Update pipelineFinal.py

pipelineFinal.py

@@ -1,130 +1,138 @@
-from calendar import c
-from os import pipe
-import pandas as pd
-import pickle
-from skforecast.utils import load_forecaster
-from filterdf import filter_datasets
-from mergedf import merging_datasets
-import numpy as np
-import plotly.graph_objects as go
-
-def load_csv(input_file):
-    try:
-        # Leer el archivo CSV
-        df = pd.read_csv(input_file)
-        
-        # Verificar si el DataFrame está vacío
-        if df.empty:
-            raise ValueError("El archivo subido está vacío o no tiene datos válidos.")
-        
-        # Retornar las primeras 5 filas como tabla HTML
-        # return df.head().to_html()
-        return df
-    except Exception as e:
-        raise f"Error al cargar el archivo CSV:{e}"
-    
-def load_model(name):
-
-    model = load_forecaster(name,verbose=True)
-    return model
-
-def load_pipeline():
-    with open('pipeline.pkl', 'rb') as file:
-        pipeline = pickle.load(file)
-    return pipeline
-
-def unscale_data(scaler, predictions):
-    placeholder = np.zeros((len(predictions), 11))
-    placeholder[:, 0] = predictions['target']
-    predictions_scaled = scaler.inverse_transform(placeholder)[:, 0]
-    predictions_scaled[predictions_scaled < 0] = 0
-    predictions = pd.DataFrame(predictions_scaled, columns=predictions.columns, index=predictions.index)
-    return predictions
-
-def create_plots(predictions):
-    # Gráfico de las predicciones vs valores reales en el conjunto de test del modelo con mejores parametros
-    fig = go.Figure()
-    trace2 = go.Scatter(x=predictions.index, y=predictions['target'], name="Estimado", mode="lines", line_color="#4EA72E")
-    fig.add_trace(trace2)
-    fig.update_layout(
-        yaxis_title="Producción (kWh)",
-        width=750,
-        height=350,
-        margin=dict(l=20, r=0, t=35, b=20),
-        legend=dict(
-            orientation="v",
-            yanchor="top",
-            xanchor="right",
-            x=0.99,
-            y=0.99
-        )
-    )
-    return fig
-
-
-def pipeline_final(texto,steps,train=None,client=None,historical_weather=None,electricity_prices=None,gas_prices=None):
-    #prueba
-    #texto = 'No'
-    # #steps
-    # steps = 24
-    # #dfs
-    
-    # train = 'files_prueba/train_filtered.csv'
-    # client = 'files_prueba/client_filtered.csv'
-    # historical_weather = 'files_prueba/historical_weather_filtered.csv'
-    # electricity_prices = 'files_prueba/electricity_prices_filtered.csv'
-    # gas_prices = 'files_prueba/gas_prices_filtered.csv'
-    pipeline = load_pipeline()
-    scaler = pipeline['scale']
-    
-    #load model
-    model = load_model('LSTM_forecaster.joblib')
-        
-    
-    if texto == 'Si':
-        pred = model.predict(steps=steps)
-        
-        pred = unscale_data(scaler, pred)
-        
-        pred_reset = pred.reset_index(drop=False)
-        
-        pred_reset = pred_reset.astype(str)
-        
-        pred_reset = pred_reset.rename(columns={'index': 'fecha'})
-        
-        fig = create_plots(pred)
-        
-        return fig , pred_reset
-        
-    else:
-        train = load_csv(train)
-        client = load_csv(client)
-        historical_weather = load_csv(historical_weather)
-        electricity_prices = load_csv(electricity_prices)
-        gas_prices = load_csv(gas_prices)
-        #filter data sets
-        filter_datasets(train,client,historical_weather,electricity_prices,gas_prices)
-        #merge data sets
-        df = merging_datasets()
-        #load pipeline
-        # sclaing the data
-        df_processed = pipeline.transform(df)
-        
-        df_processed = pd.DataFrame(df_processed, columns=df.columns, index=df.index)
-    
-        pred = model.predict(steps=steps, last_window=df_processed)
-        
-        pred = unscale_data(scaler, pred)
-        
-        pred_reset = pred.reset_index(drop=False)
-        
-        pred_reset = pred_reset.astype(str)
-        
-        pred_reset = pred_reset.rename(columns={'index': 'fecha'})
-        
-        fig = create_plots(pred)
-        
-        return fig , pred_reset
-        
-
-    
+from calendar import c
+from os import pipe
+import pandas as pd
+import pickle
+from skforecast.utils import load_forecaster
+from filterdf import filter_datasets
+from mergedf import merging_datasets
+import numpy as np
+import plotly.graph_objects as go
+
+def load_csv(input_file):
+    try:
+        # Leer el archivo CSV
+        df = pd.read_csv(input_file)
+        
+        # Verificar si el DataFrame está vacío
+        if df.empty:
+            raise ValueError("El archivo subido está vacío o no tiene datos válidos.")
