Update app.py

app.py

@@ -60,22 +60,27 @@ def app_function(input_json):
     # Parse the input JSON string
     inputs = json.loads(input_json)
     
-    # Convert stringified CSVs in JSON to DataFrames
+    # 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"]))
-    df_attractiveness = pd.Series(json.loads(inputs["df_attractiveness"]))
+    
+    # Assuming 'inputs["df_attractiveness"]' and others are already in list format in the JSON
+    df_attractiveness = pd.Series(inputs["df_attractiveness"])
     alpha = inputs["alpha"]
     beta = inputs["beta"]
-    df_capacity = pd.Series(json.loads(inputs["df_capacity"]))
-    df_population = pd.Series(json.loads(inputs["df_population"])) if "df_population" in inputs else None
+    df_capacity = pd.Series(inputs["df_capacity"])
+    
+    # Check if 'df_population' is in inputs and convert to Series, else default to None
+    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
     result = dynamic_huff_model(df_distances, df_attractiveness, alpha, beta, df_capacity, df_population, iterations, crowding_threshold)
     
-    # Convert the result DataFrame to a CSV string for Gradio output
-    result_json = result.to_json(orient='split')
-    return json.loads(result_json)
+    # Convert the result DataFrame to a JSON string for output
+    return result.to_json(orient='split')
 
 # Define the Gradio interface with a single JSON input
 iface = gr.Interface(