Update app.py

app.py

@@ -0,0 +1,105 @@
+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=[]):
+    """Text generation using GPT2"""
+    emb_user_input = tokenizer_gtp2.encode(
+        user_input + tokenizer_gtp2.eos_token, return_tensors="tf"
+    )
+    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_gtp2.generate(
+        bot_input_ids, max_length=1000, 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,
+    )
+    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