Code gen

app.py

@@ -1,60 +1,17 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer, BlenderbotForConditionalGeneration
-import torch
-
-chat_tkn = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-mdl = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
-
-
-# chat_tkn = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
-# mdl = BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
-
-def converse(user_input, chat_history=[]):
-    user_input_ids = chat_tkn(user_input + chat_tkn.eos_token, return_tensors='pt').input_ids
-
-    # keep history in the tensor
-    bot_input_ids = torch.cat([torch.LongTensor(chat_history), user_input_ids], dim=-1)
-
-    # get response
-    chat_history = mdl.generate(bot_input_ids, max_length=1000, pad_token_id=chat_tkn.eos_token_id).tolist()
-    print(chat_history)
-
-    response = chat_tkn.decode(chat_history[0]).split("<|endoftext|>")
-
-    print("starting to print response")
-    print(response)
-
-    # html for display
-    html = "<div class='mybot'>"
-    for x, mesg in enumerate(response):
-        if x % 2 != 0:
-            mesg = "Alicia:" + mesg
-            clazz = "alicia"
-        else:
-            clazz = "user"
-
-        print("value of x")
-        print(x)
-        print("message")
-        print(mesg)
-
-        html += "<div class='mesg {}'> {}</div>".format(clazz, mesg)
-    html += "</div>"
-    print(html)
-    return html, chat_history
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as grad
+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
 
-import gradio as grad
+     gen_ids = mdl.generate(input_ids, max_length=128)
+     response = codegen_tkn.decode(gen_ids[0], skip_special_tokens=True)
+     return response
 
-css = """
-.mychat {display:flex;flex-direction:column}
-.mesg {padding:5px;margin-bottom:5px;border-radius:5px;width:75%}
-.mesg.user {background-color:lightblue;color:white}
-.mesg.alicia {background-color:orange;color:white,align-self:self-end}
-.footer {display:none !important}
-"""
-text = grad.inputs.Textbox(placeholder="Lets chat")
-grad.Interface(fn=converse,
-               theme="default",
-               inputs=[text, "state"],
-               outputs=["html", "state"],
-               css=css).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()