finishing aap correction

app.py

@@ -592,8 +592,8 @@ elif page == "💸 Price Prediction":
 
     # Define input fields for car parameters
     car_model = st.selectbox("Car Brand:", ['Citroën','Peugeot','PGO','Renault','Audi','BMW','Mercedes','Opel','Volkswagen','Ferrari','Mitsubishi','Nissan','SEAT','Subaru','Toyota','other'])
-    mileage = st.slider("Mileage (km):", 0, 800000, 50000, step=1)
-    engine_power = st.slider("Engine Power (HP):", 0, 1000, 100, step=1)
+    mileage = st.slider("Mileage (km):", 0, 600000, 50000, step=1000)
+    engine_power = st.slider("Engine Power (HP):", 0, 1000, 100, step=5)
     fuel = st.selectbox("Fuel Type:", ['diesel','petrol','other'])
     paint_color = st.selectbox("Paint Color:", ['black','grey','white','red','silver','blue','beige','brown','other'])
     car_type = st.selectbox("Car Type:", ['convertible','coupe','estate','hatchback','sedan','subcompact','suv','van'])
@@ -612,7 +612,7 @@ elif page == "💸 Price Prediction":
         st.subheader("💶 Prediction Results")
 
         # Prepare input data as JSON
-        input_data = {
+        input_data = pd.DataFrame({
             "model_key": car_model,
             "mileage": mileage,
             "engine_power": engine_power,
@@ -626,7 +626,7 @@ elif page == "💸 Price Prediction":
             "has_getaround_connect": has_getaround_connect,
             "has_speed_regulator": has_speed_regulator,
             "winter_tires": winter_tires
-        }
+        })
 
         # Load the preprocessor
         with open('preprocessor.pkl', 'rb') as file: