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Update app.py
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app.py
CHANGED
@@ -172,19 +172,20 @@ with gr.Blocks(fill_height=True) as demo:
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with gr.Row():
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with gr.Column():
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start_input = gr.Textbox(label="Start Date:", placeholder="Enter date (e.g., 2023-09-01)", max_lines=1)
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gr.Markdown("
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with gr.Column():
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prediction_input = gr.Text(label="Month Prediction:", placeholder="Enter date (e.g., 2024-10-31)", max_lines=1)
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gr.Markdown("
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with gr.Column():
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range_input = gr.Slider(3, 12, 6, label="Window / Moving Average Period")
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gr.Markdown("
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table_2 = gr.DataFrame(value=df_2, label="Forecast Inputs:")
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# Configure the buttons and the panel visibility
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btn_1.click(check_words, inputs=[user_text, input_text], outputs=[panel_2, panel_1])
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with gr.Row():
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with gr.Column():
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start_input = gr.Textbox(label="Start Date:", placeholder="Enter date (e.g., 2023-09-01)", max_lines=1)
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gr.Markdown("Start of the data range of months that will be used as input by the model for revenue prediction.<br><br>The end month is automatically set to one month before the prediction month.")
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with gr.Column():
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prediction_input = gr.Text(label="Month Prediction:", placeholder="Enter date (e.g., 2024-10-31)", max_lines=1)
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gr.Markdown("Case creation month to predict its revenue.")
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with gr.Column():
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range_input = gr.Slider(3, 12, 6, label="Window / Moving Average Period")
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gr.Markdown("Window = 3-period/months, the predictive model reacts more quickly to recent monthly payment fluctuations, but it may also include more noise.<br><br>Window = 12-period/months, the Forecast adjusts more slowly and is less sensitive to small fluctuations, making it more reliable, but also slower to react to sharp changes.")
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table_1 = gr.DataFrame(value=df, label="Predictions (consult 'revenue' column):")
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btn_update = gr.Button("Run Forecast")
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btn_update.click(fn=update_table, inputs=[start_input, prediction_input, range_input, user_text], outputs=[table_1,table_2])
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table_2 = gr.DataFrame(value=df_2, label="Forecast Inputs:")
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# Configure the buttons and the panel visibility
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btn_1.click(check_words, inputs=[user_text, input_text], outputs=[panel_2, panel_1])
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