first commit

README.md

@@ -1,11 +1,9 @@
 ---
-title: Qanom End To End Demo
+title: Qanom End-To-End Pipeline Demo
 emoji: 🦀
 colorFrom: gray
 colorTo: indigo
 sdk: gradio
 app_file: app.py
-pinned: false
+pinned: true
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+from qanom.qanom_end_to_end_pipeline import QANomEndToEndPipeline
+
+
+models = ["kleinay/qanom-seq2seq-model-baseline", 
+          "kleinay/qanom-seq2seq-model-joint"]
+pipelines = {model: QANomEndToEndPipeline(model) for model in models}
+
+
+description = f"""This is a demo of the full QANom Pipeline - identifying deverbal nominalizations and parsing them with question-answer driven semantic role labeling (QASRL) """ 
+title="QANom End-to-End Pipeline Demo"
+examples = [[models[0], "The doctor was interested in Luke 's treatment .", 0.75],
+            [models[1], "The Veterinary student was interested in Luke 's treatment of sea animals .", 0.75],
+            [models[1], "Some reviewers agreed that the criticism raised by the AC is mostly justified .", 0.75]]
+
+input_sent_box_label = "Insert sentence here, or select from the examples below"
+links = """<p style='text-align: center'>
+<a href='https://www.qasrl.org' target='_blank'>QASRL Website</a>  |  <a href='https://huggingface.co/kleinay/qanom-seq2seq-model-baseline' target='_blank'>Model Repo at Huggingface Hub</a>
+</p>"""
+
+
+def call(model_name, sentence, detection_threshold):
+
+    pipeline = pipelines[model_name]
+    pipe_out_pred_infos = pipeline([sentence], detection_threshold=detection_threshold)[0]
+    def pretty_pred_output(pred_info) -> str:
+        return "\n".join([f"{qa['question']} --- {';'.join(qa['answers'])}"
+                          for qa in pred_info['QAs']])
+    pretty_output = "\n".join(pretty_pred_output(pred_info) for pred_info in pipe_out_pred_infos)
+    return pretty_output, pipe_out_pred_infos
+
+iface = gr.Interface(fn=call, 
+                     inputs=[gr.inputs.Radio(choices=models, default=models[0], label="Model"), 
+                             gr.inputs.Textbox(placeholder=input_sent_box_label, label="Sentence", lines=4), 
+                             gr.inputs.Slider(minimum=0., maximum=1., step=0.01, default=0.5, label="Nominalization Detection Threshold")], 
+                     outputs=[gr.outputs.Textbox(label="Model Output"), gr.outputs.JSON(label="Model Output - JSON")],
+                     title=title,
+                     description=description,
+                     article=links,
+                     examples=examples)
+iface.launch()

requirements.txt

@@ -0,0 +1 @@
+qanom