Update app.py

app.py

@@ -3,34 +3,29 @@ import pickle
 import gradio as gr
 from sklearn.preprocessing import LabelEncoder
 
-# --- 1. Load the original dataset to get categorical values and create encoders ---
-# Make sure this path is correct if your dataset location has changed
+# --- Load dataset ---
 df_original = pd.read_csv('AB_ELEVATORS_DATASET_final.csv')
 
 categorical_cols_original = [
     'BRAND NAME', 'LIFT_TYPE', 'DOOR_FRAME', 'DOOR_TYPE', 'CABIN_MATERIAL', 'MOTOR_TYPE'
 ]
-numerical_cols_original = [
-    'PASSENGERS CAPACITY', 'SPEED_MPS', 'FLOORS'
-]
 
-# Store label encoders for each column
+# --- Create encoders ---
 encoders = {}
 for col in categorical_cols_original:
     le = LabelEncoder()
     le.fit(df_original[col])
     encoders[col] = le
 
-# Create mappings for Gradio dropdowns (original string values)
 categorical_options = {
     col: list(df_original[col].unique()) for col in categorical_cols_original
 }
 
-# --- 2. Load the trained model ---
+# --- Load model ---
 with open("elevator_model.pkl", "rb") as f:
     model = pickle.load(f)
 
-# --- 3. Define the prediction function ---
+# --- Prediction function ---
 def predict_price(
     brand_name_str,
     lift_type_str,
@@ -42,60 +37,104 @@ def predict_price(
     speed_mps,
     floors
 ):
-    # Encode categorical inputs using the fitted encoders
-    brand_name_encoded = encoders['BRAND NAME'].transform([brand_name_str])[0]
-    lift_type_encoded = encoders['LIFT_TYPE'].transform([lift_type_str])[0]
-    door_frame_encoded = encoders['DOOR_FRAME'].transform([door_frame_str])[0]
-    door_type_encoded = encoders['DOOR_TYPE'].transform([door_type_str])[0]
-    cabin_material_encoded = encoders['CABIN_MATERIAL'].transform([cabin_material_str])[0]
-    motor_type_encoded = encoders['MOTOR_TYPE'].transform([motor_type_str])[0]
-
-    # Create DataFrame with correct lowercased and snake_cased column names and order
-    input_data = pd.DataFrame([[
-        brand_name_encoded,
-        lift_type_encoded,
-        door_frame_encoded,
-        door_type_encoded,
-        cabin_material_encoded,
-        motor_type_encoded,
-        passengers_capacity,
-        speed_mps,
-        floors
-    ]], columns=[
-        'brand_name', 'lift_type', 'door_frame', 'door_type', 'cabin_material',
-        'motor_type', 'passengers_capacity', 'speed_mps', 'floors'
-    ])
-
-    prediction = model.predict(input_data)[0]
-    prediction = int(prediction)
-    return f"Predicted Price: ₹{prediction:,.2f}", "VISIT AGAIN 😊"
-
-# --- 4. Create Gradio Interface ---
-inputs = [
-    gr.Dropdown(categorical_options['BRAND NAME'], label="Brand Name", value=categorical_options['BRAND NAME'][0]),
-    gr.Dropdown(categorical_options['LIFT_TYPE'], label="Lift Type", value=categorical_options['LIFT_TYPE'][0]),
-    gr.Dropdown(categorical_options['DOOR_FRAME'], label="Door Frame", value=categorical_options['DOOR_FRAME'][0]),
-    gr.Dropdown(categorical_options['DOOR_TYPE'], label="Door Type", value=categorical_options['DOOR_TYPE'][0]),
-    gr.Dropdown(categorical_options['CABIN_MATERIAL'], label="Cabin Material", value=categorical_options['CABIN_MATERIAL'][0]),
-    gr.Dropdown(categorical_options['MOTOR_TYPE'], label="Motor Type", value=categorical_options['MOTOR_TYPE'][0]),
-    gr.Slider(minimum=df_original['PASSENGERS CAPACITY'].min(), maximum=df_original['PASSENGERS CAPACITY'].max(), step=1, label="Passengers Capacity", value=int(df_original['PASSENGERS CAPACITY'].mean())),
-    gr.Slider(minimum=df_original['SPEED_MPS'].min(), maximum=df_original['SPEED_MPS'].max(), step=0.1, label="Speed (MPS)", value=df_original['SPEED_MPS'].mean()),
-    gr.Slider(minimum=df_original['FLOORS'].min(), maximum=df_original['FLOORS'].max(), step=1, label="Floors", value=int(df_original['FLOORS'].mean()))
-]
+    try:
+        # Encode inputs
+        input_data = pd.DataFrame([[ 
+            encoders['BRAND NAME'].transform([brand_name_str])[0],
+            encoders['LIFT_TYPE'].transform([lift_type_str])[0],
+            encoders['DOOR_FRAME'].transform([door_frame_str])[0],
+            encoders['DOOR_TYPE'].transform([door_type_str])[0],
+            encoders['CABIN_MATERIAL'].transform([cabin_material_str])[0],
+            encoders['MOTOR_TYPE'].transform([motor_type_str])[0],
+            int(passengers_capacity),
+            float(speed_mps),
+            int(floors)
+        ]], columns=[
+            'brand_name', 'lift_type', 'door_frame', 'door_type',
+            'cabin_material', 'motor_type',
+            'passengers_capacity', 'speed_mps', 'floors'
+        ])
 
