initial commit

app.py

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+import os
+
+from laserembeddings import Laser
+from scipy import spatial
+from transformers import pipeline
+import gradio as gr
+
+model = pipeline('question-answering')
+os.system("python -m laserembeddings download-models")
+laser = Laser()
+
+def question_answer(draft, reference, question):
+    ref_answer = model({'question': question, 'context': reference})['answer']
+    draft_answer = model({'question': question, 'context': draft})['answer']
+    embs = laser.embed_sentences([ref_answer, draft_answer], lang='en')
+    sim = 1 - spatial.distance.cosine(embs[0], embs[1])
+    if sim >= 0.8:
+        out = "🎉 Great draft. No comprehensibility warning."
+    else:
+        out = "⚠️ Potential comprehensibility issue!"
+    return out
+
+iface = gr.Interface(fn=question_answer, inputs=["text", "text", "text"], outputs=["text"]).launch()
+

requirements.txt

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+torch
+transformers
+scipy
+numpy
+laserembeddings
+gradio
+