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import gradio as gr
from PIL import Image
import hopsworks
#login to hopswork
project = hopsworks.login()
fs = project.get_feature_store()
#get the dataset api
dataset_api = project.get_dataset_api()
#downloads form dataset the predicted wine, the actual wine, the recent file and the confusion matrix
dataset_api.download("Resources/texts/prediction.txt", overwrite=True)
dataset_api.download("Resources/texts/label.txt", overwrite=True)
dataset_api.download("Resources/images/wine/df_recent.png", overwrite=True)
dataset_api.download("Resources/images/wine/confusion_matrix.png", overwrite=True)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
gr.Label("Today's Predicted quality")
f = open("prediction.txt", "r")
string = f.readline()
f.close()
input_img = gr.Textbox(string , elem_id="predicted-quality")
with gr.Column():
gr.Label("Today's Actual quality")
f = open("label.txt", "r")
string = f.readline()
f.close()
input_img = gr.Textbox(string, elem_id="actual-quality")
with gr.Row():
with gr.Column():
gr.Label("Recent Prediction History")
input_img = gr.Image("df_recent.png", elem_id="recent-predictions")
with gr.Column():
gr.Label("Confusion Maxtrix with Historical Prediction Performance")
input_img = gr.Image("confusion_matrix.png", elem_id="confusion-matrix")
demo.launch()
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