Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+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("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()