Update app.py

app.py

@@ -76,19 +76,33 @@
 # out=grad.Textbox(lines=10, label="Summary")
 # grad.Interface(summarize, inputs=txt, outputs=out).launch()
 
-from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+# from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+# import gradio as grad
+# mdl_name = "google/pegasus-xsum"
+# pegasus_tkn = PegasusTokenizer.from_pretrained(mdl_name)
+# mdl = PegasusForConditionalGeneration.from_pretrained(mdl_name)
+
+# def summarize(text):
+#     tokens = pegasus_tkn(text, truncation=True, padding="longest", return_tensors="pt")
+#     translated_txt = mdl.generate(**tokens,num_return_sequences=5,max_length=200,temperature=1.5,num_beams=10)
+#     response = pegasus_tkn.batch_decode(translated_txt, skip_special_tokens=True)
+#     return response
+# txt=grad.Textbox(lines=10, label="English", placeholder="English Text here")
+# out=grad.Textbox(lines=10, label="Summary")
+# grad.Interface(summarize, inputs=txt, outputs=out).launch()
+
+from transformers import pipeline
 import gradio as grad
-mdl_name = "google/pegasus-xsum"
-pegasus_tkn = PegasusTokenizer.from_pretrained(mdl_name)
-mdl = PegasusForConditionalGeneration.from_pretrained(mdl_name)
-
-def summarize(text):
-    tokens = pegasus_tkn(text, truncation=True, padding="longest", return_tensors="pt")
-    translated_txt = mdl.generate(**tokens,num_return_sequences=5,max_length=200,temperature=1.5,num_beams=10)
-    response = pegasus_tkn.batch_decode(translated_txt, skip_special_tokens=True)
-    return response
-txt=grad.Textbox(lines=10, label="English", placeholder="English Text here")
-out=grad.Textbox(lines=10, label="Summary")
-grad.Interface(summarize, inputs=txt, outputs=out).launch()
+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)
+    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()
+