Added app files

app.py

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+from typing import Sequence
+import streamlit as st
+from hf_model import classifier_zero,load_model
+from utils import plot_result
+classifier=load_model()
+if __name__ == '__main__':
+    st.header("Zero Shot Classification")
+    
+
+  
+    sequence = st.text_area(label="Input Sequence")
+    labels = st.text_input('Possible topics (separated by `,`)', max_chars=1000)
+    labels = list(set([x.strip() for x in labels.strip().split(',') if len(x.strip()) > 0]))
+    if len(labels) == 0 or len(sequence) == 0:
+        st.write('Enter some text and at least one possible topic to see predictions.')
+
+    multi_class = st.checkbox('Allow multiple correct topics', value=True)
+    
+    with st.spinner('Classifying...'):
+      top_topics, scores = classifier_zero(classifier,sequence=sequence,labels=labels,multi_class=multi_class)
+      plot_result(top_topics[::-1][-10:], scores[::-1][-10:])
+       

hf_model.py

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+from transformers import AutoTokenizer, AutoModelForSequenceClassification,pipeline
+import torch
+
+def load_model():
+
+    model_name = "MoritzLaurer/DeBERTa-v3-base-mnli"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    classifier = pipeline(task='zero-shot-classification', model=model, tokenizer=tokenizer, framework='pt')
+    return classifier
+
+
+def classifier_zero(classifier,sequence:str,labels:list,multi_class:bool):
+    outputs=classifier(sequence, labels,multi_label=multi_class)
+    return outputs['labels'], outputs['scores']
+

requirements.txt

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+transformers[sentencepiece]==4.11.0
+streamlit
+plotly
+torch

utils.py

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+import streamlit as st
+import numpy as np
+import plotly.express as px
+def plot_result(top_topics, scores):
+    top_topics = np.array(top_topics)
+    scores = np.array(scores)
+    scores *= 100
+    fig = px.bar(x=scores, y=top_topics, orientation='h', 
+                 labels={'x': 'Confidence', 'y': 'Label'},
+                 text=scores,
+                 range_x=(0,115),
+                 title='Top Predictions',
+                 color=np.linspace(0,1,len(scores)),
+                 color_continuous_scale='GnBu')
+    fig.update(layout_coloraxis_showscale=False)
+    fig.update_traces(texttemplate='%{text:0.1f}%', textposition='outside')
+    st.plotly_chart(fig)