Update app.py

app.py

@@ -57,10 +57,10 @@ def classify(text,labels):
 # grad.Interface(classify, inputs=[txt,labels], outputs=out).launch()
 
 # Text classification using BartForSequenceClassification
-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')
+# 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]
@@ -68,8 +68,21 @@ def classify(text,label):
     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="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()
+# txt=grad.Textbox(lines=1, label="English", placeholder="text to be classified")
+# 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()
 
+# GPT2
+from transformers import GPT2LMHeadModel,GPT2Tokenizer
+import gradio as grad
+mdl = GPT2LMHeadModel.from_pretrained('gpt2')
+gpt2_tkn=GPT2Tokenizer.from_pretrained('gpt2')
+def generate(starting_text):
+    tkn_ids = gpt2_tkn.encode(starting_text, return_tensors = 'pt')
+    gpt2_tensors = mdl.generate(tkn_ids)
+    response = gpt2_tensors
+    return response
+txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
+out=grad.Textbox(lines=1, label="Generated Tensors")
+grad.Interface(generate, inputs=txt, outputs=out).launch()