working app

Pipfile

@@ -0,0 +1,14 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+gradio = "==3.10.1"
+tensorflow = "==2.5.0"
+transformers = ">=4.21.0"
+
+[dev-packages]
+
+[requires]
+python_version = "3.9"

app.py

@@ -3,28 +3,25 @@ import tensorflow as tf
 import gradio as gr
 
 TITLE = "DialoGPT -- Chatbot"
-DESCRIPTION = "<center>Have funny/existencial dialogs with non-human entities</center>"
+DESCRIPTION = """<center>This application allows you to talk with a machine.
+                 In the back-end is using the DialoGPT model from Microsoft.<br>
+                 This model extends GPT2 towards the conversational neural response generetion domain.<br>
+                 You can also see the <a href="https://arxiv.org/abs/1911.00536">ArXiv paper</a><br></center>"""
 EXAMPLES = [
-    ["How will the world end?"],
-    ["Does the universe have a purpose?"],
-    ["Is the universe infinite?"],
-    ["Was Einstein right about time being relative?"],
-    ["What is Pythagoras theorem?"],
-    ["What is the meaning of life?"],
+    ["What is your favorite videogame?"],
+    ["What do you do for work?"],
+    ["What are your hobbies?"],
+    ["What is your favorite food?"],
 ]
 ARTICLE = r"""<center>
-              This application allowsa you to talk with a machine.
-              In the back-end I'm using the DialoGPT model from microsoft.<br>
-              This model extends GPT2 towards the conversational neural response generetion domain.<br>
-              ArXiv paper: https://arxiv.org/abs/1911.00536<br>
-              Done by dr. Gabriel Lopez<br> 
+              Done by dr. Gabriel Lopez<br>
               For more please visit: <a href='https://sites.google.com/view/dr-gabriel-lopez/home'>My Page</a><br>
               </center>"""
 
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 model = TFAutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
-
+# interaction function
 def chat_with_bot(user_input, chat_history_and_input=[]):
     emb_user_input = tokenizer.encode(
         user_input + tokenizer.eos_token, return_tensors="tf"
@@ -45,9 +42,14 @@ def chat_with_bot(user_input, chat_history_and_input=[]):
     )
     return bot_response, chat_history_and_input
 
+
+# gradio interface
+in_text = gr.Textbox(value="How was the class?", label="Start chatting!")
+out_text = gr.Textbox(value="", label="Chatbot response:")
+
 gr.Interface(
-    inputs=["text", "state"],
-    outputs=["text", "state"],
+    inputs=[in_text, "state"],
+    outputs=[out_text, "state"],
     examples=EXAMPLES,
     title=TITLE,
     description=DESCRIPTION,

requirements.txt

@@ -1,3 +1,3 @@
-gradio==3.10.1
-tensorflow==2.4.1
 transformers==4.24.0
+gradio==3.10.1
+tensorflow==2.5.0