Update app.py

app.py

@@ -1,25 +1,17 @@
-from transformers import AutoTokenizer, AutoModelWithLMHead
+from transformers import AutoTokenizer, AutoModelForCausalLM
 import gradio as grad
-text2text_tkn = AutoTokenizer.from_pretrained("deep-learning-analytics/wikihow-t5-small")
-mdl = AutoModelWithLMHead.from_pretrained("deep-learning-analytics/wikihow-t5-small")
+codegen_tkn = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
+mdl = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-mono")
 
+def codegen(intent):
+# give input as text which reflects intent of the program.
+     #text = " write a function which takes 2 numbers as input and returns the larger of the two"
+     input_ids = codegen_tkn(intent, return_tensors="pt").input_ids
 
-def text2text_summary(para):
-    initial_txt = para.strip().replace("\n","")
-    tkn_text = text2text_tkn.encode(initial_txt, return_tensors="pt")
+     gen_ids = mdl.generate(input_ids, max_length=128)
+     response = codegen_tkn.decode(gen_ids[0], skip_special_tokens=True)
+     return response
 
-    tkn_ids = mdl.generate(
-            tkn_text,
-            max_length=250, 
-            num_beams=5,
-            repetition_penalty=2.5, 
-           
-            early_stopping=True
-        )
-
-    response = text2text_tkn.decode(tkn_ids[0], skip_special_tokens=True)
-    return response
-
-para=grad.Textbox(lines=10, label="Paragraph", placeholder="Copy paragraph")
-out=grad.Textbox(lines=1, label="Summary")
-grad.Interface(text2text_summary, inputs=para, outputs=out).launch()
+output=grad.Textbox(lines=1, label="Generated Python Code", placeholder="")
+inp=grad.Textbox(lines=1, label="Place your intent here")
+grad.Interface(codegen, inputs=inp, outputs=output).launch()