Guardar mis cambios locales

app.py

@@ -1,114 +1,66 @@
 import gradio as gr
 import pandas as pd
 import numpy as np
-from datetime import datetime
 import plotly.graph_objects as go
-import torch
-from transformers import pipeline, TapasTokenizer, TapasForQuestionAnswering
-import pmdarima as pm
-from pmdarima import auto_arima
+import joblib  # Para cargar el modelo ARIMA guardado
+from datetime import datetime
 
-# Preprocessing functions (same as before)
-def merge(B, C, A):
-    pass  # Implement merge function here...
+# Función para cargar el modelo ARIMA guardado
+def load_arima_model():
+    model = joblib.load('arima_sales_model.pkl')  # Cargar el modelo ARIMA desde un archivo guardado (.pkl)
+    return model
 
-def merge_sort(dataframe):
-    pass  # Implement merge_sort function here...
+# Cargar el modelo ARIMA al inicio
+arima_model = load_arima_model()
 
+# Funciones de preprocesamiento y cálculo de crecimiento de ventas
 def drop(dataframe):
-    pass  # Implement drop function here...
+    pass  # Implementar según sea necesario
 
 def date_format(dataframe):
-    pass  # Implement date_format function here...
+    pass  # Implementar según sea necesario
 
 def group_to_three(dataframe):
-    pass  # Implement group_to_three function here...
-
-def series_to_df_exogenous(series):
-    pass  # Implement series_to_df_exogenous function here...
-
-def dates_df(dataframe):
-    pass  # Implement dates_df function here...
+    pass  # Implementar según sea necesario
 
 def get_forecast_period(period):
-    pass  # Implement get_forecast_period function here...
+    return period  # Retornar el periodo de pronóstico
 
-def train_test(dataframe, n):
-    pass  # Implement train_test function here...
-
-def test_fitting(dataframe, Exo, trainY):
-    pass  # Implement test_fitting function here...
-
-def forecast_accuracy(forecast, actual):
-    pass  # Implement forecast_accuracy function here...
-
-def sales_growth(dataframe, fittedValues):
-    pass  # Implement sales_growth function here...
+def sales_growth(df, forecasted_series):
+    return forecasted_series.diff()  # Calcular el crecimiento de ventas
 
 def merge_forecast_data(actual, predicted, future):
-    pass  # Implement merge_forecast_data function here...
-
-def interpret_mape(mape_score):
-    pass  # Implement interpret_mape function here...
+    return pd.DataFrame({
+        "Actual Sales": actual,
+        "Predicted Sales": predicted,
+        "Forecasted Future Sales": future
+    })
 
-def load_tapas_model():
-    model_name = "google/tapas-large-finetuned-wtq"
-    tokenizer = TapasTokenizer.from_pretrained(model_name)
-    model = TapasForQuestionAnswering.from_pretrained(model_name, local_files_only=False)
-    pipe = pipeline("table-question-answering", model=model, tokenizer=tokenizer)
-    return pipe
-
-pipe = load_tapas_model()
-
-def get_answer(table, query):
-    answers = pipe(table=table, query=query)
-    return answers
-
-def convert_answer(answer):
-    pass  # Implement convert_answer function here...
-
-def get_converted_answer(table, query):
-    pass  # Implement get_converted_answer function here...
-
-# Gradio Interface with emojis and colors
+# Función principal para la carga de archivo y la predicción
 def upload_and_forecast(uploaded_file, period):
     if uploaded_file is None:
         return "⚠️ Please upload a file to proceed."
 
-    # Load the data
     df = pd.read_csv(uploaded_file)
     df = drop(df)
     df = date_format(df)
-    merge_sort(df)
     series = group_to_three(df)
-    
+
+    # Realizar la predicción con el modelo ARIMA
     forecast_period = get_forecast_period(period)
-    df = series_to_df_exogenous(series)
+    forecasted_values, confint = arima_model.predict(n_periods=forecast_period, return_conf_int=True)
     
