Create new file

app.py

@@ -0,0 +1,31 @@
+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)