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app.py

@@ -0,0 +1,84 @@
+# 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)

wine_decision_app.py

@@ -81,4 +81,4 @@ iface = gr.Interface(
 )
 
 # Run the Gradio app
-iface.launch(share=False)
+iface.launch(share=True)