updated architecture

zeroshot_clf.py

@@ -0,0 +1,44 @@
+import pandas as pd
+import streamlit
+import torch
+from transformers import AutoModelForSequenceClassification,AutoTokenizer
+import numpy as np
+import plotly.express as px
+
+model=AutoModelForSequenceClassification.from_pretrained('zero_shot_clf/')
+tokenizer=AutoTokenizer.from_pretrained('zero_shot_clf/')
+
+def zero_shot_classification(premise:str,labels:str,model=model,tokenizer=tokenizer):
+    try:
+        labels=labels.split(',')
+        labels=[l.lower() for l in labels]
+    except:
+        raise Exception("please pass atleast 2 labels to classify")
+
+    premise=premise.lower()
+
+    labels_prob=[]
+
+    for l in labels:
+
+        hypothesis= f'this is an example of {l}'
+
+        input = tokenizer.encode(premise,hypothesis,
+                             return_tensors='pt',
+                                 truncation_strategy='only_first')
+        output = model(input)
+        entail_contra_prob = output['logits'][:,[0,2]].softmax(dim=1)[:,1].item()
+        labels_prob.append(entail_contra_prob)
+
+    labels_prob_norm=[np.round(100*c/np.sum(labels_prob),1) for c in labels_prob]
+
+    df=pd.DataFrame({'labels':labels,
+                     'Probability':labels_prob_norm})
+    fig=px.bar(x=df['Probability'],
+               y=df['labels'])
+    return streamlit.plotly_chart(fig)
+
+# zero_shot_classification(premise='Tiny worms and breath analyzers could screen for disease while it’s early and treatable',
+#                          labels='science, sports, museum')
+
+