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app.py

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+import gradio as gr
+import joblib
+import numpy as np
+import pandas as pd
+
+# Load the model and unique brand values
+model = joblib.load('model.joblib')
+unique_values = joblib.load('unique_values.joblib')
+brand_values = unique_values['Brand']
+
+# Define the prediction function
+def predict(brand, screen_size, resolution_width, resolution_height):
+    # Convert inputs to appropriate types
+    screen_size = float(screen_size)
+    resolution_width = int(resolution_width)
+    resolution_height = int(resolution_height)
+    
+    # Prepare the input array for prediction
+    input_data = pd.DataFrame({
+        'Brand': [brand],
+        'Screen Size': [screen_size],
+        'Resolution (Width)': [resolution_width],
+        'Resolution (Height)': [resolution_height]
+    })
+    
+    # Perform the prediction
+    prediction = model.predict(input_data)
+    
+    return prediction[0]
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Dropdown(choices=list(brand_values), label="Brand"),
+        gr.Textbox(label="Screen Size"),
+        gr.Textbox(label="Resolution (Width)"),
+        gr.Textbox(label="Resolution (Height)")
+    ],
+    outputs="text",
+    title="Monitor Predictor",
+    description="Enter the brand, screen size, and resolution to predict the target value."
+)
+
+# Launch the app
+interface.launch()

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f3791b6924a3511c032ec93116e277ecda73f7a7e745a3c8dd170fd069dd1a62
+size 165387

requirements.txt

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+joblib
+pandas
+scikit-learn==1.3.2
+xgboost==2.1.1

unique_values.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1fae982d906ebe09bb185149a43b296d40305dd74a80eb1c2731dd1ad1f32589
+size 1203