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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=False)
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