modify to qasem pipeline

app.py

@@ -1,61 +1,61 @@
 import gradio as gr
-import nltk
-from qanom.qanom_end_to_end_pipeline import QANomEndToEndPipeline
-from typing import List
 
-models = ["kleinay/qanom-seq2seq-model-baseline", 
-          "kleinay/qanom-seq2seq-model-joint"]
-pipelines = {model: QANomEndToEndPipeline(model) for model in models}
+pipeline = QASemEndToEndPipeline()
 
 
-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[1], "the construction of the officer 's building finished right after the beginning of the destruction of the previous construction .", 0.7],
-            [models[1], "The doctor asked about the progress in Luke 's treatment .", 0.75],
-            [models[0], "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 .", 0.5]]
+description = f"""This is a demo of the QASem Parsing pipeline. It wraps models of three QA-based semantic tasks, composing a comprehensive semi-structured representation of sentence meaning - covering verbal and nominal semantic role labeling together with discourse relations.""" 
+title="QASem Parsing Demo"
+examples = [["the construction of the officer 's building finished right after the beginning of the destruction of the previous construction .", 0.7],
+            ["The doctor asked about the progress in Luke 's treatment .", 0.7],
+            ["The Veterinary student was interested in Luke 's treatment of sea animals .", 0.7],
+            ["Both were shot in the confrontation with police and have been recovering in hospital since the attack .", 0.7],
+            ["Some reviewers agreed that the criticism raised by the AC is mostly justified .", 0.5]]
             
 
 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>
+<a href='https://github.com/kleinay/QASem' target='_blank'>Github Repo</a> | <a href='https://arxiv.org/abs/2205.11413' target='_blank'>Paper</a>
 </p>"""
 
 
-def call(model_name, sentence, detection_threshold):
+def call(sentence, detection_threshold):
 
-    pipeline = pipelines[model_name]
-    pred_infos = pipeline([sentence], detection_threshold=detection_threshold)[0]
-    def pretty_qas(pred_info) -> List[str]:
+    outputs = pipeline([sentence], nominalization_detection_threshold=detection_threshold)[0]
+    def pretty_qadisc_qas(pred_info) -> List[str]:
+        if not pred_info: return []
+        return [f"{qa['question']} --- {qa['answer']}"
+                for qa in pred_info if qa is not None]
+    def pretty_qasrl_qas(pred_info) -> List[str]:
         if not pred_info or not pred_info['QAs']: return []
         return [f"{qa['question']} --- {';'.join(qa['answers'])}"
                 for qa in pred_info['QAs'] if qa is not None]
-    all_qas = [qa for pred_info in pred_infos for qa in pretty_qas(pred_info)]                      
-    if not pred_infos:
-        pretty_qa_output = "NO NOMINALIZATION FOUND"
-    elif not all_qas:
+    
+    qasrl_qas = pretty_qasrl_qas(outputs['qasrl'])
+    qanom_qas = pretty_qasrl_qas(outputs['qanom'])
+    qadisc_qas= pretty_qadisc_qas(outputs['qadiscourse'])
+    all_qas = ['QASRL:'] + qasrl_qas + ['\nQANom:'] + qanom_qas + ['\nQADiscourse:'] + qadisc_qas                      
+    if not qasrl_qas + qanom_qas + qadisc_qas:
         pretty_qa_output = "NO QA GENERATED"
     else:
         pretty_qa_output = "\n".join(all_qas) 
+    
     # also present highlighted predicates
-    positives = [pred_info['predicate_idx'] for pred_info in pred_infos] 
+    qasrl_predicates = [pred_info['predicate_idx'] for pred_info in outputs['qasrl']] 
+    qanom_predicates = [pred_info['predicate_idx'] for pred_info in outputs['qanom']] 
     def color(idx):
-        if idx in positives: return "lightgreen"
-    idx2verb = {d["predicate_idx"] : d["verb_form"] for d in pred_infos}
-    idx2prob = {d["predicate_idx"] : d["predicate_detector_probability"] for d in pred_infos}
+        if idx in qasrl_predicates : return "purple"
+        if idx in qanom_predicates : return "blue"
     def word_span(word, idx):
-        tooltip = f'title=" probability={idx2prob[idx]:.2}&#010;verb={idx2verb[idx]}"' if idx in idx2verb else ''
-        return f'<span {tooltip} style="background-color: {color(idx)}">{word}</span>'
+        return f'<span style="background-color: {color(idx)}">{word}</span>'
     html = '<span>' + ' '.join(word_span(word, idx) for idx, word in enumerate(sentence.split(" "))) + '</span>'
-    return html, pretty_qa_output , pred_infos
+    return html, pretty_qa_output , outputs
 
 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), 
+                     inputs=[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.HTML(label="Detected Nominalizations"),
+                     outputs=[gr.outputs.HTML(label="Detected Predicates"),
                               gr.outputs.Textbox(label="Generated QAs"), 
-                              gr.outputs.JSON(label="Raw Model Output")],
+                              gr.outputs.JSON(label="Raw QASemEndToEndPipeline Output")],
                      title=title,
                      description=description,
                      article=links,