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LICENSE

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+
+MIT License
+
+Copyright (c) 2025 Eric Maldon
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+
+---
+title: Emoji Mood Classifier
+emoji: 🎭
+colorFrom: yellow
+colorTo: pink
+sdk: gradio
+app_file: app.py
+pinned: false
+---
+
+# Emoji Mood Classifier
+
+## Overview
+A lightweight and fun Hugging Face Space that performs sentiment analysis on text and returns a sequence of emojis matching the detected mood. Built using the `transformers` library and a pre-trained sentiment model, it provides a creative and unique way to visualize text sentiment.
+
+## Key Features
+- Returns emoji sequences instead of boring labels.
+- Works entirely on CPU with a lightweight model.
+- Handles Positive, Negative, and Neutral moods.
+
+## How to Run Locally
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+## Acceptable Use
+This project is for educational, research, and entertainment purposes only. Do not use it for harmful, illegal, or unethical purposes. It follows Hugging Face and model license policies.

app.py

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+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()

requirements.txt

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+gradio>=4.36.1
+transformers>=4.41.0
+torch

runtime.txt

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+python-3.10