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import gradio as gr
from transformers import pipeline
import os
HF_TOKEN = os.getenv('HF_TOKEN')
hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "crowdsourced-sentiment")
def sentiment_analysis_generate_text(text):
# Define the model
model_name = "gsar78/HellenicSentimentAI"
# Create the pipeline
nlp = pipeline("sentiment-analysis", model=model_name)
# Split the input text into individual sentences
sentences = text.split('|')
# Run the pipeline on each sentence and collect the results
results = nlp(sentences)
output = []
for sentence, result in zip(sentences, results):
output.append(f"Text: {sentence.strip()}\nSentiment: {result['label']}, Score: {result['score']:.4f}\n")
# Join the results into a single string to return
return "\n".join(output)
def sentiment_analysis_generate_table(text):
# Define the model
model_name = "gsar78/HellenicSentimentAI"
# Create the pipeline
nlp = pipeline("sentiment-analysis", model=model_name)
# Split the input text into individual sentences
sentences = text.split('|')
# Generate the HTML table with enhanced colors and bold headers
html = """
<html>
<head>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.0/css/bootstrap.min.css">
<style>
.label {
transition: .15s;
border-radius: 8px;
padding: 5px 10px;
font-size: 14px;
text-transform: uppercase;
}
.positive {
background-color: rgb(54, 176, 75);
color: white;
}
.negative {
background-color: rgb(237, 83, 80);
color: white;
}
.neutral {
background-color: rgb(255, 165, 0);
color: white;
}
th {
font-weight: bold;
color: rgb(106, 38, 198);
}
</style>
</head>
<body>
<table class="table table-striped">
<thead>
<tr>
<th scope="col">Text</th>
<th scope="col">Score</th>
<th scope="col">Sentiment</th>
</tr>
</thead>
<tbody>
"""
for sentence in sentences:
result = nlp(sentence.strip())[0]
text = sentence.strip()
score = f"{result['score']:.4f}"
sentiment = result['label']
# Determine the sentiment class
if sentiment.lower() == "positive":
sentiment_class = "positive"
elif sentiment.lower() == "negative":
sentiment_class = "negative"
else:
sentiment_class = "neutral"
# Generate table rows
html += f'<tr><td>{text}</td><td>{score}</td><td><span class="label {sentiment_class}">{sentiment}</span></td></tr>'
html += """
</tbody>
</table>
</body>
</html>
"""
return html
if __name__ == "__main__":
iface = gr.Interface(
fn=sentiment_analysis_generate_table,
inputs=gr.Textbox(placeholder="Enter sentence here..."),
outputs=gr.HTML(),
title="Hellenic Sentiment AI",
description="A sentiment analysis model, primarily for the Greek language.<br>"
"Type in some text to see its sentiment classification: positive, neutral, or negative.<br>"
"Multiple sentences can be classified when separated by the | character.<br>"
"For Emotion & Sentiment Classification visit Version 2.0: <a href='https://gsar78-hellenicsentimentai-v2.hf.space' target='_blank'>Hellenic Sentiment AI v2</a><br>"
"Version 1.1 - Developed by GeoSar",
examples=[
["Η πικάντικη γεύση αυτής της σούπας λαχανικών ήταν ακριβώς αυτό που χρειαζόμουν σήμερα. Είχε μια ωραία γαργαλιστική αίσθηση χωρίς να είναι πολύ καυτερή."],
["Η πίτσα ήταν καμένη και τα υλικά φθηνής ποιότητας. Σίγουρα δεν θα ξαναπαραγγείλω από εκεί."]
],
allow_flagging="manual",
flagging_options=["Incorrect", "Ambiguous"],
flagging_callback=hf_writer,
examples_per_page=2,
allow_duplication=False,
concurrency_limit="default"
)
iface.launch(share=True) |