| import gradio as gr |
| import numpy as np |
| from PIL import Image |
| import requests |
|
|
| import hopsworks |
| import joblib |
|
|
| project = hopsworks.login() |
| fs = project.get_feature_store() |
|
|
|
|
| mr = project.get_model_registry() |
| model = mr.get_model("titan_modal", version=50) |
| model_dir = model.download() |
| model = joblib.load(model_dir + "/titan_model.pkl") |
|
|
|
|
| def titan(pclass, sex, age, fare, famliy): |
| input_list = [] |
| input_list.append(pclass) |
| input_list.append(sex) |
| input_list.append(age) |
| input_list.append(fare) |
| input_list.append(famliy) |
| |
| res = model.predict(np.asarray(input_list).reshape(1, -1)) |
| |
| |
| survivor_url = "https://raw.githubusercontent.com/Chaouo/Titanic_serverless_ML/main/image/"+ str(res[0]) + ".png" |
| img = Image.open(requests.get(survivor_url, stream=True).raw) |
| return img |
| |
| demo = gr.Interface( |
| fn=titan, |
| title="Titanic Survival Predictive Analytics", |
| description="Experiment with pclass, sex, age, fare, famliy to predict which flower it is.", |
| allow_flagging="never", |
| inputs=[ |
| gr.inputs.Number(default=1.0, label="pclass (1-3)"), |
| gr.inputs.Number(default=1.0, label="sex (0 indecates male and 1 indecates female)"), |
| gr.inputs.Number(default=1.0, label="age"), |
| gr.inputs.Number(default=1.0, label="fare (0-512)"), |
| gr.inputs.Number(default=1.0, label="famliy (numbers)"), |
| ], |
| outputs=gr.Image(type="pil")) |
|
|
| demo.launch() |
|
|
|
|
