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# Import necessary libraries
from transformers import pipeline
import gradio as gr
# Load the Filipino sentiment analysis model
pipe = pipeline("text-classification", model="raphgonda/FilipinoShopping")
# Define the sentiment analysis function
def analyze_sentiment(text):
try:
# Predict sentiment using the model
results = pipe(text)
# Extract label and score
label = results[0]["label"]
score = round(results[0]["score"] * 100, 2) # Convert score to percentage
return label, f"{score}%"
except Exception as e:
return "Error", "N/A"
# Create a Gradio interface with custom UI
with gr.Blocks() as interface:
gr.Markdown("<h1 style='text-align: center;'>Filipino Sentiment Analysis</h1>")
gr.Markdown("<p style='text-align: center;'>Enter text in Filipino to analyze its sentiment.</p>")
with gr.Row():
input_text = gr.Textbox(
label="Enter text to analyze its sentiment",
placeholder="Type your text here...",
)
with gr.Row():
submit_btn = gr.Button("Submit")
clear_btn = gr.Button("Clear")
sentiment_label = gr.Textbox(label="Sentiment Label", interactive=False, visible=True)
with gr.Row():
emotion_score = gr.Textbox(label="Emotion Score", interactive=False)
examples = gr.Examples(
examples=[
["Okay ang aesthetic"],
["Mabagal ang delivery"],
["Napakaganda ng serbisyo!"],
["Ang pangit ng produkto."]
],
inputs=input_text,
)
# Define the function connection
submit_btn.click(
analyze_sentiment,
inputs=[input_text],
outputs=[sentiment_label, emotion_score],
)
clear_btn.click(
lambda: ("", ""),
inputs=[],
outputs=[sentiment_label, emotion_score],
)
# Launch the app
interface.launch()
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