app.py

import sklearn
import gradio as gr
import joblib
import pandas as pd

pipe = joblib.load("./model.pkl")

title = "Supersoaker Defective Product Prediction"
description = "This model predicts Supersoaker production line failures. Drag and drop any slice from dataset or edit values as you wish in below dataframe component."


with open("./config.json") as f:
    config_dict = eval(f.read())
headers = config_dict["sklearn"]["columns"]

example_dict = config_dict["sklearn"]["example_input"]
df = pd.DataFrame.from_dict(example_dict,orient='index').transpose()



inputs = [gr.Dataframe(headers = [item for item in example_dict], row_count = (2, "dynamic"), col_count=(24,"dynamic"), label="Input Data", interactive=1)]
outputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(1, "fixed"), label="Predictions", headers=["Failures"])]


def infer(inputs):
    data = pd.DataFrame(inputs, columns=[item for item in example_dict])
    predictions = pipe.predict(inputs)
    return pd.DataFrame(predictions, columns=["results"])

gr.Interface(infer, inputs = inputs, outputs = outputs, title = title,
            description = description, examples=df.tail(3), cache_examples=False).launch(debug=True)