-02-16",
+        "name": "Guru Ravidas's Birthday (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-02-19",
+        "name": "Shivaji Jayanti (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-02-26",
+        "name": "Swami Dayananda Saraswati Jayanti (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-03-01",
+        "name": "Maha Shivaratri (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-03-17",
+        "name": "Holika Dahan (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-03-18",
+        "name": "Holi  (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-03-18",
+        "name": "Dolyatra (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-04-02",
+        "name": "Chaitra Sukladi/ Gudi Padava/ Ugadi/ Cheti Chand (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-04-10",
+        "name": "Ram Navami (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-04-14",
+        "name": "Mahavir Jayanti (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-04-14",
+        "name": "Vaisakhi/ Vishu/ Meshadi (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-04-14",
+        "name": "Birthday of Dr. B. R. Ambedkar (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-04-15",
+        "name": "Good Friday (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-04-15",
+        "name": "Vaisakhadi (Bengal)/ Bahag Bihu (Assam) (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-04-17",
+        "name": "Easter Sunday  (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-04-29",
+        "name": "Jamat-Ul-Vida (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-05-03",
+        "name": "Id-ul- Fitr (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-05-09",
+        "name": "Guru Rabindranath's Birthday (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-05-16",
+        "name": "Buddha Purnima (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-07-01",
+        "name": "Rath Yatra (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-07-10",
+        "name": "Id-ul-Zuha (Bakrid) (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-08-09",
+        "name": "Muharram (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-08-11",
+        "name": "Raksha Bandhan (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-08-15",
+        "name": "Independence day  (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-08-16",
+        "name": "Parsi New Year's day/ Nauraj (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-08-18",
+        "name": "Janmashtami (Smarta) (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-08-19",
+        "name": "Janmashtami (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-08-31",
+        "name": "Vinayaka Chaturthi/ Ganesh Chaturthi (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-09-08",
+        "name": "Onam or Thiru Onam Day (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-10-02",
+        "name": "Mahatma Gandhi's Birthday (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-10-02",
+        "name": "Dussehra (Maha Saptami) (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-10-03",
+        "name": "Dussehra (Maha Ashtami) (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-10-04",
+        "name": "Dussehra (Maha Navmi) (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-10-05",
+        "name": "Dussehra (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-10-09",
+        "name": "Milad-un-Nabi or Id-e-Milad (Birthday of Prophet Mohammad) (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-10-09",
+        "name": "Maharishi Valmiki's Birthday (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-10-13",
+        "name": "Karaka Chaturthi (Karva Chouth) (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-10-24",
+        "name": "Diwali (Deepawali) (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-10-24",
+        "name": "Naraka Chaturdasi (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-10-25",
+        "name": "Govardhan Puja (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-10-26",
+        "name": "Bhai Duj (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-10-30",
+        "name": "Pratihar Sashthi or Surya Sashthi (Chhat Puja) (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-11-08",
+        "name": "Guru Nanak's Birthday (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-11-24",
+        "name": "Guru Teg Bahadur's Martyrdom Day (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-12-24",
+        "name": "Christmas Eve (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2022-12-25",
+        "name": "Christmas Day (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2022-12-29",
+        "name": "Guru Gobind Singh's Birthday (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-01-01",
+        "name": "New Year's Day (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-01-14",
+        "name": "Makar Sankranti/Magha Bihu (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-01-15",
+        "name": "Pongal (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-01-26",
+        "name": "Republic Day (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2023-01-26",
+        "name": "Basant Panchami/Sri Panchami (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-02-05",
+        "name": "Hazarat Ali's Birthday, Guru Ravidas's Birthday (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-02-15",
+        "name": "Swami Dayananda Saraswati Jayanti (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-02-18",
+        "name": "Maha Shivaratri (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-02-19",
+        "name": "Shivaji Jayanti (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-03-07",
+        "name": "Holika Dahan, Dolyatra (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-03-08",
+        "name": "Holi (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2023-03-22",
+        "name": "Chaitra Sukladi/ Gudi Padava/ Ugadi/ Cheti Chand (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-03-30",
+        "name": "Ram Navami (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2023-04-04",
+        "name": "Mahavir Jayanti (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2023-04-07",
+        "name": "Good Friday (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2023-04-09",
+        "name": "Easter Sunday (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-04-14",
+        "name": "Vaisakhi/Vishu/Meshadi (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-04-14",
+        "name": "Birthday of Dr. B. R. Ambedkar (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2023-04-15",
+        "name": "Vaisakhadi (Bengal)/ Bahag Bihu (Assam) (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-04-21",
+        "name": "Jamat-Ul-Vida (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-04-22",
+        "name": "Id-ul-Fitr (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2023-05-05",
+        "name": "Buddha Purnima (G)",
+        "type": "Gazetted"
+    },
+    {
+        "date": "2023-05-09",
+        "name": "Guru Rabindranath's Birthday (R)",
+        "type": "Restricted"
+    },
+    {
+        "date": "2023-06-20",
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