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| import modal | |
| from PIL import Image | |
| import gradio as gr | |
| MODEL_VERSION = 6 | |
| LOCAL = True | |
| if LOCAL == False: | |
| hopsworks_image = modal.Image.debian_slim(python_version='3.9').pip_install(["gradio", "requests", "hopsworks", "joblib", "pandas", "scikit-learn==1.1.1"]) | |
| stub = modal.Stub("wine_prediction_user_interface") | |
| def f(): | |
| g() | |
| def g(): | |
| import requests | |
| import hopsworks | |
| import joblib | |
| import pandas as pd | |
| project = hopsworks.login() | |
| fs = project.get_feature_store() | |
| mr = project.get_model_registry() | |
| model = mr.get_model("wine_model", version=MODEL_VERSION) | |
| model_dir = model.download() | |
| model = joblib.load(model_dir + "/wine_model.pkl") | |
| print("Model downloaded") | |
| def wine(fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide, | |
| total_sulfar_dioxide, density, ph, sulphates, alcohol, color): | |
| print("Calling function") | |
| # df = pd.DataFrame([[sepal_length],[sepal_width],[petal_length],[petal_width]], | |
| df = pd.DataFrame([[fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide, | |
| total_sulfar_dioxide, density, ph, sulphates, alcohol, color]], | |
| columns=['fixed_acidity', 'volatile_acidity', 'citric_aicd', 'residual_sugar', 'chlorides', 'free_sulfur_dioxide', | |
| 'total_sulfur_dioxide', 'density', 'ph', 'sulphates', 'alcohol', 'color']) | |
| print("Predicting") | |
| print(df) | |
| # 'res' is a list of predictions returned as the label. | |
| res = model.predict(df) | |
| # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want | |
| # the first element. | |
| # print("Res: {0}").format(res) | |
| print(res) | |
| return res | |
| demo = gr.Interface( | |
| fn=wine, | |
| title="Wine Quality Predictive Analytics", | |
| description="Experiment with several parameters to predict the quality of wine.", | |
| allow_flagging="never", | |
| inputs=[ | |
| gr.Number(value=1.0, label="fixed_acidity [3.8,15.9]"), | |
| gr.Number(value=1.0, label="volatile_acidity [0.1,1.6]"), | |
| gr.Number(value=1.0, label="citric_aicd [0.0,1.6]"), | |
| gr.Number(value=1.0, label="residual_sugar [0.6,65.8]"), | |
| gr.Number(value=1.0, label="chlorides [0.0,0.6]"), | |
| gr.Number(value=1.0, label="free_sulfur_dioxide [1.0,289.0]"), | |
| gr.Number(value=1.0, label="total_sulfur_dioxide [6.0,440.0]"), | |
| gr.Number(value=1.0, label="density [0.9,1.0]"), | |
| gr.Number(value=1.0, label="ph [2.7,4.0]"), | |
| gr.Number(value=1.0, label="sulphates [0.2,2.0]"), | |
| gr.Number(value=1.0, label="alcohol [8.0,14.9]"), | |
| gr.Number(value=1.0, label="color (0:red, 1:white)"), | |
| ], | |
| outputs=gr.Number(label="predicted quality")) | |
| demo.launch(debug=True) | |
| if __name__ == "__main__": | |
| if LOCAL == True : | |
| g() | |
| else: | |
| modal.runner.deploy_stub(stub) | |
| with stub.run(): | |
| f.remote() |