+        
+        # Retornar las primeras 5 filas como tabla HTML
+        # return df.head().to_html()
+        return df
+    except Exception as e:
+        raise f"Error al cargar el archivo CSV:{e}"
+    
+def load_model(name):
+
+    model = load_forecaster(name,verbose=True)
+    return model
+
+def load_pipeline():
+    with open('pipeline.pkl', 'rb') as file:
+        pipeline = pickle.load(file)
+    return pipeline
+
+def unscale_data(scaler, predictions):
+    placeholder = np.zeros((len(predictions), 11))
+    placeholder[:, 0] = predictions['target']
+    predictions_scaled = scaler.inverse_transform(placeholder)[:, 0]
+    predictions_scaled[predictions_scaled < 0] = 0
+    predictions = pd.DataFrame(predictions_scaled, columns=predictions.columns, index=predictions.index)
+    return predictions
+
+def create_plots(predictions):
+    # Gráfico de las predicciones vs valores reales en el conjunto de test del modelo con mejores parametros
+    fig = go.Figure()
+    trace2 = go.Scatter(x=predictions.index, y=predictions['target'], name="Estimado", mode="lines", line_color="#4EA72E")
+    fig.add_trace(trace2)
+    fig.update_layout(
+        yaxis_title="Producción (kWh)",
+        width=750,
+        height=350,
+        margin=dict(l=20, r=0, t=35, b=20),
+        legend=dict(
+            orientation="v",
+            yanchor="top",
+            xanchor="right",
+            x=0.99,
+            y=0.99
+        )
+    )
+    return fig
+
+
+def pipeline_final(texto,steps,train=None,client=None,historical_weather=None,electricity_prices=None,gas_prices=None):
+    #prueba
+    #texto = 'No'
+    # #steps
+    # steps = 24
+    # #dfs
+    
+    # train = 'files_prueba/train_filtered.csv'
+    # client = 'files_prueba/client_filtered.csv'
+    # historical_weather = 'files_prueba/historical_weather_filtered.csv'
+    # electricity_prices = 'files_prueba/electricity_prices_filtered.csv'
+    # gas_prices = 'files_prueba/gas_prices_filtered.csv'
+    pipeline = load_pipeline()
+    scaler = pipeline['scale']
+    
+    #load model
+    model = load_model('LSTM_forecaster.joblib')
+        
+    
+    if texto == 'Si':
+        pred = model.predict(steps=steps)
+        
+        pred = unscale_data(scaler, pred)
+        
+        pred_reset = pred.reset_index(drop=False)
+        
+        pred_reset = pred_reset.astype(str)
+        
+        pred_reset = pred_reset.rename(columns={'index': 'fecha'})
+
+        pred_reset = pred_reset.rename(columns={'target': 'generacion'})
+        
+        pred_reset['generacion'] = pred_reset['generacion'].astype(float).round(2).astype(str)
+        
+        fig = create_plots(pred)
+        
+        return fig , pred_reset
+        
+    else:
+        train = load_csv(train)
+        client = load_csv(client)
+        historical_weather = load_csv(historical_weather)
+        electricity_prices = load_csv(electricity_prices)
+        gas_prices = load_csv(gas_prices)
+        #filter data sets
+        filter_datasets(train,client,historical_weather,electricity_prices,gas_prices)
+        #merge data sets
+        df = merging_datasets()
+        #load pipeline
+        # sclaing the data
+        df_processed = pipeline.transform(df)
+        
+        df_processed = pd.DataFrame(df_processed, columns=df.columns, index=df.index)
+    
+        pred = model.predict(steps=steps, last_window=df_processed)
+        
+        pred = unscale_data(scaler, pred)
+        
+        pred_reset = pred.reset_index(drop=False)
+        
+        pred_reset = pred_reset.astype(str)
+        
+        pred_reset = pred_reset.rename(columns={'index': 'fecha'})
+
+        pred_reset = pred_reset.rename(columns={'target': 'generacion'})
+        
+        pred_reset['generacion'] = pred_reset['generacion'].astype(float).round(2).astype(str)
+        
+        fig = create_plots(pred)
+        
+        return fig , pred_reset
+        
+
+