update app.py

app.py

@@ -3,22 +3,52 @@ from pyterrier_doc2query import Doc2Query
 
 doc2query = Doc2Query()
 
-def predict(input):
-  return doc2query(input)
+def df2code(df):
+  rows = []
+  for row in df.itertuples(index=False):
+    rows.append(f'  {dict(row._asdict())},')
+  rows = '\n'.join(rows)
+  return f'''pd.DataFrame([
+{rows}
+])'''
+
+def predict(input, append):
+  doc2query.append = append
+  code = f'''
+## Code:
+
+```python
+import pandas as pd
+from pyterrier_doc2query import Doc2Query
+doc2query = Doc2Query(append={append})
+doc2query({df2code(input)})
+```
+'''
+  return (doc2query(input), code)
 
 gr.Interface(
     predict,
-    inputs=gr.Dataframe(
+    inputs=[gr.Dataframe(
       headers=["docno", "text"],
       datatype=["str", "str"],
       col_count=(2, "fixed"),
+      row_count=1,
+      wrap=True,
+      label='Pipeline Input',
       value=[['0', 'The presence of communication amid scientific minds was equally important to the success of the Manhattan Project as scientific intellect was. The only cloud hanging over the impressive achievement of the atomic researchers and engineers is what their success truly meant; hundreds of thousands of innocent lives obliterated.']],
-    ),
-    outputs=gr.Dataframe(
+    ), gr.Checkbox(
+      value=False,
+      label="Append",
+    )],
+    outputs=[gr.Dataframe(
       headers=["docno", "text", "querygen"],
       datatype=["str", "str", "str"],
-      row_count=0,
-      col_count=3,
-    ),
-    title="pyterrier_doc2query",
+      col_count=(3, "fixed"),
+      row_count=1,
+      wrap=True,
+      label='Pipeline Output',
+      value=[["[docno]", "[text]", "[querygen]"]],
+    ), gr.Markdown()],
+    title="PyTerrier: Doc2Query",
+    allow_flagging=False,
 ).launch()