-    # Train the model
-    n_periods = round(len(df) * 0.2)
-    train = train_test(df, n_periods)
-    training_y, test_y, test_y_series, training_X, test_X, future_X = train
-    train_test_model = test_fitting(df, training_X, training_y)
+    # Crear serie con los valores pronosticados
+    forecasted_series = pd.Series(forecasted_values)
+    forecasted_series.index = pd.date_range(df['Date'].iloc[-1], periods=forecast_period, freq='3D')
     
-    fitted, confint = train_test_model.predict(X=test_X, n_periods=n_periods, return_conf_int=True)
-    index_of_fc = test_y_series.index
-    fitted_series = pd.Series(fitted)
-    fitted_series.index = index_of_fc
-
-    future_n_periods = forecast_period
-    future_fitted, confint = train_test_model.predict(X=df.iloc[-future_n_periods:, 1:], n_periods=future_n_periods, return_conf_int=True, freq='3D')
-    future_index_of_fc = pd.date_range(df['Sales'].index[-1], periods=future_n_periods, freq='3D')
-    future_fitted_series = pd.Series(future_fitted)
-    future_fitted_series.index = future_index_of_fc
-
-    # Calculate sales growth
-    future_sales_growth = sales_growth(df, future_fitted_series)
+    # Calcular el crecimiento de las ventas
+    future_sales_growth = sales_growth(df, forecasted_series)
     
-    # Prepare merged data for chart plotting
-    merged_data = merge_forecast_data(df['Sales'], fitted_series, future_fitted_series)
+    # Combinar los datos para graficar
+    merged_data = merge_forecast_data(df['Sales'], series, forecasted_series)
     
-    # Plot the charts
+    # Crear gráficos
     fig_compare = go.Figure()
     fig_compare.add_trace(go.Scatter(x=merged_data[merged_data.columns[0]], y=merged_data['Actual Sales'], mode='lines', name='Actual Sales'))
     fig_compare.add_trace(go.Scatter(x=merged_data[merged_data.columns[0]], y=merged_data['Predicted Sales'], mode='lines', name='Predicted Sales', line=dict(color='#006400')))
@@ -116,13 +68,12 @@ def upload_and_forecast(uploaded_file, period):
 
     fig_forecast = go.Figure()
     fig_forecast.add_trace(go.Scatter(x=merged_data[merged_data.columns[0]], y=merged_data['Actual Sales'], mode='lines', name='Actual Sales'))
-    fig_forecast.add_trace(go.Scatter(x=merged_data[merged_data.columns[0]], y=merged_data['Forecasted Future Sales'], mode='lines', name='Future Forecasted Sales'))
+    fig_forecast.add_trace(go.Scatter(x=merged_data[merged_data.columns[0]], y=forecasted_series, mode='lines', name='Forecasted Sales'))
     fig_forecast.update_layout(title='🔮 Forecasted Sales Data', xaxis_title='Date', yaxis_title='Sales')
 
-    # Return the figures and growth data
     return fig_compare, fig_forecast, future_sales_growth
 
-# Create the sidebar (inputs) and main interface for outputs
+# Interfaz de Gradio
 def create_sidebar():
     with gr.Column():
         gr.Markdown("### 📂 Upload your sales data (CSV)")
@@ -131,17 +82,15 @@ def create_sidebar():
         period = gr.Slider(minimum=30, maximum=90, step=1, label="Forecast period (in days)")
         return uploaded_file, period
 
-# Create the sidebar and main interface
+# Crear el sidebar y la interfaz principal
 uploaded_file, period = create_sidebar()
 
-# Gradio interface for output display (results shown below)
 output_plots = [
     gr.Plot(label="📈 Historical vs Predicted Sales"),
     gr.Plot(label="🔮 Forecasted Sales Data"),
     gr.DataFrame(label="📊 Sales Growth")
 ]
 
-# Create and launch the gradio interface
 iface = gr.Interface(
     fn=upload_and_forecast,
     inputs=[uploaded_file, period],
@@ -149,7 +98,7 @@ iface = gr.Interface(
     live=True,
     title="Sales Forecasting System ✨",
     description="Upload your sales data to start forecasting 🚀",
-    css=open("styles.css", "r").read()  # Load the external CSS file here
+    css=open("styles.css", "r").read()  # Cargar el archivo CSS para los estilos personalizados
 )
 
-iface.launch()
+iface.launch()  # Lanzar la interfaz