Upload app (1).py

app (1).py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[3]:
+
+
+import pandas as pd
+import pmdarima as pm
+import matplotlib.pyplot as plt
+import gradio as gr
+import io
+import warnings
+warnings.simplefilter("ignore")
+
+
+def predict_timeseries(data_file):
+    # Load CSV file into a pandas DataFrame
+    data = pd.read_csv(data_file.name, index_col=[0], parse_dates=True)
+
+    # Convert date column to datetime object
+    data.index = pd.to_datetime(data.index)
+
+    # Fit the auto ARIMA model
+    model = pm.auto_arima(data.values, seasonal=True, m=12)
+
+    # Get ARIMA order
+    arima_order = model.order
+
+    # Plot the actual data
+   # Plot the actual data
+    fig_actual, ax_actual = plt.subplots()
+    ax_actual.plot(data, label = data.columns[-1])
+    ax_actual.set_xlabel(data.index.name)
+    plt.legend()
+    ax_actual.set_ylabel(data.columns[-1])
+    ax_actual.set_title("Plot of Actual data for {}".format(data.columns[-1]))
+    plt.show()
+    
+    # Get the last date in the actual data
+    last_date = data.index[-1]
+
+    # Make predictions
+    predicted_values = model.predict(n_periods=12)
+
+    # Generate a range of dates starting from the start date
+    pred_index = pd.date_range(start=last_date, periods=len(predicted_values)+1, freq="MS")[1:]
+
+    # Create a new dataframe with the predicted values and the generated dates
+    predictions = pd.DataFrame({'predicted_values': predicted_values}, index=pred_index)
+    predictions.columns = data.columns
+    predictions.index.name = data.index.name
+    predictions.index.freq = data.index.freq
+
+    # Merge the dataframes using the index
+    merged_data = pd.concat([data, predictions], axis=0)
+
+    num_actual = len(data.index)
+
+    # Plot the actual vs predicted data
+    actual_data = merged_data.iloc[:num_actual,:]
+    fig, ax = plt.subplots()
+    ax.plot(actual_data.index, actual_data[data.columns[-1]], label='Actual')
+
+    # Plot the predicted data
+    predicted_data = merged_data.iloc[num_actual:,:]
+    ax.plot(predicted_data.index, predicted_data[data.columns[-1]], label='Predicted')
+
+    # Add x and y axis labels
+    ax.set_xlabel(data.index.name)
+    ax.set_ylabel(data.columns[-1])
+
+    # Add title and legend
+    ax.set_title('Actual vs Predicted')
+    ax.legend()
+    plt.show()
+
+
+    return data.head(), fig_actual, arima_order, predictions, fig
+
+input_data = gr.inputs.File(label="Upload CSV file")
+
+outputs = [gr.outputs.Dataframe(type = "pandas", label = "FIRST FIVE ROWS OF DATASET"), 
+                                'plot', 
+                                gr.outputs.Textbox(label = "ARIMA ORDER"), 
+                                gr.outputs.Dataframe(type = "pandas", label = "PREDICTIONS FOR NEXT 12 PERIODS"), 
+                                'plot'
+          ]
+examples = ["Electric_Production.csv"]
+interface = gr.Interface(fn=predict_timeseries, inputs=input_data, outputs=outputs,
+                         title="Time series Forecast using AUTO ARIMA",
+                         description="Upload a CSV file of monthly time series data to generate 12 period forecasts using ARIMA.\nPlease Note: \nThe first plot displays the Time Plot of the Actual Series \n\tThe second Plot displays Plot Of Actual  and Predicted values",
+                         theme = "darkhuggingface", 
+                         examples = examples)
+interface.launch()
+
+
+# In[ ]:
+
+
+
+