-outputs = [
-    gr.Textbox(label="Prediction Result"),
-    gr.Markdown()
-]
+        prediction = model.predict(input_data)[0]
+        price = float(prediction)
+
+        return (
+            f"### 💰 Estimated Price: ₹ {price:,.2f}",
+            "✅ Quote generated successfully"
+        )
+
+    except Exception as e:
+        return f"❌ Error: {str(e)}", "⚠️ Please check inputs"
+
+# --- Custom UI using Blocks ---
+with gr.Blocks(theme=gr.themes.Soft(), title="AB Elevators") as demo:
+
+    gr.Markdown("""
+    <div style="text-align:center; padding:20px;">
+        <h1 style="color:gold; text-shadow:2px 2px 5px black;">
+            🚀 AB ELEVATORS
+        </h1>
+        <h3>Smart Lift Price Prediction System</h3>
+        <p>Get instant quotations based on your requirements</p>
+    </div>
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            brand = gr.Dropdown(categorical_options['BRAND NAME'], label="🏢 Brand")
+            lift = gr.Dropdown(categorical_options['LIFT_TYPE'], label="🛗 Lift Type")
+            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")
+            cabin = gr.Dropdown(categorical_options['CABIN_MATERIAL'], label="🧱 Cabin Material")
+            motor = gr.Dropdown(categorical_options['MOTOR_TYPE'], label="⚙️ Motor Type")
+
+    gr.Markdown("### 🔢 Configuration")
+
+    with gr.Row():
+        passengers = gr.Slider(
+            minimum=int(df_original['PASSENGERS CAPACITY'].min()),
+            maximum=int(df_original['PASSENGERS CAPACITY'].max()),
+            step=1,
+            label="👥 Passengers Capacity",
+            value=int(df_original['PASSENGERS CAPACITY'].mean())
+        )
+
+        speed = gr.Slider(
+            minimum=float(df_original['SPEED_MPS'].min()),
+            maximum=float(df_original['SPEED_MPS'].max()),
+            step=0.1,
+            label="⚡ Speed (MPS)",
+            value=float(df_original['SPEED_MPS'].mean())
+        )
+
+        floors = gr.Slider(
+            minimum=int(df_original['FLOORS'].min()),
+            maximum=int(df_original['FLOORS'].max()),
+            step=1,
+            label="🏢 Floors",
+            value=int(df_original['FLOORS'].mean())
+        )
+
+    predict_btn = gr.Button("💡 Generate Quote", variant="primary")
+
+    output_price = gr.Markdown()
+    output_status = gr.Markdown()
+
+    predict_btn.click(
+        fn=predict_price,
+        inputs=[brand, lift, door_frame, door_type, cabin, motor,
+                passengers, speed, floors],
+        outputs=[output_price, output_status]
+    )
+
+    gr.Markdown("""
+    <hr>
+    <div style="text-align:center;">
+        <p>✨ Designed for AB Elevators | AI Powered Pricing</p>
+    </div>
+    """)
 
-iface = gr.Interface(
-    fn=predict_price,
-    inputs=inputs,
-    outputs=outputs,
-    title="AB ELEVATOR Company - Lift Quotation",
-    description="<h1 style='color: gold; border: 2px solid black; padding: 10px; text-shadow: -1px -1px 0 #000, 1px -1px 0 #000, -1px 1px 0 #000, 1px 1px 0 #000;'>WELCOME TO AB ELEVATORS</h1>", # Styled welcome message
-    live=False
-)
-
-# Launch the interface
-iface.launch()
+# --- Launch ---
+demo.launch()