Add python file and requirements

app.py

@@ -0,0 +1,71 @@
+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 compareTextAndLabels (userText, userLabels):
+    userLabelsArray = userLabels.split(",")
+    labelsMatches = {}
+    
+    textScores = netScores (HEXACO, userText, 'akhtet/mDeBERTa-v3-base-myXNLI')
+    for label in userLabelsArray:
+        labelScores = netScores (HEXACO, label, 'akhtet/mDeBERTa-v3-base-myXNLI')
+        labelMatch = scoresMatch(HEXACO, textScores, labelScores)
+        labelsMatches[label] = str(np.round(labelMatch*100,2))+"%"
+    
+    return labelsMatches
+
+               
+demo = gr.Interface(
+    fn=compareTextAndLabels,
+    inputs=[gr.Textbox(label="Text"), gr.Textbox(label="Labels (separated by commas)")],
+    outputs=[gr.Textbox(label="Label Scores")],
+)
+demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+transformers
+numpy
+torch --index-url https://download.pytorch.org/whl/cpu
+torchvision --index-url https://download.pytorch.org/whl/cpu
+torchaudio --index-url https://download.pytorch.org/whl/cpu