Upload Gradio app.py

Gradio app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Text2TextGenerationPipeline
+
+pipe = Text2TextGenerationPipeline(model = AutoModelForSeq2SeqLM.from_pretrained("jpwahle/t5-word-sense-disambiguation"),
+tokenizer = AutoTokenizer.from_pretrained("jpwahle/t5-word-sense-disambiguation"))
+
+def wsd_gen(word, context, d1, d2, d3):
+            question = 'question: question: which description describes the word' + ' " ' + word + ' " '
+            descriptions_context = 'best in the following context? \descriptions:[  " ' + d1 + '" , " ' + d2 + ' " , or " '+ d3 + ' " ] context: ' + context + "'"
+            raw_input = question + descriptions_context
+            output = pipe(raw_input)[0]['generated_text']
+            return output
+word_mask = gr.inputs.Textbox(lines=1, placeholder= "Enter word to disambiguate", default="", label = "Based on the context, which description best matches this word: ")
+input_context = gr.inputs.Textbox(lines=1, placeholder="Enter context", default="", label = "context: ")
+input_desc1 = gr.inputs.Textbox(lines=1, placeholder="Enter description", default="", label = "description 1: ")
+input_desc2 = gr.inputs.Textbox(lines=1, placeholder="Enter description", default="", label = "description 2: ")
+input_desc3 = gr.inputs.Textbox(lines=1, placeholder="Enter description", default="", label = "description 3: ")
+input_desc4 = gr.inputs.Textbox(lines=1, placeholder="Enter description", default="", label = "description 4: ")
+gr.Interface(wsd_gen,
+            inputs = [word_mask , input_context, input_desc1, input_desc2, input_desc3],
+            outputs= "textbox",
+            title = "T5-Word Sense Disambiguation",
+            theme = "seafoam",
+            allow_flagging="never").launch(inbrowser=True,share=True)