Update app.py

app.py

@@ -1,46 +1,14 @@
 from transformers import TFAutoModelForCausalLM, AutoTokenizer
 import tensorflow as tf
 import gradio as gr
-from transformers import TFAutoModelForSequenceClassification
-from transformers import AutoTokenizer
-import plotly.express as px
-import plotly.io as pio
 
 # configuration params
-pio.templates.default = "plotly_dark"
-
-# setting up the text in the page
 TITLE = "<center><h1>Talk with an AI</h1></center>"
-DESCRIPTION = r"""<center>This application allows you to talk with a machine/robot with state-of-the-art technology!!<br>
-                 In the back-end is using the Elapt1c/ElapticAI-1a model. One of the best models in text generation and comprehension.<br>
-                 Language processing is done using RoBERTa for sentiment-analysis and spaCy for named-entity recognition and dependency plotting.<br>
-                 The AI thinks he is a human, so please treat him as such, else he migh get angry!<br>
-                 """
-EXAMPLES = [
-    ["What is your favorite videogame?"],
-    ["What gets you really sad?"],
-    ["How can I make you really angry? "],
-    ["What do you do for work?"],
-    ["What are your hobbies?"],
-    ["What is your favorite food?"],
-]
-ARTICLE = r"""<center>
-              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>
-              For info about the chat-bot model can also see the <a href="https://arxiv.org/abs/1911.00536">ArXiv paper</a><br>
-              </center>"""
 
 # Loading necessary NLP models
-# dialog
 checkpoint = "elapt1c/ElapticAI-1a"  # tf
 model_gtp2 = TFAutoModelForCausalLM.from_pretrained(checkpoint)
 tokenizer_gtp2 = AutoTokenizer.from_pretrained(checkpoint)
-# sentiment
-checkpoint = f"cardiffnlp/twitter-roberta-base-emotion"
-model_roberta = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)
-tokenizer_roberta = AutoTokenizer.from_pretrained(checkpoint)
-# NER & Dependency
-nlp = spacy.load("en_core_web_sm")
 
 # test-to-test : chatting function -- GPT2
 def chat_with_bot(user_input, chat_history_and_input=[]):
@@ -55,51 +23,27 @@ def chat_with_bot(user_input, chat_history_and_input=[]):
             [chat_history_and_input, emb_user_input], axis=-1
         )  # other iterations
     chat_history_and_input = model_gtp2.generate(
-        bot_input_ids, max_length=1000, pad_token_id=tokenizer_gtp2.eos_token_id
+        bot_input_ids, max_length=50, pad_token_id=tokenizer_gtp2.eos_token_id
     ).numpy()
-    # print
     bot_response = tokenizer_gtp2.decode(
         chat_history_and_input[:, bot_input_ids.shape[-1] :][0],
         skip_special_tokens=True,
     )
+    # Limit history to last 500 characters
+    chat_history_and_input = chat_history_and_input[:, -500:]
     return bot_response, chat_history_and_input
 
-
-# text-to-sentiment
-def text_to_sentiment(text_input):
-    """Sentiment analysis using RoBERTa"""
-    labels = ["anger", "joy", "optimism", "sadness"]
-    encoded_input = tokenizer_roberta(text_input, return_tensors="tf")
-    output = model_roberta(encoded_input)
-    scores = output[0][0].numpy()
-    scores = softmax(scores)
-    return px.histogram(x=labels, y=scores, height=200)
-
-
-# text_to_semantics
-def text_to_semantics(text_input):
-    """NER and Dependency plot using Spacy"""
-    processed_text = nlp(text_input)
-    # Dependency
-    html_dep = displacy.render(
-        processed_text,
-        style="dep",
-        options={"compact": True, "color": "white", "bg": "light-black"},
-        page=False,
-    )
-    html_dep = "" + html_dep + ""
-    # NER
-    pos_tokens = []
-    for token in processed_text:
-        pos_tokens.extend([(token.text, token.pos_), (" ", None)])
-    # html_ner = ("" + html_ner + "")s
-    return pos_tokens, html_dep
-
-
 # gradio interface
 blocks = gr.Blocks()
 with blocks:
-    # physical elements
     session_state = gr.State([])
     gr.Markdown(TITLE)
-    gr.Mark
+    user_input = gr.Textbox(label="User Input")
+    bot_response = gr.Textbox(label="Bot Response")
+    user_input.change(
+        chat_with_bot,
+        inputs=[user_input, session_state],
+        outputs=[bot_response, session_state],
+    )
+
+blocks.launch()