Update app.py

app.py

@@ -74,10 +74,10 @@ def classify(text,label):
 # 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')
+# 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,max_length=100,no_repeat_ngram_size=True,num_beams=3,do_sample=True)
@@ -86,6 +86,18 @@ def generate(starting_text):
     for i, x in enumerate(gpt2_tensors):
        response=response+f"{i}: {gpt2_tkn.decode(x, skip_special_tokens=True)}"
     return response
+# txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
+# out=grad.Textbox(lines=1, label="Generated Text")
+# grad.Interface(generate, inputs=txt, outputs=out).launch()
+
+#DistlGPT2
+from transformers import pipeline, set_seed
+import gradio as grad
+gpt2_pipe = pipeline('text-generation', model='distilgpt2')
+set_seed(42)
+def generateDistlGPT2(starting_text):
+    response= gpt2_pipe(starting_text, max_length=20, num_return_sequences=5)
+    return response
 txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
 out=grad.Textbox(lines=1, label="Generated Text")
-grad.Interface(generate, inputs=txt, outputs=out).launch()
+grad.Interface(generateDistlGPT2, inputs=txt, outputs=out).launch()