| import os | |
| import joblib | |
| import gradio as gr | |
| import pandas as pd | |
| price_predictor = joblib.load('model-v1.joblib') | |
| carat_input = gr.Number(label="Carat") | |
| shape_input = gr.Dropdown( | |
| ['Round', 'Princess', 'Emerald', 'Asscher', 'Cushion', 'Radiant', 'Oval', | |
| 'Pear', 'Marquise'], | |
| label="Shape" | |
| ) | |
| cut_input = gr.Dropdown( | |
| ['Ideal', 'Premium', 'Very Good', 'Good', 'Fair'], | |
| label="Cut" | |
| ) | |
| color_input = gr.Dropdown( | |
| ['D', 'E', 'F', 'G', 'H', 'I', 'J'], | |
| label="Color" | |
| ) | |
| clarity_input = gr.Dropdown( | |
| ['IF', 'VVS1', 'VVS2', 'VS1', 'VS2', 'SI1', 'SI2', 'I1'], | |
| label="Clarity" | |
| ) | |
| report_input = gr.Dropdown(['GIA', 'IGI', 'HRD', 'AGS'], label="Report") | |
| type_input = gr.Dropdown(['Natural', 'Lab Grown'], label="Type") | |
| hf_token = os.environ["HF_TOKEN"] | |
| hf_writer = gr.HuggingFaceDatasetSaver(hf_token, "diamond-price-predictor-logs") | |
| model_output = gr.Label(label="Predicted Price (USD)") | |
| def predict_price(carat, shape, cut, color, clarity, report, type): | |
| sample = { | |
| 'carat': carat, | |
| 'shape': shape, | |
| 'cut': cut, | |
| 'color': color, | |
| 'clarity': clarity, | |
| 'report': report, | |
| 'type': type, | |
| } | |
| data_point = pd.DataFrame([sample]) | |
| prediction = price_predictor.predict(data_point).tolist() | |
| return prediction[0] | |
| demo = gr.Interface( | |
| fn=predict_price, | |
| inputs=[carat_input, shape_input, cut_input, color_input, | |
| clarity_input, report_input, type_input], | |
| outputs=model_output, | |
| theme=gr.themes.Soft(), | |
| title="Diamond Price Predictor", | |
| description="This API allows you to predict the price of a diamond given its attributes", | |
| allow_flagging="auto", | |
| flagging_callback=hf_writer, | |
| concurrency_limit=8 | |
| ) | |
| demo.queue() | |
| demo.launch(share=False) |