Update app.py

app.py

@@ -91,13 +91,31 @@ def generate(starting_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)
+# 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(generateDistlGPT2, inputs=txt, outputs=out).launch()
+# txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
+# out=grad.Textbox(lines=1, label="Generated Text")
+# grad.Interface(generateDistlGPT2, inputs=txt, outputs=out).launch()
+
+#Text Generation
+from transformers import AutoModelWithLMHead, AutoTokenizer
+import gradio as grad
+text2text_tkn = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
+mdl = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
+def text2text(context,answer):
+    input_text = "answer: %s  context: %s </s>" % (answer, context)
+    features = text2text_tkn ([input_text], return_tensors='pt')
+    output = mdl.generate(input_ids=features['input_ids'],
+               attention_mask=features['attention_mask'],
+               max_length=64)
+    response=text2text_tkn.decode(output[0])
+    return response
+context=grad.Textbox(lines=10, label="English", placeholder="Context")
+ans=grad.Textbox(lines=1, label="Answer")
+out=grad.Textbox(lines=1, label="Genereated Question")
+grad.Interface(text2text, inputs=[context,ans], outputs=out).launch()