Update app.py

app.py

@@ -155,7 +155,7 @@ def inject_relation_data(html: str, sorted_entities, binary_relations) -> str:
     return str(soup)
 
 
-def predict(text, render_as, render_kwargs_json):
+def predict(text: str) -> Tuple[dict, str]:
     document = TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions(text=text)
 
     # add single partition from the whole text (the model only considers text in partitions)
@@ -164,6 +164,15 @@ def predict(text, render_as, render_kwargs_json):
     # execute prediction pipeline
     pipeline(document)
 
+    document_dict = document.asdict()
+    return document_dict, json.dumps(document_dict)
+
+
+def render(document_txt: str, render_as: str, render_kwargs_json: str) -> str:
+    document_dict = json.loads(document_txt)
+    document = TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions.fromdict(
+        document_dict
+    )
     render_kwargs = json.loads(render_kwargs_json)
     if render_as == "Pretty Table":
         html = render_pretty_table(document, **render_kwargs)
@@ -175,12 +184,22 @@ def predict(text, render_as, render_kwargs_json):
     return html
 
 
+def open_accordion():
+    return gr.Accordion(open=True)
+
+
+def close_accordion():
+    return gr.Accordion(open=False)
+
+
 if __name__ == "__main__":
 
     model_name_or_path = "ArneBinder/sam-pointer-bart-base-v0.3"
     # local path
     # model_name_or_path = "models/dataset-sciarg/task-ner_re/v0.3/2024-03-01_18-25-32"
 
+    example_text = "Scholarly Argumentation Mining (SAM) has recently gained attention due to its potential to help scholars with the rapid growth of published scientific literature. It comprises two subtasks: argumentative discourse unit recognition (ADUR) and argumentative relation extraction (ARE), both of which are challenging since they require e.g. the integration of domain knowledge, the detection of implicit statements, and the disambiguation of argument structure. While previous work focused on dataset construction and baseline methods for specific document sections, such as abstract or results, full-text scholarly argumentation mining has seen little progress. In this work, we introduce a sequential pipeline model combining ADUR and ARE for full-text SAM, and provide a first analysis of the performance of pretrained language models (PLMs) on both subtasks. We establish a new SotA for ADUR on the Sci-Arg corpus, outperforming the previous best reported result by a large margin (+7% F1). We also present the first results for ARE, and thus for the full AM pipeline, on this benchmark dataset. Our detailed error analysis reveals that non-contiguous ADUs as well as the interpretation of discourse connectors pose major challenges and that data annotation needs to be more consistent."
+
     pipeline = AutoPipeline.from_pretrained(model_name_or_path, device=-1, num_workers=0)
     re_pipeline = AutoPipeline.from_pretrained(
         model_name_or_path,
@@ -201,27 +220,48 @@ if __name__ == "__main__":
         },
     }
 
-    iface = gr.Interface(
-        fn=predict,
-        inputs=[
-            gr.Textbox(
-                lines=20,
-                value="Scholarly Argumentation Mining (SAM) has recently gained attention due to its potential to help scholars with the rapid growth of published scientific literature. It comprises two subtasks: argumentative discourse unit recognition (ADUR) and argumentative relation extraction (ARE), both of which are challenging since they require e.g. the integration of domain knowledge, the detection of implicit statements, and the disambiguation of argument structure. While previous work focused on dataset construction and baseline methods for specific document sections, such as abstract or results, full-text scholarly argumentation mining has seen little progress. In this work, we introduce a sequential pipeline model combining ADUR and ARE for full-text SAM, and provide a first analysis of the performance of pretrained language models (PLMs) on both subtasks. We establish a new SotA for ADUR on the Sci-Arg corpus, outperforming the previous best reported result by a large margin (+7% F1). We also present the first results for ARE, and thus for the full AM pipeline, on this benchmark dataset. Our detailed error analysis reveals that non-contiguous ADUs as well as the interpretation of discourse connectors pose major challenges and that data annotation needs to be more consistent.",
-            ),
-        ],
-        additional_inputs=[
-            gr.Dropdown(
-                label="Render as",
-                choices=["Pretty Table", "spaCy"],
-                value="spaCy",
-            ),
-            gr.Textbox(
-                label="Render Arguments",
-                lines=5,
-                value=json.dumps(default_render_kwargs, indent=2),
-            ),
-        ],
-        additional_inputs_accordion=gr.Accordion(label="Render Options", open=False),
-        outputs=["html"],
-    )
-    iface.launch()
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column(scale=1):
+                text = gr.Textbox(
+                    label="Input Text",
+                    lines=20,
+                    value=example_text,
+                )
+
+                predict_btn = gr.Button("Predict")
+
+                output_txt = gr.Textbox(visible=False)
+
+            with gr.Column(scale=1):
+
+                with gr.Accordion("See plain result ...", open=False) as output_accordion:
+                    output_json = gr.JSON(label="Model Output")
+
+                with gr.Accordion("Render Options", open=False):
+                    render_as = gr.Dropdown(
+                        label="Render as",
+                        choices=["Pretty Table", "spaCy"],
+                        value="spaCy",
+                    )
+                    render_kwargs = gr.Textbox(
+                        label="Render Arguments",
+                        lines=5,
+                        value=json.dumps(default_render_kwargs, indent=2),
+                    )
+                render_btn = gr.Button("Re-render")
+
+                rendered_output = gr.HTML(label="Rendered Output")
+
+        render_button_kwargs = dict(
+            fn=render, inputs=[output_txt, render_as, render_kwargs], outputs=rendered_output
+        )
+        predict_btn.click(open_accordion, inputs=[], outputs=[output_accordion]).then(
+            fn=predict, inputs=text, outputs=[output_json, output_txt], api_name="predict"
+        ).success(**render_button_kwargs).success(
+            close_accordion, inputs=[], outputs=[output_accordion]
+        )
+        render_btn.click(**render_button_kwargs, api_name="render")
+
+    demo.launch()
+