Update app.py

app.py

@@ -60,23 +60,22 @@ def app_function(input_json):
     # Parse the input JSON string
     inputs = json.loads(input_json)
     
-    # Assuming 'inputs["df_distances"]' is a JSON string of a CSV, use StringIO to convert it to a DataFrame
-    df_distances = pd.read_csv(StringIO(inputs["df_distances"]))
+    # Convert 'df_distances' from a list of lists into a DataFrame directly
+    df_distances = pd.DataFrame(inputs["df_distances"])
     
-    # Assuming 'inputs["df_attractiveness"]' and others are already in list format in the JSON
+    # 'inputs["df_attractiveness"]' and others are directly usable as lists
     df_attractiveness = pd.Series(inputs["df_attractiveness"])
     alpha = inputs["alpha"]
     beta = inputs["beta"]
     df_capacity = pd.Series(inputs["df_capacity"])
     
-    # Check if 'df_population' is in inputs and convert to Series, else default to None
+    # Check if 'df_population' is provided and convert to Series
     df_population = pd.Series(inputs["df_population"]) if "df_population" in inputs else None
     
     iterations = inputs.get("iterations", 5)
     crowding_threshold = inputs.get("crowding_threshold", 1.0)
     
-    # Call the dynamic Huff model function with these parameters
-    # Ensure 'dynamic_huff_model' is defined and ready to accept these parameters
+    # Assuming dynamic_huff_model function is correctly defined to accept these parameters
     result = dynamic_huff_model(df_distances, df_attractiveness, alpha, beta, df_capacity, df_population, iterations, crowding_threshold)
     
     # Convert the result DataFrame to a JSON string for output