v1

app.py

@@ -0,0 +1,99 @@
+import xgboost as xgb
+import pandas as pd
+import os
+from datetime import datetime
+from pandas.tseries.holiday import USFederalHolidayCalendar as calendar
+import streamlit as st
+
+# Load your trained XGBoost model from a .bin file
+model = xgb.Booster()
+model.load_model(os.path.join(os.path.dirname(__file__), "xgb_model.bin"))
+
+# Load the unseen data from a CSV file
+df = pd.read_csv(os.path.join(os.path.dirname(__file__), "unseen_data.csv"))
+
+# Define the expected columns for prediction
+expected_columns = ['temperature', 'year', 'month', 'day', 'hr', 'day_of_week', 'is_weekend', 'holiday']
+
+def get_random_data():
+    # Select 5 random rows from the unseen data
+    random_data = df.sample(5).copy()
+    return random_data
+
+def predict_demand(input_df):
+    # Prepare data for prediction
+    prediction_data = input_df[expected_columns]
+    dmatrix = xgb.DMatrix(prediction_data)
+    predictions = model.predict(dmatrix)
+    # Add predictions to the dataframe
+    input_df['predicted_demand'] = predictions.round(0).astype(int)
+    return input_df
+
+def format_random_output(prediction_df):
+    # Calculate percentage error
+    prediction_df['error_percentage'] = ((prediction_df['predicted_demand'] - prediction_df['demand']) / prediction_df['demand'] * 100).round(2)
+    
+    # Format date and time
+    prediction_df['datetime'] = pd.to_datetime(prediction_df['date'] + ' ' + prediction_df['hr'].astype(str) + ':00:00')
+    
+    # Select and rename columns
+    output_df = prediction_df[['datetime', 'temperature', 'predicted_demand', 'demand', 'error_percentage']]
+    output_df.columns = ['Date and Time', 'Temperature (°C)', 'Predicted Demand (MW)', 'Actual Demand (MW)', 'Error (%)']
+    
+    return output_df
+
+def custom_predict(date_time, temperature):
+    # Parse date and time
+    dt = pd.to_datetime(date_time)
+    
+    # Calculate additional parameters
+    is_weekend = dt.dayofweek >= 5
+    holidays = calendar().holidays(start=dt.floor('D'), end=dt.ceil('D'))
+    is_holiday = dt.floor('D') in holidays
+
+    # Create custom data
+    custom_data = pd.DataFrame([[
+        temperature,
+        dt.year,
+        dt.month,
+        dt.day,
+        dt.hour,
+        dt.dayofweek,
+        int(is_weekend),
+        int(is_holiday)
+    ]], columns=expected_columns)
+    
+    # Predict
+    prediction_df = predict_demand(custom_data)
+    
+    # Format output
+    output_df = pd.DataFrame({
+        'Date and Time': [dt],
+        'Temperature (°C)': [temperature],
+        'Predicted Demand (MW)': prediction_df['predicted_demand']
+    })
+    
+    return output_df
+
+# Streamlit app
+st.title("Electricity Demand Prediction")
+st.markdown("Predict electricity demand based on various factors.")
+
+tab1, tab2 = st.tabs(["Random Predictions", "Custom Prediction"])
+
+with tab1:
+    st.header("Random Predictions")
+    if st.button("Predict for 5 Random Data Points"):
+        random_data = get_random_data()
+        prediction_df = predict_demand(random_data)
+        formatted_output = format_random_output(prediction_df)
+        st.dataframe(formatted_output)
+
+with tab2:
+    st.header("Custom Prediction")
+    date_time = st.datetime_input("Date and Time", value=datetime.now())
+    temperature = st.slider("Temperature (°C)", 0, 40)
+    
+    if st.button("Predict for Custom Input"):
+        custom_output = custom_predict(date_time, temperature)
+        st.dataframe(custom_output)

requirements.txt

@@ -0,0 +1,5 @@
+numpy==1.26.4
+pandas==2.2.2
+xgboost==2.1.1
+gradio
+

unseen_data.csv

@@ -0,0 +1,214 @@
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