Update app.py

app.py

@@ -91,18 +91,38 @@
 # out=grad.Textbox(lines=10, label="Summary")
 # grad.Interface(summarize, inputs=txt, outputs=out).launch()
 
-from transformers import pipeline
-import gradio as grad
-zero_shot_classifier = pipeline("zero-shot-classification")
+# from transformers import pipeline
+# import gradio as grad
+# zero_shot_classifier = pipeline("zero-shot-classification")
 
 
-def classify(text,labels):
-    classifer_labels = labels.split(",")
-    #["software", "politics", "love", "movies", "emergency", "advertisment","sports"]
-    response = zero_shot_classifier(text,classifer_labels)
+# def classify(text,labels):
+#     classifer_labels = labels.split(",")
+#     #["software", "politics", "love", "movies", "emergency", "advertisment","sports"]
+#     response = zero_shot_classifier(text,classifer_labels)
+#     return response
+# txt=grad.Textbox(lines=1, label="English", placeholder="text to be classified")
+# labels=grad.Textbox(lines=1, label="Labels", placeholder="comma separated labels")
+# out=grad.Textbox(lines=1, label="Classification")
+# grad.Interface(classify, inputs=[txt,labels], outputs=out).launch()
+
+from transformers import BartForSequenceClassification, BartTokenizer
+import gradio as grad
+bart_tkn = BartTokenizer.from_pretrained('facebook/bart-large-mnli')
+mdl = BartForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
+
+def classify(text,label): 
+    tkn_ids = bart_tkn.encode(text, label, return_tensors='pt')
+    tkn_lgts = mdl(tkn_ids)[0]
+    entail_contra_tkn_lgts = tkn_lgts[:,[0,2]]
+    probab = entail_contra_tkn_lgts.softmax(dim=1)
+    response =  probab[:,1].item() * 100 
     return response
 txt=grad.Textbox(lines=1, label="English", placeholder="text to be classified")
-labels=grad.Textbox(lines=1, label="Labels", placeholder="comma separated labels")
-out=grad.Textbox(lines=1, label="Classification")
+labels=grad.Textbox(lines=1, label="Label", placeholder="Input a Label")
+out=grad.Textbox(lines=1, label="Probablity of label being true is")
 grad.Interface(classify, inputs=[txt,labels], outputs=out).launch()
 
+
+
+