added model and interface

app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+
+model = joblib.load('decision_tree.pkl')
+
+def onehot(df, column):
+    df = df.copy()
+    dummies = pd.get_dummies(df[column])
+    df = pd.concat([df,dummies], axis=1)
+    df = df.drop(column, axis=1)
+    return df
+
+
+def dataframe(file_obj):
+    df = pd.read_csv(file_obj.name)
+    df = onehot(df, column='type')
+    df = df.drop(['nameOrig','nameDest','isFlaggedFraud'], axis=1)
+    print(df.shape)
+    y_pred = model.predict(df)
+    
+    pred_df = pd.DataFrame(y_pred, columns = ['isFraud'])
+    print(type(pred_df))
+    print(pred_df.shape)
+#     clr = classification_report(y_test, y_pred, target_names=['Not Fraud','Fraud'])
+#     return 'Classification Report:\n'+ clr
+    return pred_df
+
+file = gr.inputs.File(file_count="single", type="file", label="CSV File for Predictions", optional=False)
+y_pred_df = gr.outputs.Dataframe(max_rows=20, max_cols=None, overflow_row_behaviour="paginate", type="auto", label="Predictions of records in the file")
+interface_csv = gr.Interface(
+                            fn=dataframe, 
+                            inputs=file, 
+                            outputs=y_pred_df,
+                            title="Fraud Detection - EXPERT SYSTEM",
+                            theme="dark-peach"
+                            )
+
+interface_csv.launch(share=True, inline=False)

decision_tree.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2aa27c6228c73b27dccb1837f7fb2ae30b92067c622ed936de6df219702e92f3
+size 131544