2024-02-09-15-39-57

app.py

@@ -0,0 +1,198 @@
+
+import os
+
+os.system("pip install pandas")
+os.system("pip install gradio")
+os.system("pip install scikit-learn==1.3.2 matplotlib")
+
+import gradio as gr
+import pandas as pd
+import pickle
+
+connections_list = ['2G', '3G', '4G', '5G', 'WIFI', 'BLUETOOTH', 'GPS', 'NFC', 'RADIO', 'FACE_UNLOCK', 'SINGLE_SOM', 'USB_TYPE_C', 'OTG', 'USB', 'TECH', 'DUAL_SIM', 'ETHERNET']
+colors_list = ['BLACK', 'WHITE', 'BLUE', 'PURPLE', 'GOLD', 'GREEN', 'YELLOW', 'RED', 'ORANGE', 'PEACH', 'SUNLIGHT', 'STARLIGHT', 'NAVY', 'KHAKI', 'HAZEL', 'RAINFOREST', 'CHARCOAL', 'SNOW', 'SEA', 'CORAL', 'COPPER', 'BROWN', 'TRANSPARENT', 'MINT', 'GRAPHITE', 'CREAM', 'LAVENDER', 'GRAY', 'LIME', 'CYAN', 'INDIGO', 'TANGERINE', 'VIOLET', 'SILVER', 'MIDNIGHT', 'PLATINUM', 'AURORA', 'PORCELAIN', 'OBSIDIAN', 'CHALK', 'SAGE', 'OLIVE', 'DUSK', 'TURQUOISE', 'SAND', 'NEON', 'VOILET', 'TITANIUM', 'NIGHT', 'STEEL', 'MIST', 'PEARL', 'GRADATION', 'TWILIGHT.', 'LEMONGRASS', 'GREY', 'PINK', 'BRONZE', 'NO_COLOR', 'OTHER_COLORS']
+processors_list = ['UNISOC', 'QUALCOMM', 'MEDIATEK', 'OCTA_CORE', 'QUAD_CORE', 'HEXA_CORE', 'EXYNOS', 'GOOGLE_TENSOR', 'APPLE_M_CHIP', 'APPLE_A_CHIP', 'HUAWEI', 'SAMSUNG', 'INTEL', 'DIMENSITY', 'OTHERS']
+brands_list = ['XIAOMI', 'SAMSUNG', 'TECNO', 'INFINIX', 'OPPO', 'APPLE', 'NOKIA', 'ONEPLUS', 'HUAWEI', 'REALME', 'XTIGI', 'ITEL', 'VIVO', 'GOOGLE_PIXEL', 'ASUS', 'LENOVO', 'ZTE', 'OTHERS']
+model_column_list = ['ram', 'internal_storage', 'battery', 'main_camera', 'front_camera', 'display', 'connectivity_2G', 'connectivity_3G', 'connectivity_4G', 'connectivity_5G', 'connectivity_BLUETOOTH', 'connectivity_DUAL_SIM', 'connectivity_ETHERNET', 'connectivity_FACE_UNLOCK', 'connectivity_GPS', 'connectivity_NFC', 'connectivity_OTG', 'connectivity_RADIO', 'connectivity_SINGLE_SOM', 'connectivity_TECH', 'connectivity_USB', 'connectivity_USB_TYPE_C', 'connectivity_WIFI', 'colors_AURORA', 'colors_BLACK', 'colors_BLUE', 'colors_BRONZE', 'colors_BROWN', 'colors_CHALK', 'colors_CHARCOAL', 'colors_COPPER', 'colors_CORAL', 'colors_CREAM', 'colors_CYAN', 'colors_DUSK', 'colors_GOLD', 'colors_GRADATION', 'colors_GRAPHITE', 'colors_GRAY', 'colors_GREEN', 'colors_GREY', 'colors_HAZEL', 'colors_INDIGO', 'colors_KHAKI', 'colors_LAVENDER', 'colors_LEMONGRASS', 'colors_LIME', 'colors_MIDNIGHT', 'colors_MINT', 'colors_MIST', 'colors_NAVY', 'colors_NEON', 'colors_NIGHT', 'colors_NO_COLOR', 'colors_OBSIDIAN', 'colors_OLIVE', 'colors_ORANGE', 'colors_OTHER_COLORS', 'colors_PEACH', 'colors_PEARL', 'colors_PINK', 'colors_PLATINUM', 'colors_PORCELAIN', 'colors_PURPLE', 'colors_RAINFOREST', 'colors_RED', 'colors_SAGE', 'colors_SAND', 'colors_SEA', 'colors_SILVER', 'colors_SNOW', 'colors_STARLIGHT', 'colors_STEEL', 'colors_SUNLIGHT', 'colors_TANGERINE', 'colors_TITANIUM', 'colors_TRANSPARENT', 'colors_TURQUOISE', 'colors_TWILIGHT.', 'colors_VIOLET', 'colors_VOILET', 'colors_WHITE', 'colors_YELLOW', 'brand_APPLE', 'brand_ASUS', 'brand_GOOGLE_PIXEL', 'brand_HUAWEI', 'brand_INFINIX', 'brand_ITEL', 'brand_LENOVO', 'brand_NOKIA', 'brand_NO_BRAND', 'brand_ONEPLUS', 'brand_OPPO', 'brand_OTHERS', 'brand_REALME', 'brand_SAMSUNG', 'brand_TECNO', 'brand_VIVO', 'brand_XIAOMI', 'brand_XTIGI', 'brand_ZTE', 'processor_APPLE_A_CHIP', 'processor_APPLE_M_CHIP', 'processor_DIMENSITY', 'processor_EXYNOS', 'processor_GOOGLE_TENSOR', 'processor_HEXA_CORE', 'processor_HUAWEI', 'processor_INTEL', 'processor_MEDIATEK', 'processor_OCTA_CORE', 'processor_OTHERS', 'processor_QUAD_CORE', 'processor_QUALCOMM', 'processor_SAMSUNG', 'processor_UNISOC']
+with open('./model_results.pkl', 'rb') as f:
+    model_results = pickle.load(f)
+
+def prediction_factory(launch=True):
+    import traceback
+    import pandas as pd
+    import gradio as gr
+    import json
+
+    def fn(model, data: dict):
+        try:
+            data_df = pd.DataFrame([data])
+            onehot_data_df = pd.get_dummies(
+                data_df.explode('connectivity'),
+                columns=['connectivity', 'colors', 'brand', 'processor']).copy()
+            columns_not_in_data = [col for col in model_column_list if col not in onehot_data_df.columns]
