app.py

import gradio as gr
import pandas as pd
import sklearn
import xgboost as xgb
import datasets


inputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(38,"fixed"), label="Input Data", interactive=1)]

outputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(1, "fixed"), label="Predictions", headers=["Failures"])]

model = xgb.XGBClassifier()
model.load_model("pdm_fail_20231206.json")

# we will give our dataframe as example
# df = datasets.load_dataset("merve/supersoaker-failures")
# df = df["train"].to_pandas()

def infer(input_dataframe):
  return pd.DataFrame(model.predict(input_dataframe))
# examples = [[df.head(2)]]
gr.Interface(fn = infer, inputs = inputs, outputs = outputs).launch()