Update app.py

app.py

@@ -27,23 +27,27 @@ def dynamic_huff_model(df_distances, df_attractiveness, alpha, beta, df_capacity
     df_population_per_iteration = df_population / iterations
 
     # Run the iterative distribution process
-    for _ in range(int(iterations)):
+    for i in range(int(iterations)):
+        print("iteration " + str(i) + "/"+str(int(iterations)))
         attractiveness = df_attractiveness.copy()
         current_visitors = df_visitors.sum(axis=0)
         
         # Calculate the decay based on the relative share of free capacity
+        print(" Calculate the decay based on the relative share of free capacity")
         relative_crowding = current_visitors / df_capacity
         decay_factor = np.where(relative_crowding < crowding_threshold, 1, 1 - (relative_crowding - crowding_threshold) / (1 - crowding_threshold))
         attractiveness *= decay_factor
         
         # Calculate Huff model probabilities
+        print("Calculate Huff model probabilities")
         distance_term = df_distances ** -beta
         # If df_distances is a DataFrame and df_attractiveness is a Series, you might need an operation like:
         numerator = df_distances.multiply(df_attractiveness, axis=0)  # Adjust based on actual intent
 
         denominator = numerator.sum(axis='columns')
         probabilities = numerator.div(denominator, axis='index').fillna(0)
-        
+
+        print("Distribute visitors based on probabilities and population")
         # Distribute visitors based on probabilities and population
         visitors_this_iteration = probabilities.multiply(df_population_per_iteration, axis='index')