+            onehot_data_df[columns_not_in_data] = [0] * len(columns_not_in_data) # Add the missing column with the default value
+            onehot_data_df = onehot_data_df[model_column_list] # To ensure the order of columns is the same as the training set
+            [predicted_price,*_] = model.predict(onehot_data_df)
+            return f"Predicted selling price: {predicted_price:,.0f} Ksh"
+        except Exception as e:
+            error = traceback.format_exc()
+            return error
+    if not launch:
+        return fn
+
+    ###########################################################
+    ################### Start of Gradio setup #################
+    ###########################################################
+    title = "Mobile Phone Price Prediction"
+    css = '''
+    .prediction-output {
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        justify-items: center;
+    }'''
+    formatted_model_results = [(f'{name} - rmse: {model_data["rmse"]}', model_data['model']) for name, model_data in model_results]
+    with gr.Blocks(title = title, css = css) as app:
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown(f"# {title}")
+                gr.Markdown("### Fill in the details of the mobile phone to get the predicted price")
+                model_name = gr.Dropdown(
+                    choices = [name for name,_ in formatted_model_results],
+                    value = formatted_model_results[0][0],
+                    label = "Model",
+                    info = "Select the model to use for the prediction")
+                connectivity = gr.Dropdown(
+                    choices = connections_list,
+                    value = [],
+                    multiselect = True,
+                    label = "Connectivity",
+                    info = "Select the connectivity options of the mobile phone")
+                colors = gr.Dropdown(
+                    choices = colors_list,
+                    value = '',
+                    label = "Colors",
+                    info = "Select the colors of the mobile phone")
+                processor = gr.Dropdown(
+                    choices = processors_list,
+                    value = '',
+                    label = "Processor",
+                    info = "Select the processor of the mobile phone")
+                brand = gr.Dropdown(
+                    choices = brands_list,
+                    value = '',
+                    label = "Brand",
+                    info = "Select the brand of the mobile phone")
+                ram = gr.Slider(
+                    0,
+                    100,
+                    label="RAM",
+                    info="Select the RAM of the mobile phone (in GB)")
+                internal_storage= gr.Slider(
+                    0,
+                    1000,
+                    label="Internal Storage",
+                    info="Select the internal storage of the mobile phone (in GB)")
+                battery = gr.Slider(
+                    0,
+                    10000,
+                    label="Battery",
+                    info="Select the battery capacity of the mobile phone (in mAh)")
+                main_camera = gr.Slider(
+                    0,
+                    100,
+                    label="Main Camera",
+                    info="Select the main camera capacity of the mobile phone (in MP)")
+                front_camera = gr.Slider(
+                    0,
+                    100,
+                    label="Front Camera",
+                    info="Select the front camera capacity of the mobile phone (in MP)")
+                display = gr.Slider(
+                    0,
+                    10,
+                    label="Display",
+                    info="Select the display size of the mobile phone (in inches)")
+                def fn_proxy(*args):
+                    # convert args to dict
+                    keys = ['connectivity', 'colors', 'processor', 'brand', 'ram', 'internal_storage', 'battery', 'main_camera', 'front_camera', 'display']
+                    data = {key: value for key, value in zip(keys, args)}
