app.py

app.py

@@ -1,19 +1,23 @@
-from transformers import pipeline
 import gradio as gr
+from transformers import pipeline
 
-# Load sentiment analysis model
-sentiment_pipeline = pipeline("text-classification", model="tabularisai/multilingual-sentiment-analysis")
+# Load pre-trained sentiment analysis model from Hugging Face
+sentiment_pipeline = pipeline("tabularisai/multilingual-sentiment-analysis")
 
-# Function for sentiment analysis
-def sentiment_analysis(text):
+def analyze_sentiment(text):
     result = sentiment_pipeline(text)[0]
-    return f"Label: {result['label']}, Confidence: {round(result['score'], 4)}"
+    label = result['label']
+    score = round(result['score'], 3)
+    return f"Sentiment: {label} | Confidence: {score}"
 
-# Gradio Interface
-demo = gr.Interface(fn=sentiment_analysis, 
-                    inputs=gr.Textbox(label="Enter Text"), 
-                    outputs=gr.Textbox(label="Sentiment Output"), 
-                    title="Multilingual Sentiment Analysis")
+# Gradio UI
+iface = gr.Interface(
+    fn=analyze_sentiment,
+    inputs=gr.Textbox(label="Enter Text"),
+    outputs=gr.Textbox(label="Sentiment Result"),
+    title="Sentiment Analysis with Hugging Face 🤗",
+    description="This app performs sentiment analysis on user input text using Hugging Face Transformers."
+)
 
-# Launch app locally
-demo.launch()
+if __name__ == "__main__":
+    iface.launch()