Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# 3-class model (negative / neutral / positive)
+MODEL_ID = "cardiffnlp/twitter-roberta-base-sentiment-latest"
+sentiment_pipeline = pipeline("sentiment-analysis", model=MODEL_ID)
+
+LABEL_MAP = {
+    "LABEL_0": "Negative",
+    "LABEL_1": "Neutral",
+    "LABEL_2": "Positive",
+    "NEGATIVE": "Negative",
+    "NEUTRAL": "Neutral",
+    "POSITIVE": "Positive",
+}
+
+def analyze_sentiment(text):
+    text = (text or "").strip()
+    if not text:
+        return "⚠️ Please enter some text."
+    result = sentiment_pipeline(text, truncation=True)[0]
+    label = LABEL_MAP.get(result["label"], result["label"].title())
+    score = round(float(result["score"]), 3)
+    return f"{label}  (confidence: {score})"
+
+demo = gr.Interface(
+    fn=analyze_sentiment,
+    inputs=gr.Textbox(lines=3, placeholder="Type a sentence here..."),
+    outputs="text",
+    title="Sentiment Analyzer",
+    description="Classifies text as Negative, Neutral, or Positive using a Hugging Face transformer.",
+    examples=[["I love this!"], ["This is okay, I guess."], ["I hate it."]],
+)
+
+if __name__ == "__main__":
+    demo.launch()