Add python file and requirements

app.py

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+from transformers import pipeline
+import numpy as np
+import gradio as gr
+
+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 compareTextAndLabels (userText, userLabels):
+    userLabelsArray = userLabels.split(",")
+    
+    labelsScores = netScores (userLabelsArray, userText, 'akhtet/mDeBERTa-v3-base-myXNLI')
+    for label in labelsScores:
+        labelsScores[label] = str(np.round(labelsScores[label]*100,2))+"%"
+    
+    return labelsScores
+
+               
+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

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+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