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6b63915 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | # !pip install gradio ipywidgets
import pandas as pd
import gradio as gr
import joblib
import numpy as np
# "Artifacts"
pipeline = joblib.load("pipeline.joblib")
label_pipeline = joblib.load("label_pipeline.joblib")
cities = joblib.load("cities.joblib")
classes = joblib.load("classes.joblib")
def predict(city, location, area, bedrooms, baths):
sample = dict()
sample["city"] = city
sample["location"] = location
sample["area"] = area # Column names matching feature names
sample["bedrooms"] = bedrooms
sample["baths"] = baths
sample = pd.DataFrame([sample])
y_pred = pipeline.predict_proba(sample)[0]
y_pred = dict(zip(classes, y_pred))
return y_pred
# https://www.gradio.app/guides
with gr.Blocks() as demo: #value คือ ค่าเริ่มต้น
city = gr.Dropdown(cities, value=cities[0], label="City")
location = gr.Textbox(label="Location", placeholder="E.g. Bangkhen")
area = gr.Number(label="Area", value=0.5, minimum=0.5, step=0.5)
bedrooms = gr.Slider(value=1, label="Bedrooms", minimum=0, maximum=10, step=1)
baths = gr.Slider(value=1, label="Baths", minimum=0, maximum=10, step=1)
# with gr.Row():
# city_init = np.random.choice(cities)
# city = gr.Dropdown(cities, value=city_init, label="City")
# location = gr.Textbox(label="Location", placeholder="E.g. Bangken")
# with gr.Row():
# area_init = np.random.choice(np.arange(0, 50, 0.5))
# area = gr.Number(label="Area", value=area_init, minimum=0.5, step=0.5)
# bedrooms_init = np.random.choice(np.arange(0, 10, 1))
# bedrooms = gr.Slider(value=bedrooms_init, label="Bedrooms", minimum=0, maximum=10, step=1)
# baths_init = np.random.choice(np.arange(0, 10, 1))
# baths = gr.Slider(value=baths_init, label="Baths", minimum=0, maximum=10, step=1)
predict_btn = gr.Button("Predict", variant="primary")
price = gr.Label(label="Price")
inputs = [city, location, area, bedrooms, baths]
outputs = [price]
predict_btn.click(predict, inputs=inputs, outputs=outputs)
if __name__ == "__main__":
demo.launch() # Local machine only
# demo.launch(server_name="0.0.0.0") # LAN access to local machine
# demo.launch(share=True) # Public access to local machine
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