app.py

import gradio as gr
from transformers import pipeline

# Load a lightweight sentiment model (CPU-friendly)
classifier = pipeline("sentiment-analysis")

# Map model labels to emoji sets
EMOJI_MAP = {
    "POSITIVE": ["😊", "🌟", "💖", "🔥", "🎉"],
    "NEGATIVE": ["😞", "💔", "😡", "😭", "👎"],
    "NEUTRAL":  ["😐", "🤔", "📘", "💬", "🔍"],
}

def analyze_mood(text: str) -> str:
    if not text or not text.strip():
        return "❌ Please enter some text."
    result = classifier(text)[0]
    label = result["label"].upper()
    score = float(result["score"])

    # Low confidence → treat as neutral to avoid overclaiming
    if score < 0.60:
        label = "NEUTRAL"

    emojis = " ".join(EMOJI_MAP.get(label, ["🤷‍♂️"]))
    return f"{emojis}  (Confidence: {score:.2f})"

with gr.Blocks(title="Emoji Mood Classifier") as demo:
    gr.Markdown(
        "# Emoji Mood Classifier\n"
        "A fun twist on sentiment analysis — returns emojis matching your text's mood."
    )
    inp = gr.Textbox(label="Enter text", placeholder="Type a sentence here...", lines=3)
    out = gr.Textbox(label="Emoji Mood", lines=2)
    btn = gr.Button("Analyze")
    btn.click(analyze_mood, inputs=inp, outputs=out)

# Expose a top-level `demo` for Spaces
if __name__ == "__main__":
    demo.launch()