Update app.py

app.py

@@ -1,26 +1,22 @@
-import gradio as gr
 from transformers import pipeline
-# Load model directly
-from transformers import AutoTokenizer, AutoModel
-
-tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
-model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+import gradio as gr
 
-# Load Sentiment Analysis model
+# Load pre-trained sentiment analysis pipeline
 sentiment_pipeline = pipeline("sentiment-analysis")
 
+# Function to analyze sentiment
 def analyze_sentiment(text):
-    result = sentiment_pipeline(text)[0]
-    return f"Sentiment: {result['label']} (Confidence: {result['score']:.2f})"
+    results = sentiment_pipeline(text)
+    return {result['label']: round(result['score'], 2) for result in results}
 
-# Create Gradio Interface
+# Gradio Interface
 iface = gr.Interface(
     fn=analyze_sentiment,
-    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
-    outputs="text",
-    title="Sentiment Analysis API",
-    description="Enter a sentence, and the model will predict if it's POSITIVE or NEGATIVE."
+    inputs=gr.Textbox(lines=2, placeholder="Enter a sentence here..."),
+    outputs=gr.Label(num_top_classes=3),
+    title="Sentiment Analysis",
+    description="Enter a sentence, and the model will classify it as Positive, Negative, or Neutral."
 )
 
-# Launch the Gradio app
-iface.launch()
+# Launch the app
+iface.launch()