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app.py

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+import needed libraries:
+import pandas as pd
+import numpy as np
+from IPython.display import display
+from sklearn import preprocessing
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.neural_network import MLPRegressor
+from sklearn.preprocessing import OneHotEncoder
+from pickle import dump, load
+import gradio as gr
+
+# load the model
+mlp_model = load(open('mlp_classifier.pkl', 'rb'))
+# load the scaler
+my_scaler = load(open('scaler.pkl', 'rb'))
+hot_enc_scaler = load(open('hot_enc.pkl', 'rb'))
+
+def predict_value(age,height_cm,weight_kg,overall,potential,nationality,club):
+  #pre-processing:
+  numerical_features = [[age,height_cm,weight_kg,overall,potential]]
+  catagorical_features = [[nationality,club]]
+  numerical_features = my_scaler.transform(numerical_features)  
+  catagorical_features = hot_enc_scaler.transform(catagorical_features).toarray()
+  sample_player = np.concatenate((numerical_features[0], catagorical_features[0]), axis=0)
+  #predict:
+  predicted_value = mlp_model.predict(sample_player.reshape(1, -1))
+  return predicted_value
+
+
+demo = gr.Interface(
+    fn=predict_value,
+    inputs=[gr.Slider(15, 60),gr.Slider(100, 200),gr.Slider(0, 100),gr.Slider(0, 100),gr.Slider(0, 100),
+            gr.inputs.Dropdown(["Argentina" , "Saudi Arabia", "England"]),
+            gr.inputs.Dropdown(["FC Barcelona" , "Juventus", "Liverpool"])],
+            outputs=["number"])
+demo.launch()