Add python file and requirements

app.py

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+from transformers import pipeline
+import numpy as np
+import gradio as gr
+
+HEXACO = [
+    "honesty-humility",
+    "emotionality",
+    "extraversion",
+    "agreeableness",
+    "conscientiousness",
+    "openness to experience"
+]
+
+def netScores(tagList: list, sequence_to_classify: str, modelName: str) -> dict:
+    classifier = pipeline("zero-shot-classification", model=modelName)
+    hypothesis_template_pos = "This example is {}"
+    hypothesis_template_neg = "This example is not {}"
+    output_pos = classifier(sequence_to_classify, tagList, hypothesis_template=hypothesis_template_pos, multi_label=True)
+    output_neg = classifier(sequence_to_classify, tagList, hypothesis_template=hypothesis_template_neg, multi_label=True)
+
+    positive_scores = {}
+    for x in range(len(tagList)):
+        positive_scores[output_pos["labels"][x]] = output_pos["scores"][x]
+
+    negative_scores = {}
+    for x in range(len(tagList)):
+        negative_scores[output_neg["labels"][x]] = output_neg["scores"][x]
+
+    pos_neg_scores = {}
+    for tag in tagList:
+        pos_neg_scores[tag] = [positive_scores[tag],negative_scores[tag]]
+    
+    net_scores = {}
+    for tag in tagList:
+        net_scores[tag] = positive_scores[tag]-negative_scores[tag]
+
+    net_scores = dict(sorted(net_scores.items(), key=lambda x:x[1], reverse=True))
+
+    return net_scores
+
+def scoresMatch(tagList: list, scoresA: dict, scoresB: dict):
+    maxDistance = 2*np.sqrt(len(tagList))
+    differenceSquares = []
+    for tag in tagList:
+        difference = (scoresA[tag] - scoresB[tag])
+        differenceSquare = difference*difference
+        differenceSquares.append(differenceSquare)
+    distance = np.sqrt(np.sum(differenceSquares))
+    percentDifference = distance/maxDistance
+    
+    return 1-percentDifference
+
+def compareDocuments (userText1, userText2):
+    scores1 = netScores (HEXACO, userText1, 'akhtet/mDeBERTa-v3-base-myXNLI')
+    scores2 = netScores (HEXACO, userText2, 'akhtet/mDeBERTa-v3-base-myXNLI')
+    
+    return scoresMatch(HEXACO, scores1, scores2)
+               
+               
+demo = gr.Interface(
+    fn=compareDocuments,
+    inputs=["text", "text"],
+    outputs=["text"],
+)
+demo.launch()

requirements.txt

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+transformers
+numpy