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README.md

@@ -1,12 +1,43 @@
 ---
-title: Emotion Classifier
-emoji: ⚡
-colorFrom: red
-colorTo: blue
+title: Emotion Text Classifier
+emoji: 😊
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 6.10.0
+sdk_version: "4.19.0"
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Emotion Text Classifier
+
+Fine-tuned `roberta-base` on the [dair-ai/emotion](https://huggingface.co/datasets/dair-ai/emotion) dataset.
+
+Classifies text into 6 emotions: sadness, joy, love, anger, fear, surprise.
+
+## Files
+
+| File | Purpose |
+|------|---------|
+| `train.py` | Fine-tune roberta-base on dair-ai/emotion (run on Colab) |
+| `push_to_hub.py` | Push trained model to HuggingFace Hub with model card |
+| `app.py` | Gradio demo app |
+| `requirements.txt` | Python dependencies |
+
+## Quick Start (Colab)
+
+```bash
+# 1. Install dependencies
+pip install transformers datasets scikit-learn accelerate
+
+# 2. Train
+python train.py
+
+# 3. Push to Hub (log in first: huggingface-cli login)
+python push_to_hub.py --repo_id your-username/emotion-roberta
+
+# 4. Test the demo locally
+pip install gradio
+python app.py
+```

app.py

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+
+import gradio as gr
+from transformers import pipeline
+
+# ---------------------------------------------------------------------------
+# Load the model — update this to your Hub repo ID after pushing
+# ---------------------------------------------------------------------------
+MODEL_ID = "dk409/emotion-roberta" for Hub
+classifier = pipeline("text-classification", model=MODEL_ID, top_k=None)
+
+# ---------------------------------------------------------------------------
+# Prediction function
+# ---------------------------------------------------------------------------
+def classify_emotion(text):
+    """
+    Run the classifier and return a dict of {label: score} for Gradio's Label component.
+    The Label component automatically sorts and displays as a bar chart.
+    """
+    if not text or not text.strip():
+        return {}
+
+    results = classifier(text)[0]  # list of {"label": ..., "score": ...}
+    return {r["label"]: r["score"] for r in results}
+
+# ---------------------------------------------------------------------------
+# Gradio interface
+# ---------------------------------------------------------------------------
+examples = [
+    "I'm so happy to see you after all these years!",
+    "This is absolutely terrifying, I can't watch.",
+    "I can't believe they cancelled the show. So angry right now.",
+    "She looked at him with so much love in her eyes.",
+    "I feel so alone and empty inside.",
+    "Wait, you got promoted? I had no idea! That's amazing!",
+]
+
+demo = gr.Interface(
+    fn=classify_emotion,
+    inputs=gr.Textbox(
+        label="Enter text",
+        placeholder="Type a sentence and I'll detect the emotion...",
+        lines=3,
+    ),
+    outputs=gr.Label(label="Emotion Probabilities", num_top_classes=6),
+    title="Emotion Text Classifier",
+    description=(
+        "Detects 6 emotions in text: **sadness, joy, love, anger, fear, surprise**. "
+        "Fine-tuned RoBERTa model trained on the dair-ai/emotion dataset."
+    ),
+    examples=examples,
+    allow_flagging="never",
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers>=4.38.0
+torch>=2.0.0
+datasets>=2.18.0
+scikit-learn>=1.4.0
+accelerate>=0.27.0
+gradio>=4.19.0
+huggingface_hub>=0.21.0