Create app.py

app.py

@@ -0,0 +1,126 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import pickle
+
+categorical_features = ['Manufacturer', 'CPU', 'RAM Type', "Screen Resolution"]
+numerical_features = ['CPU Speed (GHz)', 'RAM (GB)', 'Bus (MHz)', 'Storage (GB)', 'CPU brand modifier',
+                      'Screen Size (inch)', 'Refresh Rate (Hz)', 'Weight (kg)', 'Battery']
+label = ['Price (VND)']
+
+with open("./checkpoint/ohe.pkl", "rb") as f:
+    ohe = pickle.load(f)
+
+
+def load_model(model):
+    if model == "XGBRegressor":
+        with open("./checkpoint/XGBRegressor.pkl", "rb") as f:
+            pred_model = pickle.load(f)
+    elif model == "RandomForestRegressor":
+        with open("./checkpoint/RandomForestRegressor.pkl", "rb") as f:
+            pred_model = pickle.load(f)
+    elif model == "GradientBoostingRegressor":
+        with open("./checkpoint/GradientBoostingRegressor.pkl", "rb") as f:
+            pred_model = pickle.load(f)
+    return pred_model
+
+
+def predict(brand, cpu, cpu_brand_type, cpu_hz, ram_type, ram, ram_bus,
+            screen_resolution, refresh_rate, screen_size,
+            storage, battery, weight, model
+            ):
+    pred_model = load_model(model)
+    cate_data = {
+        "Manufacturer": [brand],
+        "CPU": [cpu],
+        "RAM Type": [ram_type],
+        "Screen Resolution": [screen_resolution]
+    }
+    nume_data = {
+        "CPU Speed (GHz)": [cpu_hz],
+        "RAM (GB)": [ram],
+        "Bus (MHz)": [ram_bus],
+        "CPU brand modifier": [cpu_brand_type],
+        "Screen Size (inch)": [screen_size],
+        "Refresh Rate (Hz)": [refresh_rate],
+        "Storage (GB)": [storage],
+        "Battery": [battery],
+        "Weight (kg)": [weight]
+    }
+    cate_data = pd.DataFrame(cate_data)
+    nume_data = pd.DataFrame(nume_data)
+    cate_data = ohe.transform(cate_data)
+    cate_data = pd.DataFrame(cate_data, columns=ohe.get_feature_names_out())
+    data = pd.concat([cate_data, nume_data], axis=1)
+    return round(float(pred_model.predict(np.array(data))[0]), 2)
+
+
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="green")) as demo:
+    # add gr title to the middle of the page
+    gr.Markdown("# Laptop Price Prediction")
+
+    with gr.Row():
+        model = gr.Dropdown(
+            label="Model",
+            choices=["XGBRegressor", "RandomForestRegressor", "GradientBoostingRegressor"],
+            value="XGBRegressor",
+        )
+
+        Brand = gr.Radio(
+            label="Brand",
+            choices=['acer', 'asus', 'dell', 'hp', 'lenovo', 'lg', 'msi'],
+            value='acer'
+        )
+    gr.Markdown("## **CPU**")
+    with gr.Row():
+
+        CPUBrand = gr.Dropdown(label="CPU", choices=[
+            "AMD Gen 4.0th", "AMD Gen 5.0th", "AMD Gen 6.0th", "AMD Gen 7.0th",
+            "Intel Gen 11.0th", "Intel Gen 12.0th", "Intel Gen 13.0th"],
+            value="Intel Gen 12.0th"
+        )
+        CPUHz = gr.Slider(label="CPU Speed (GHz)", minimum=1.0, maximum=5.0, step=0.1, value=2.0, interactive=True)
+        CPUBrandType = gr.Radio(label="CPU Type", choices=[3, 5, 7, 9], value=7)
+
+    gr.Markdown("## **RAM**")
+    with gr.Row():
+        RAMType = gr.Dropdown(
+            label="RAM Type",
+            choices=["DDR4", "LPDDR4", "LPDDR4X", "DDR5", "LPDDR5", "LPDDR5X"],
+            value="DDR5"
+        )
+        RAMBus = gr.Slider(label="Bus (MHz)", minimum=1600, maximum=6400, step=400, value=3200, interactive=True)
+        RAM = gr.Radio(label="RAM (GB)", choices=[8, 16, 32, 64, 128], value=16)
+    gr.Markdown("## **Screen**")
+    with gr.Row():
+        ScreenResolution = gr.Dropdown(
+            label="Screen Resolution",
+            choices=["1366x768", "1920x1080", "1920x1200", "2560x1440", "2560x1600", "3840x2160"],
+            value="1920x1080"
+        )
+        ScreenSize = gr.Radio(label="Screen Size (inch)", choices=[13.3, 14.0, 15.6, 17.3], value=15.6)
+        RefreshRate = gr.Radio(label="Refresh Rate (Hz)", choices=[60, 120, 144, 240], value=60)
+
+    gr.Markdown("## **Other Features**")
+    with gr.Row():
+        Battery = gr.Radio(label="Battery (Wh)", choices=[40, 50, 60, 70, 80])
+        Weight = gr.Radio(label="Weight (kg)", choices=[1.0, 1.5, 2.0, 2.5, 3.0])
+        Storage = gr.Radio(label="Storage (GB)", choices=[256, 512, 1024, 2048], value=512)
+    # Output Prediction
+    gr.Markdown("## **Prediction**")
+    with gr.Row():
+
+        output = gr.Number(label="Prediction (Million VND)", info="Click Submit to predict")
+    with gr.Row():
+        submit_button = gr.Button("Submit")
+        submit_button.click(fn=predict,
+                            outputs=output,
+                            inputs=[Brand, CPUBrand, CPUBrandType, CPUHz, RAMType, RAM, RAMBus,
+                                    ScreenResolution, RefreshRate, ScreenSize, Storage, Battery, Weight, model
+                                    ],
+                            queue=True,
+                            )
+        clear_button = gr.ClearButton(components=[output], value="Clear")
+
+if __name__ == "__main__":
+    demo.launch(max_threads=50, debug=True, prevent_thread_lock=True, show_error=True, share=True)