Update app.py

app.py

@@ -4,59 +4,63 @@ import xgboost as xgb
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import mean_squared_error
 import gradio as gr
-import os
-import json
 import datetime
+import os
 
 # API Endpoints
 WEATHER_API = "https://api.open-meteo.com/v1/forecast"
 ELECTRICITY_PRICE_API = "https://www.elprisetjustnu.se/api/v1/prices"
 ENERGY_CHARTS_API = "https://energy-charts.info/api/public_power"
 
-# Fetch weather data
-def fetch_weather_data():
+# Fetch historical weather data
+def fetch_weather_data(start_date="2023-01-01", end_date=None):
+    if end_date is None:
+        end_date = datetime.datetime.now().strftime('%Y-%m-%d')
+
     params = {
         "latitude": 59.3293,  # Stockholm latitude
         "longitude": 18.0686,  # Stockholm longitude
-        "hourly": "temperature_2m,precipitation,wind_speed_10m,humidity",  # Comma-separated string
-        "start": "2024-01-01",
-        "end": "2024-12-31"
+        "daily": "temperature_2m_mean,precipitation_sum,wind_speed_10m_max,wind_direction_10m_dominant",
+        "start_date": start_date,
+        "end_date": end_date,
+        "timezone": "Europe/Stockholm"
     }
     response = requests.get(WEATHER_API, params=params)
     response.raise_for_status()
-    hourly_data = response.json()["hourly"]
-    # Convert hourly data to a DataFrame
-    return pd.DataFrame(hourly_data)
+    daily_data = response.json()["daily"]
+    return pd.DataFrame(daily_data)
 
-# Fetch electricity price data
+# Fetch historical electricity prices
 def fetch_electricity_prices():
     today = datetime.datetime.now().strftime('%Y/%m-%d')
-    url = f"{ELECTRICITY_PRICE_API}/{today}_SE3.json"  # Replace 'SE3' with the desired price category
+    url = f"{ELECTRICITY_PRICE_API}/{today}_SE3.json"
     response = requests.get(url)
     response.raise_for_status()
     return pd.DataFrame(response.json())
 
 # Fetch energy production data using Energy-Charts API
-def fetch_energy_production_data():
+def fetch_energy_production_data(start_date="2023-01-01", end_date=None):
+    if end_date is None:
+        end_date = datetime.datetime.now().strftime('%Y-%m-%d')
+
     params = {
         "country": "se",  # Sweden country code
-        "start": "2024-01-01",
-        "end": "2024-12-31"
+        "start": start_date,
+        "end": end_date
     }
     response = requests.get(ENERGY_CHARTS_API, params=params)
     response.raise_for_status()
     data = response.json()
     production_data = {
         "unix_seconds": data["unix_seconds"],
-        "production": {ptype["name"]: ptype["data"] for ptype in data["production_types"]}
+        **{ptype["name"]: ptype["data"] for ptype in data["production_types"]}
     }
-    production_df = pd.DataFrame(production_data)
-    return production_df
+    return pd.DataFrame(production_data)
 
 # Prepare the dataset
 def prepare_dataset(weather_data, electricity_data, energy_data):
     dataset = pd.concat([weather_data, electricity_data, energy_data], axis=1)
-    dataset = dataset.dropna()  # Remove any rows with missing values
+    dataset = dataset.dropna()
     return dataset
 
 # Train the model
@@ -71,7 +75,8 @@ def train_model(dataset):
 
     rmse = mean_squared_error(y_test, predictions, squared=False)
     model.save_model("electricity_price_model.json")
-    return rmse
+    print(f"Model trained with RMSE: {rmse}")
+    return model
 
 # Load the model and make predictions
 def predict_price(features):
@@ -101,6 +106,7 @@ def update_predictions():
     predictions_output = dataset.copy()
     predictions_output["predicted_price"] = predictions
     predictions_output.to_json("predictions.json", orient="records")
+    print("Predictions updated and saved.")
 
 # Gradio Interface
 def gradio_interface():
@@ -144,8 +150,8 @@ if __name__ == "__main__":
 
         # Prepare dataset and train the model
         dataset = prepare_dataset(weather_data, electricity_data, energy_data)
-        rmse = train_model(dataset)
-        print(f"Model trained with RMSE: {rmse}")
+        train_model(dataset)
 
         # Launch Gradio interface
         gradio_interface()
+