testing gender - get first character only

app.py

@@ -43,11 +43,12 @@ def predict_amputation(age, gender, race, diabetes_type):
 	#input_dict = {"AGE": 80, "GENDER": "F", "RACE": "Asian", "DIABETES_CLASS":"Type 2 diabetes", "AMPUTATION":''}
 	
 	diabetes_class = "Type "+str(diabetes_type)+" diabetes"
+	gender = gender[0]
 	input_dict = {"AGE": age, "GENDER": gender, "RACE": race, "DIABETES_CLASS":diabetes_class, "AMPUTATION":''}
 	
 	input_df = pd.DataFrame([input_dict])
 	
-	return str(predict(model=model, input_df=input_df))
+	return gender#str(predict(model=model, input_df=input_df))
 	#return str("ALLAH " + " " + str(age) + " " + gender + " " + race + diabetes_type)
 	#return diabetes_type
 	#return "ALLAH: "+str(predict(model=model, input_df=input_df)) # calls the predict function when 'submit' is clicked
@@ -71,7 +72,7 @@ iface = gr.Interface(
 		article=article,
 		inputs=[gr.inputs.Slider(minimum=0,maximum=100, step=1, default=0, label="Age"), gr.inputs.Dropdown(["Female", "Male"], default="Female", label="Gender"), gr.inputs.Dropdown(["Asian", "Black", "Coloured", "White", "Other"], default="Asian", label="Race"), gr.inputs.Dropdown(["1", "2"], default="1", label="Diabetes Type")], 
 		outputs="text",
-		theme="darkhuggingface",
+		theme="huggingface",
 		examples=[
 			[50, "Male", "Black", 2],
 			[76, "Female", "Asian", 2],