Update app.py

app.py

@@ -395,57 +395,80 @@
 #-----------------------------------------------------------------------------------
 # 17. Chatbot/Dialog Bot: a simple bot named Alicia that is based on the Microsoft DialoGPT model .
 
-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)
+# 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
+#     return html, chat_history
 
+# import gradio as grad
+
+# 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.Textbox(placeholder="Lets chat")
+
+# grad.Interface(fn=converse,
+#                theme="default",
+#                inputs=[text, "state"],
+#                outputs=["html", "state"],
+#                css=css).launch()
+
+#-----------------------------------------------------------------------------------
+# 18. Code and Code Comment Generation
+
+# CodeGen is a language model that converts basic English prompts into code that can be executed. 
+# Instead of writing code yourself, you describe what the code should do using natural language, and 
+# the machine writes the code for you based on what you’ve described.
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
 import gradio as grad
 
-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.Textbox(placeholder="Lets chat")
-
-grad.Interface(fn=converse,
-               theme="default",
-               inputs=[text, "state"],
-               outputs=["html", "state"],
-               css=css).launch()
+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
+    gen_ids = mdl.generate(input_ids, max_length=128)
+    response = codegen_tkn.decode(gen_ids[0], skip_special_tokens=True)
+    return response
+
+output=grad.Textbox(lines=1, label="Generated Python Code", placeholder="")