app.py

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, pipeline
import gradio as gr

# Load tokenizer and model explicitly
tokenizer = AutoTokenizer.from_pretrained("raphgonda/FilipinoShopping")
model = TFAutoModelForSequenceClassification.from_pretrained("raphgonda/FilipinoShopping")

# Create a pipeline
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, framework="tf")

# Define the sentiment analysis function
def analyze_sentiment(text):
    try:
        result = pipe(text)
        label = result[0]["label"]
        score = round(result[0]["score"] * 100, 2)  # Convert score to percentage
        return label, f"{score}%"
    except Exception as e:
        return "Error", str(e)

# Gradio app
interface = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.Textbox(label="Enter Filipino Text"),
    outputs=[gr.Textbox(label="Sentiment"), gr.Textbox(label="Emotion Score")],
    title="Filipino Sentiment Analysis"
)

interface.launch()