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| # Logic for calculating E-values | |
| import gradio as gr | |
| # Initialize the probabilities with Alejandro's initial beliefs | |
| prob_storm = 0.5 # Probability of a storm | |
| prob_norot_sweet = 0.6 # Probability of no sugar increase without storm | |
| prob_typical_sweet = 0.3 # Probability of typical sugar increase without storm | |
| prob_high_sweet = 0.1 # Probability of high sugar increase without storm | |
| # E-Value without model to be used for comparison | |
| e_value_without_model = 928500 | |
| # Logic for calculating E-values | |
| def calculate_e_values(prob_storm, prob_botrytis, prob_norot_sweet, prob_typical_sweet, prob_high_sweet, sensitivity=0.83, specificity=0.87): | |
| # Market revenues per bottle for each Riesling type | |
| revenue_per_bottle = { | |
| "Trocken": 5, | |
| "Kabinett": 10, | |
| "Spätlese": 15, | |
| "Auslese": 30, | |
| "Beerenauslese": 40, | |
| "Trockenbeerenauslese": 120 | |
| } | |
| # Number of cases produced under different scenarios | |
| cases = { | |
| "Harvest Now": {"Trocken": 6000, "Kabinett": 2000, "Spätlese": 2000}, | |
| "Storm-No Mold": {"Trocken": 5000, "Kabinett": 1000}, | |
| "Storm-Mold": {"Trockenbeerenauslese": 2000}, | |
| "No Storm-No Sugar": {"Trocken": 6000, "Kabinett": 2000, "Spätlese": 2000}, | |
| "No Storm-Typical Sugar": {"Trocken": 5000, "Kabinett": 1000, "Spätlese": 2500, "Auslese": 1500}, | |
| "No Storm-High Sugar": {"Trocken": 4000, "Kabinett": 2500, "Spätlese": 2000, "Auslese": 1000, "Beerenauslese": 500} | |
| } | |
| prob_storm_corrected = prob_storm * sensitivity + (1 - prob_storm) * (1 - specificity) | |
| prob_no_storm_corrected = (1 - prob_storm) * specificity + prob_storm * (1 - sensitivity) | |
| # Revenue calculations for different scenarios | |
| storm_revenue = (prob_botrytis * cases["Storm-Mold"]["Trockenbeerenauslese"] * 12 * revenue_per_bottle["Trockenbeerenauslese"]) + \ | |
| ((1 - prob_botrytis) * (cases["Storm-No Mold"]["Trocken"] * 12 * revenue_per_bottle["Trocken"] + | |
| cases["Storm-No Mold"]["Kabinett"] * 12 * revenue_per_bottle["Kabinett"])) | |
| no_sugar_revenue = cases["No Storm-No Sugar"]["Trocken"] * 12 * revenue_per_bottle["Trocken"] + \ | |
| cases["No Storm-No Sugar"]["Kabinett"] * 12 * revenue_per_bottle["Kabinett"] + \ | |
| cases["No Storm-No Sugar"]["Spätlese"] * 12 * revenue_per_bottle["Spätlese"] | |
| typical_sugar_revenue = cases["No Storm-Typical Sugar"]["Trocken"] * 12 * revenue_per_bottle["Trocken"] + \ | |
| cases["No Storm-Typical Sugar"]["Kabinett"] * 12 * revenue_per_bottle["Kabinett"] + \ | |
| cases["No Storm-Typical Sugar"]["Spätlese"] * 12 * revenue_per_bottle["Spätlese"] + \ | |
| cases["No Storm-Typical Sugar"]["Auslese"] * 12 * revenue_per_bottle["Auslese"] | |
| high_sugar_revenue = cases["No Storm-High Sugar"]["Trocken"] * 12 * revenue_per_bottle["Trocken"] + \ | |
| cases["No Storm-High Sugar"]["Kabinett"] * 12 * revenue_per_bottle["Kabinett"] + \ | |
| cases["No Storm-High Sugar"]["Spätlese"] * 12 * revenue_per_bottle["Spätlese"] + \ | |
| cases["No Storm-High Sugar"]["Auslese"] * 12 * revenue_per_bottle["Auslese"] + \ | |
| cases["No Storm-High Sugar"]["Beerenauslese"] * 12 * revenue_per_bottle["Beerenauslese"] | |
| # Calculate the total revenue and determine the recommended action | |
| total_revenue = (storm_revenue * prob_storm_corrected) + \ | |
| ((no_sugar_revenue * prob_norot_sweet) + | |
| (typical_sugar_revenue * prob_typical_sweet) + | |
| (high_sugar_revenue * prob_high_sweet)) * prob_no_storm_corrected | |
| e_value = total_revenue | |
| recommended_action = "Wait" if total_revenue > 928500 else "Harvest Now" | |
| return f"Expected Value (E-value) of the Decision: ${e_value}", f"Recommended Course of Action: {recommended_action}" | |
| # Gradio interface setup | |
| iface = gr.Interface( | |
| fn=calculate_e_values, | |
| inputs=[ | |
| gr.Slider(0, 1, step=0.01, label="Probability of Storm", value=prob_storm), | |
| gr.Slider(0, 1, step=0.01, label="Probability of Botrytis if Storm", value=0.1), | |
| gr.Slider(0, 1, step=0.01, label="Probability of No Sugar Increase", value=prob_norot_sweet), | |
| gr.Slider(0, 1, step=0.01, label="Probability of Typical Sugar Increase", value=prob_typical_sweet), | |
| gr.Slider(0, 1, step=0.01, label="Probability of High Sugar Increase", value=prob_high_sweet) | |
| ], | |
| outputs=["text", "text"], | |
| title="Wine Production Decision Model" | |
| ) | |
| # Run the Gradio app | |
| iface.launch(share=True) | |