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README.md

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+Emotion Detection from Text using BERT
+
+Welcome to the Emotion Detection Web App. This application uses a fine-tuned BERT model to detect human emotions from short pieces of text.
+
+#Description
+
+This project leverages the `nateraw/bert-base-uncased-emotion` model from Hugging Face Transformers to classify input text into one of six emotions:
+
+- 😢 Sadness
+- 😀 Joy
+- 💖 Love
+- 😡 Anger
+- 😱 Fear
+- 😲 Surprise
+
+It uses:
+-Hugging Face Transformers** for model and tokenizer
+-PyTorch for deep learning inference
+-Gradio to build an interactive web interface
+
+Model Used
+
+Model Name: `nateraw/bert-base-uncased-emotion`
+Base Architecture: BERT (uncased)
+Dataset: GoEmotions subset
+
+ How It Works
+
+1. You type a sentence like:
+   > "I just got a new job!"
+
+2. The model analyzes the text and returns the predicted emotion with confidence score.
+
+
+Dependencies
+
+Dependencies are defined in `requirements.txt`:
+- `transformers`
+- `torch`
+- `gradio`
+
+
+ Use Cases
+
+- Social media sentiment analysis
+- Customer feedback classification
+- Chatbot emotion understanding
+- Mental health applications
+
+
+**Author
+
+- **Sujith Kumar**
+- Hugging Face: [@sujith13082003](https://huggingface.co/sujith13082003)
+
 ---
-title: Text Emotion Detection
-emoji: 💻
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 5.29.0
-app_file: app.py
-pinned: false
----
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 📝 License
+
+This project is for educational and research purposes. Refer to individual library licenses for commercial use.
+