+                    data['ram'] *= 1024 # convert to MB
+                    data['internal_storage'] *= 1024
+                    model_name_ = args[-1]
+                    model = [model for name, model in formatted_model_results if name == model_name_][0]
+                    result = fn(model, data)
+                    return f'''<div class="prediction-output">
+                    <div>
+                    <h1>Model Prediction</h1>
+                    <h3>{result}</h3>
+                    <h3>Model Name: {model_name_}</h3>
+                    <pre>{json.dumps(data, indent = 4)}</pre>
+                    </div>
+                    </div>
+                    '''
+            with gr.Column():
+                value = fn_proxy(
+                    connectivity.value,
+                    colors.value,
+                    processor.value,
+                    brand.value,
+                    ram.value,
+                    internal_storage.value,
+                    battery.value,
+                    main_camera.value,
+                    front_camera.value,
+                    display.value,
+                    model_name.value)
+                prediction = gr.HTML(value=value, label="Prediction")
+            model_name.change(
+                fn=fn_proxy,
+                inputs=[connectivity, colors, processor, brand, ram, internal_storage, battery, main_camera, front_camera, display, model_name],
+                outputs=[prediction])
+            connectivity.change(
+                fn=fn_proxy,
+                inputs=[connectivity, colors, processor, brand, ram, internal_storage, battery, main_camera, front_camera, display, model_name],
+                outputs=[prediction])
+            colors.change(
+                fn=fn_proxy,
+                inputs=[connectivity, colors, processor, brand, ram, internal_storage, battery, main_camera, front_camera, display, model_name],
+                outputs=[prediction])
+            processor.change(
+                fn=fn_proxy,
+                inputs=[connectivity, colors, processor, brand, ram, internal_storage, battery, main_camera, front_camera, display, model_name],
+                outputs=[prediction])
+            brand.change(
+                fn=fn_proxy,
+                inputs=[connectivity, colors, processor, brand, ram, internal_storage, battery, main_camera, front_camera, display, model_name],
+                outputs=[prediction])
+            ram.change(
+                fn=fn_proxy,
+                inputs=[connectivity, colors, processor, brand, ram, internal_storage, battery, main_camera, front_camera, display, model_name],
+                outputs=[prediction])
+            internal_storage.change(
+                fn=fn_proxy,
+                inputs=[connectivity, colors, processor, brand, ram, internal_storage, battery, main_camera, front_camera, display, model_name],
+                outputs=[prediction])
+            battery.change(
+                fn=fn_proxy,
+                inputs=[connectivity, colors, processor, brand, ram, internal_storage, battery, main_camera, front_camera, display, model_name],
+                outputs=[prediction])
+            main_camera.change(
+                fn=fn_proxy,
+                inputs=[connectivity, colors, processor, brand, ram, internal_storage, battery, main_camera, front_camera, display, model_name],
+                outputs=[prediction])
+            front_camera.change(
+                fn=fn_proxy,
+                inputs=[connectivity, colors, processor, brand, ram, internal_storage, battery, main_camera, front_camera, display, model_name],
+                outputs=[prediction])
+            display.change(
+                fn=fn_proxy,
+                inputs=[connectivity, colors, processor, brand, ram, internal_storage, battery, main_camera, front_camera, display, model_name],
+                outputs=[prediction])
+    ###########################################################
+    ################### End of Gradio setup ###################
+    ###########################################################
+    return app.launch
+
+
+prediction_factory()()