Update app.py

app.py

@@ -2,6 +2,7 @@ import pandas as pd
 import pickle
 import gradio as gr
 from sklearn.preprocessing import LabelEncoder
+import os
 
 # --- Load dataset ---
 df_original = pd.read_csv('AB_ELEVATORS_DATASET_final.csv')
@@ -66,6 +67,18 @@ def predict_price(
     except Exception as e:
         return f"❌ Error: {str(e)}", "⚠️ Please check inputs"
 
+#Function to get image path from dropdown
+def get_image_path(category, selection):
+    if selection is None:
+        return None
+    
+    # Convert dropdown value → UPPERCASE filename
+    filename = selection.upper().replace(" ", "_") + ".jpg"
+    
+    path = os.path.join("images", category, filename)
+    
+    return path if os.path.exists(path) else None
+
 # --- Custom UI using Blocks ---
 with gr.Blocks(theme=gr.themes.Soft(), title="AB Elevators") as demo:
 
@@ -83,12 +96,15 @@ with gr.Blocks(theme=gr.themes.Soft(), title="AB Elevators") as demo:
         with gr.Column():
             brand = gr.Dropdown(categorical_options['BRAND NAME'], label="🏢 Brand")
             lift = gr.Dropdown(categorical_options['LIFT_TYPE'], label="🛗 Lift Type")
+            lift_img = gr.Image(label="Lift Type Preview", height=150) #Lift_Type Image Display
             door_frame = gr.Dropdown(categorical_options['DOOR_FRAME'], label="🚪 Door Frame")
 
         with gr.Column():
             door_type = gr.Dropdown(categorical_options['DOOR_TYPE'], label="🚪 Door Type")
+            door_img = gr.Image(label="Door Type Preview", height=150)
             cabin = gr.Dropdown(categorical_options['CABIN_MATERIAL'], label="🧱 Cabin Material")
             motor = gr.Dropdown(categorical_options['MOTOR_TYPE'], label="⚙️ Motor Type")
+            motor_img = gr.Image(label="Motor Type Preview", height=150)
 
     gr.Markdown("### 🔢 Configuration")
 
@@ -122,6 +138,25 @@ with gr.Blocks(theme=gr.themes.Soft(), title="AB Elevators") as demo:
     output_price = gr.Markdown()
     output_status = gr.Markdown()
 
+    # --- Image Updates ---
+    lift.change(
+        fn=lambda x: get_image_path("Lift_Type", x),
+        inputs=lift,
+        outputs=lift_img
+    )
+
+    door_type.change(
+        fn=lambda x: get_image_path("Door_Type", x),
+        inputs=door_type,
+        outputs=door_img
+    )
+
+    motor.change(
+        fn=lambda x: get_image_path("Motor_Type", x),
+        inputs=motor,
+        outputs=motor_img
+    )    
+
     predict_btn.click(
         fn=predict_price,
         inputs=[brand, lift, door_frame, door_type, cabin, motor,