Update app.py

app.py

@@ -1,13 +1,34 @@
 import gradio as gr
-from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
 from peft import PeftModel
 
-# Load model + LoRA
+# Load base + adapter
 base = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
 model = PeftModel.from_pretrained(base, "NightPrince/peft-distilbert-sst2")
 tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
 
 pipe = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
 
-# Gradio interface
-gr.Interface(fn=pipe, inputs="text", outputs="label", title="LoRA Sentiment Tester").launch()
+# Wrapper function to return result in {label: score} format
+def classify(text):
+    result = pipe(text)[0]
+    return {result['label']: result['score']}
+
+# Define example inputs
+examples = [
+    ["I love this movie! It's fantastic."],
+    ["The product was terrible and broke after one use."],
+    ["Honestly, it was okay — not bad, not great."],
+    ["What a masterpiece, I was speechless."],
+    ["I wouldn't recommend this to anyone."],
+]
+
+# Launch Gradio Interface
+gr.Interface(
+    fn=classify,
+    inputs=gr.Textbox(placeholder="Enter a sentence...", label="Input Text"),
+    outputs=gr.Label(num_top_classes=2, label="Sentiment"),
+    examples=examples,
+    title="🧠 LoRA Sentiment Classifier",
+    description="Fine-tuned DistilBERT using PEFT (LoRA) on SST-2. Try it with your own sentences!"
+).launch()