Update README.md

README.md

@@ -1,33 +1,43 @@
-# 🎭 Emotion Classifier — RoBERTa Based
-A deep-learning model that classifies text into **five emotion categories**:
-**anger, fear, joy, sadness, surprise**.
-
-Built with **PyTorch, RoBERTa transformer, Streamlit UI** and deployed on **Hugging Face Spaces**.
-
----
-
-## 🧠 Model Details
-| Component | Description |
-|-----------|-------------|
-| Base model | `roberta-base` |
-| Task | Single-label Emotion Classification |
-| Input | Raw text |
-| Output | Softmax probability distribution (5 emotions) |
-| Framework | PyTorch + Transformers |
-
-### Load Model from Hugging Face
-```python
-from huggingface_hub import hf_hub_download
-import torch
-from BertEmotionClassifier import BertEmotionClassifier
-
-model_path = hf_hub_download(repo_id="aadhi3/RoBert_Model", filename="model.pth")
-
-from transformers import AutoTokenizer
-tokenizer = AutoTokenizer.from_pretrained("roberta-base")
-
-model = BertEmotionClassifier(model_name="roberta-base", num_labels=5)
-state_dict = torch.load(model_path, map_location="cpu")
-model.load_state_dict(state_dict)
-model.eval()
+---
+title: Emotion Classifier
+emoji: 🎭
+colorFrom: indigo
+colorTo: blue
+sdk: streamlit
+sdk_version: "1.32.0"
+app_file: app.py
+pinned: false
+---
+# 🎭 Emotion Classifier — RoBERTa Based
+A deep-learning model that classifies text into **five emotion categories**:
+**anger, fear, joy, sadness, surprise**.
+
+Built with **PyTorch, RoBERTa transformer, Streamlit UI** and deployed on **Hugging Face Spaces**.
+
+---
+
+## 🧠 Model Details
+| Component | Description |
+|-----------|-------------|
+| Base model | `roberta-base` |
+| Task | Single-label Emotion Classification |
+| Input | Raw text |
+| Output | Softmax probability distribution (5 emotions) |
+| Framework | PyTorch + Transformers |
+
+### Load Model from Hugging Face
+```python
+from huggingface_hub import hf_hub_download
+import torch
+from BertEmotionClassifier import BertEmotionClassifier
+
+model_path = hf_hub_download(repo_id="aadhi3/RoBert_Model", filename="model.pth")
+
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("roberta-base")
+
+model = BertEmotionClassifier(model_name="roberta-base", num_labels=5)
+state_dict = torch.load(model_path, map_location="cpu")
+model.load_state_dict(state_dict)
+model.eval()
 ```