Create app.py

app.py

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+import pandas as pd
+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
+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
+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 ---
+with open("elevator_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# --- 3. Define the prediction function ---
+def predict_price(
+    brand_name_str,
+    lift_type_str,
+    door_frame_str,
+    door_type_str,
+    cabin_material_str,
+    motor_type_str,
+    passengers_capacity,
+    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]
+    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()))
+]
+
+outputs = [
+    gr.Textbox(label="Prediction Result"),
+    gr.Markdown()
+]
+
+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()