working prototype

.gitignore

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+.env
+.git*

app.py

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+from transformers import TFAutoModelForCausalLM, AutoTokenizer
+import tensorflow as tf
+import gradio as gr
+
+TITLE = "DialoGPT -- Chatbot"
+DESCRIPTION = "<center>Have funny/existencial dialogs with non-human entities</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?"],
+]
+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> 
+              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")
+
+
+def chat_with_bot(user_input, chat_history_and_input=[]):
+    emb_user_input = tokenizer.encode(
+        user_input + tokenizer.eos_token, return_tensors="tf"
+    )
+    print("chat_history:", chat_history_and_input)
+    print("emb_user_input:", emb_user_input)
+    if chat_history_and_input == []:
+        bot_input_ids = emb_user_input  # first iteration
+    else:
+        bot_input_ids = tf.concat(
+            [chat_history_and_input, emb_user_input], axis=-1
+        )  # other iterations
+    chat_history_and_input = model.generate(
+        bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id
+    ).numpy()
+    # print
+    bot_response = tokenizer.decode(
+        chat_history_and_input[:, bot_input_ids.shape[-1] :][0],
+        skip_special_tokens=True,
+    )
+    print(f"{bot_response=}")
+    print(f"{chat_history_and_input=}")
+    return bot_response, chat_history_and_input
+
+gr.Interface(
+    inputs=["text", "state"],
+    outputs=["text", "state"],
+    examples=EXAMPLES,
+    title=TITLE,
+    description=DESCRIPTION,
+    article=ARTICLE,
+    fn=chat_with_bot,
+    allow_flagging=False,
+).launch()