Update app.py

app.py

@@ -14,9 +14,13 @@ mlp_model = load(open('mlp_classifier.pkl', 'rb'))
 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):
+description = '''
+This small prototype is using Big Data and AI to provide an accurate estimate of FootBall player net worth in euros.
+'''
+
+def predict_value(age,height_cm,weight_kg,overall_skill,potential_skill,nationality,club):
   #pre-processing:
-  numerical_features = [[age,height_cm,weight_kg,overall,potential]]
+  numerical_features = [[age,height_cm,weight_kg,overall_skill,potential_skill]]
   catagorical_features = [[nationality,club]]
   numerical_features = my_scaler.transform(numerical_features)  
   catagorical_features = hot_enc_scaler.transform(catagorical_features).toarray()
@@ -29,7 +33,10 @@ def predict_value(age,height_cm,weight_kg,overall,potential,nationality,club):
 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","Egypt"]),
-            gr.inputs.Dropdown(["FC Barcelona" , "Juventus", "Liverpool","Al Hilal","Al Nassr"])],
-            outputs=["number"])
+            gr.inputs.Dropdown(["Argentina" , "Saudi Arabia", "England"]),
+            gr.inputs.Dropdown(["FC Barcelona" , "Juventus", "Liverpool"])],
+            outputs=[gr.Number(label='Net Worth (Euros)')],
+            title= "TalentAI - Estimate FB Player Value (Eur)",
+            description = description,
+            article = "Abdulaziz Alakooz developed this prototype as part of Thkaa AI in sports contest - August 2022.")
 demo.launch()