Create app.py

app.py

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+import gradio as gr
+import nltk
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+
+# Download VADER lexicon on first run
+nltk.download("vader_lexicon")
+
+# Instantiate once
+sid = SentimentIntensityAnalyzer()
+
+def classify_sentiment(text: str) -> str:
+    """
+    Returns one of: "Positive", "Neutral", "Negative"
+    based on VADER’s compound score.
+    """
+    comp = sid.polarity_scores(text)["compound"]
+    if comp >=  0.05:
+        return "Positive 😀"
+    elif comp <= -0.05:
+        return "Negative 😞"
+    else:
+        return "Neutral 😐"
+
+demo = gr.Interface(
+    fn=classify_sentiment,
+    inputs=gr.Textbox(
+        lines=2,
+        placeholder="Type an English sentence here…",
+        label="Your text"
+    ),
+    outputs=gr.Radio(
+        choices=["Positive 😀", "Neutral 😐", "Negative 😞"],
+        label="Sentiment"
+    ),
+    examples=[
+        ["I absolutely love this product!"],
+        ["It was okay, nothing special."],
+        ["This is the worst experience ever…"]
+    ],
+    title="3-Way Sentiment Classifier",
+    description=(
+        "Classifies English text as **Positive**, **Neutral**, or **Negative**\n"
+        "using NLTK’s VADER (thresholds at ±0.05 on the compound score)."
+    ),
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    demo.launch()