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import gradio as gr
from app.utils import (
create_input_instruction,
format_prediction_ouptut,
display_sentiment_score_table,
sentiment_flow_plot,
sentiment_intensity_analysis,
EXAMPLE_CONVERSATIONS,
label_analysis,
)
import sys
sys.path.insert(0, "../") # neccesary to load modules outside of app
from app import deberta_model, tokenizer
from preprocessing import preprocess
from Model.DeBERTa.deberta import predict, decode_deberta_label
def deberta_preprocess(input):
result = preprocess.process_user_input(input)
if not result["success"]:
raise gr.Error(result["message"])
data = result["data"]
speakers = [item[1] for item in data]
messages = [item[2] for item in data]
return speakers, messages
def deberta_classifier(input):
speakers, messages = deberta_preprocess(input)
predictions = predict(deberta_model, tokenizer, messages)
# Assuming that there's only one conversation
labels = [decode_deberta_label(pred) for pred in predictions]
output = format_prediction_ouptut(speakers, messages, labels)
return output
def deberta_ui():
with gr.Blocks() as deberta_model:
gr.Markdown(
"""
# DeBERTa
Building upon the DeBERTa architecture, the model was customized and
retrained on Epik data to classify messages between Visitors and Agents into
corresponding sentiment labels. At the time of training by the team prior to
the Fall 2023 semester, the model was trained on 15 labels, including
Openness, Anxiety, Confusion, Disapproval, Remorse, Accusation, Denial,
Obscenity, Disinterest, Annoyance, Information, Greeting, Interest,
Curiosity, or Acceptance.
The primary difference between DeBERTa and COSMIC is that while DeBERTa's
prediction is solely based on its own context, COSMIC uses the context of
the entire conversation (i.e., all messages from the chat history of the
conversation).
"""
)
create_input_instruction()
with gr.Row():
with gr.Column():
example_dropdown = gr.Dropdown(
choices=["-- Not Selected --"] + list(EXAMPLE_CONVERSATIONS.keys()),
value="-- Not Selected --",
label="Select an example",
)
gr.Markdown('<p style="text-align: center;color: gray;">--- OR ---</p>')
conversation_input = gr.TextArea(
value="",
label="Input you conversation",
placeholder="Plese input your conversation here",
lines=15,
max_lines=15,
)
def on_example_change(input):
if input in EXAMPLE_CONVERSATIONS:
return EXAMPLE_CONVERSATIONS[input]
return ""
example_dropdown.input(
on_example_change,
inputs=example_dropdown,
outputs=conversation_input,
)
with gr.Column():
output = gr.Textbox(
value="",
label="Predicted Sentiment Labels",
lines=22,
max_lines=22,
interactive=False,
)
submit_btn = gr.Button(value="Submit")
submit_btn.click(deberta_classifier, conversation_input, output)
# reset the output whenever a change in the input is detected
conversation_input.change(lambda x: "", conversation_input, output)
gr.Markdown("# Analysis of Labels")
with gr.Row():
with gr.Column(scale=1):
gr.Markdown(
"""
<b>Frequency Analysis of Labels</b>
One key aspect of our analysis involves examining the
frequency distribution of labels assigned to different
parts of the conversation. This includes tracking the
occurrence of labels such as "Interest," "Curiosity,"
"Confused," "Openness," and "Acceptance." The resulting
distribution provides insights into the prevalence of
various sentiments during the interaction.
<b>Word Cloud Visualization</b>
In addition to label frequency, we employ word cloud
visualization to depict the prominent terms in the input
conversations. This visual representation highlights the
most frequently used words, shedding light on the key
themes and topics discussed.
"""
)
with gr.Column(scale=3):
labels_plot = gr.Plot(label="Analysis of Labels Plot")
with gr.Column(scale=3):
wordcloud_plot = gr.Plot(label="Analysis of Labels Plot")
labels_btn = gr.Button(value="Plot Label Analysis")
labels_btn.click(label_analysis, inputs=[output], outputs=[labels_plot,wordcloud_plot])
gr.Markdown("# Sentiment Flow Plot")
with gr.Row():
with gr.Column(scale=1):
display_sentiment_score_table()
with gr.Column(scale=2):
plot_box = gr.Plot(label="Analysis Plot")
plot_btn = gr.Button(value="Plot Sentiment Flow")
plot_btn.click(sentiment_flow_plot, inputs=[output], outputs=[plot_box])
gr.Markdown("# Sentiment Intensity Analysis")
with gr.Row():
with gr.Column(scale=1):
gr.Markdown(
"""
How accurate is the model? How good are the labels? These are
some questions that we may have at this point, and we need to
look at different metrics to assess the performance of our
models. One of them is sentiment intensity which measures how
strong a sentiment is expressed in the text.
This can be done by using NLTK's `SentimentIntensityAnalyzer`
which analyzes the connotation of the words in the text and
suggests whether a text is positive (with score > 0) or negative
(score < 0) and at what degree the text is positive or negative.
The graph to the right illustrates the change in sentiment
intensity of the agent and visitor across the course of the
conversation.
<b><u>Note:</u></b> While NLTK's SentimentIntensityAnalyzer
offers valuable insights, it is primarily trained on social media
data like Twitter. Its performance might falter for lengthy or
intricate messages. However, it remains a useful tool for
gaining perspective on sentiment in conversations.
"""
)
with gr.Column(scale=2):
intensity_plot = gr.LinePlot()
intensity_plot_btn = gr.Button(value="Plot Sentiment Intensity")
intensity_plot_btn.click(
sentiment_intensity_analysis,
inputs=[conversation_input],
outputs=[intensity_plot],
)
# reset all outputs whenever a change in the input is detected
conversation_input.change(
lambda x: ("", None, None, None, None),
conversation_input,
outputs=[output, labels_plot, wordcloud_plot, plot_box, intensity_plot],
)
return deberta_model |