Update app.py

app.py

@@ -355,29 +355,6 @@ def write_logs(message, message_type="Prediction"):
         #            {"name": message_type, "message": message, "time": str(datetime.now())}
          #       )
     return 
-    
-def predict(x, request: gr.Request):
-    """
-    Predict the hardness and yield strength using the ML model. Input data is a dataframe
-    """
-    loaded_model = tf.keras.models.load_model("hardness_nn_graph_separate_elements.h5")
-    print("summary is", loaded_model.summary())
-    #x = x.replace("", 0)
-    x = np.asarray(x).astype("float32")
-    y = loaded_model.predict(x)
-    y_hardness = y[0][0]
-    y_ys = y[0][1]
-    minimum_hardness, maximum_hardness = scaling_factors['PROPERTY: HV']
-    minimum_ys, maximum_ys = scaling_factors['PROPERTY: YS (MPa)']
-    print("Prediction is ", y)
-    if request is not None:   # Verify if request is not None (when building the app the first request is None)
-        message = f"{request.username}_{request.client.host}"
-        print("MESSAGE")
-        print(message)
-        res = write_logs(message)
-    #interpret_fig = utils.interpret(x)
-    return (round(y_hardness*(maximum_hardness-minimum_hardness)+minimum_hardness, 2), 12,
-            round(y_ys*(maximum_ys-minimum_ys)+minimum_ys, 2), 12)
 
 def fit_outputs_constraints(X, antimicrobial_activity_target, request: gr.Request):
     reduction_target = 100 - int(antimicrobial_activity_target)
@@ -417,7 +394,7 @@ def predict_inverse(antimicrobial_activity_target, request: gr.Request):
         if column in one_hot_mapping:
             optimized_x.loc[0, column] = one_hot_mapping[column][int(optimized_x.loc[0, column])]
 
-    return optimized_x
+    return optimized_x.transpose()
 
 
 example_inputs = [80]