Multiple models

app.py

@@ -1,23 +1,25 @@
 import gradio as gr
 from qasrl_model_pipeline import QASRL_Pipeline
-model = "kleinay/qanom-seq2seq-model-baseline"
-pipeline = QASRL_Pipeline(model)
 
-description = f"""This is a demo of the '{model}' model, which fine-tuned a Seq2Seq pretrained model on the QANom task.""" 
+models = ["kleinay/qanom-seq2seq-model-baseline", 
+          "kleinay/qanom-seq2seq-model-joint"]
+pipelines = {model: QASRL_Pipeline(model) for model in models}
+
+
+description = f"""This is a demo of our QASRL\QANom models, which fine-tuned Seq2Seq pretrained models on the QASRL\QANom task.""" 
 title="QANom Parser Demo"
-examples = [["The doctor was interested in Luke 's <p> treatment .", True, "treat"],
-            ["The Veterinary student was interested in Luke 's <p> treatment of sea animals .", True, "treat"]]
-input_sent_box_label = "Insert sentence here. Mark the predicate by adding the token '<p>' before it."
-verb_form_inp_placeholder = "e.g. 'decide' for the nominalization 'decision', 'teach' for 'teacher', etc."
+examples = [["The doctor was interested in Luke 's <predicate> treatment .", True, "treat"],
+            ["The Veterinary student was interested in Luke 's <predicate> treatment of sea animals .", True, "treat"]]
+input_sent_box_label = "Insert sentence here. Mark the predicate by adding the token '<predicate>' before it."
 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://www.qasrl.org' target='_blank'>QASRL Website</a> |
+<a href='https://huggingface.co/spaces/kleinay/qanom-seq2seq-demo' target='_blank'>Model Repo at Huggingface Hub</a> | 
 </p>"""
-
-def call(sentence, is_nominal, verb_form):
-    predicate_marker="<p>"
+def call(model_name, sentence, is_nominal, verb_form):
+    predicate_marker="<predicate>"
     if predicate_marker not in sentence:
-        raise ValueError("You must highlight one word of the sentence as a predicate using preceding '<p>'.")
-        
+        print("You must highlight one word of the sentence as a '<predicate>'.")
+        return
     if not verb_form:
         if is_nominal:
             raise ValueError("You should provide the verbal form of the nominalization")
@@ -26,18 +28,18 @@ def call(sentence, is_nominal, verb_form):
         pred_idx = toks.index(predicate_marker)
         predicate = toks(pred_idx+1)
         verb_form=predicate
-    pipe_out = pipeline(sentence, 
+    pipeline = pipelines[model_name]
+    return pipeline(sentence, 
                     predicate_marker=predicate_marker, 
                     predicate_type="nominal" if is_nominal else "verbal",
                     verb_form=verb_form)
-    return pipe_out[0]["QAs"], pipe_out[0]["generated_text"]
 iface = gr.Interface(fn=call, 
-                     inputs=[gr.inputs.Textbox(placeholder=input_sent_box_label, label="Sentence", lines=4), 
+                     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.Checkbox(default=True, label="Is Nominalization?"),
-                             gr.inputs.Textbox(placeholder=verb_form_inp_placeholder, label="Verbal form (for nominalizations)", default='')], 
-                     outputs=[gr.outputs.JSON(label="Model Output - QASRL"), gr.outputs.Textbox(label="Raw output sequence")],
+                             gr.inputs.Textbox(label="verbal form of nominalization", default='')], 
+                     outputs=gr.outputs.JSON(label="Model Output - QASRL"),
                      title=title,
                      description=description,
                      article=links,
-                     examples=examples )
-iface.launch()
+                     examples=examples )