Update app.py

app.py

@@ -5,34 +5,6 @@ import json
 from io import StringIO
 
 
-def huff_model_probability(df_distances, df_attractiveness, alpha, beta, df_population=None, distance_threshold=None, decay_factor=0.1):
-    """
-    Calculates the probability of choosing among destinations and the adjustment factors for willingness to travel.
-    """
-    if df_population is None:
-        df_population = pd.Series(np.ones(df_distances.shape[0]), index=df_distances.index)
-
-    adjustment_factors = pd.DataFrame(index=df_distances.index, columns=df_distances.columns)
-    if distance_threshold is not None and distance_threshold is not "None":
-        # Calculate adjustment factors for each origin-destination pair
-        for destination in df_distances.columns:
-            adjustment_factors[destination] = df_distances[destination].apply(
-                lambda x: np.exp(-(max(0, x - distance_threshold)) * decay_factor))
-    else:
-        adjustment_factors[:] = 1
-    # deactivate adjustment factor !!!!!!!!!!!!!!!!!!!!
-    adjustment_factors[:] = 1
-
-    adjusted_population = df_population.repeat(df_distances.shape[1]).values.reshape(df_distances.shape) * adjustment_factors
-    attractiveness_term = df_attractiveness ** alpha
-    distance_term =np.exp(df_distances * -beta)
-
-    numerator = (attractiveness_term * distance_term).multiply(adjusted_population, axis=0)
-    denominator = numerator.sum(axis=1)
-    probabilities = numerator.div(denominator, axis=0).fillna(0)
-
-    return probabilities, adjustment_factors
-
 def app_function(input_json):
     print("Received input")
     # Parse the input JSON string
@@ -45,40 +17,13 @@ def app_function(input_json):
     # Assuming the input structure is correctly formatted to include the necessary parameters
     inputs = inputs["input"]
 
-    # Convert 'df_distances' from a list of lists into a DataFrame
-    df_distances = pd.DataFrame(inputs["df_distances"])
-    print("df_distances shape:", df_distances.shape)
-    
-    # Convert 'df_attractiveness' into a Series
-    df_attractiveness = pd.Series(inputs["df_attractiveness"])
-    print("df_attractiveness shape:", df_attractiveness.shape)
-    
-    # Extract alpha and beta parameters
-    alpha = inputs["alpha"]
-    beta = inputs["beta"]
-    
-    # Check if 'df_population' is provided and convert to Series, else default to None
-    df_population = pd.Series(inputs["df_population"]) if "df_population" in inputs else None
-
-    # Additional parameters for the updated Huff model
-    distance_threshold = inputs.get("distance_threshold", None)
-    decay_factor = inputs.get("decay_factor", 0.1)  # Default decay factor if not provided
+    print(inputs["somedata"])
+    calcRes = float(inputs["somedata"])*float(inputs["somedata"])
     
-    # Call the updated Huff model function
-    probabilities, adjustment_factors = huff_model_probability(
-        df_distances=df_distances,
-        df_attractiveness=df_attractiveness,
-        alpha=alpha,
-        beta=beta,
-        df_population=df_population,
-        distance_threshold=distance_threshold,
-        decay_factor=decay_factor
-    )
 
     # Prepare the output
     output = {
-        "probabilities": probabilities.to_dict(orient='split'),
-        "adjustment_factors": adjustment_factors.to_dict(orient='split')
+        "calcRes": calcRes
     }
 
     return json.dumps(output)