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README.md

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-tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
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-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+# Facial Emotion Recognition – SimpleCNN
+
+A lightweight convolutional neural network (CNN) for **facial emotion recognition**.  
+The model is trained to classify grayscale facial images into **7 emotion categories**.
+
+## Model Details
+- **Model type:** SimpleCNN (custom lightweight CNN)
+- **Framework:** PyTorch
+- **Task:** Image Classification
+- **Input shape:** 1 × 48 × 48 (grayscale image)
+- **Number of classes:** 7
+- **Classes:**
+  - 0 → Angry  
+  - 1 → Disgust  
+  - 2 → Fear  
+  - 3 → Happy  
+  - 4 → Sad  
+  - 5 → Surprise  
+  - 6 → Neutral  
+
+## Intended Uses & Limitations
+- ✅ Educational projects, demos, prototypes.  
+- ❌ Not suitable for medical, psychological, or safety-critical applications.  
+- ❌ May not generalize well outside datasets like FER2013.  
+
+## How to Use
+```python
+from huggingface_hub import hf_hub_download
+import torch
+import json
+
+from facial_emotion import SimpleCNN  # your model class
+
+# Load config
+config = json.load(open("config.json"))
+
+# Build model
+model = SimpleCNN(num_classes=config["num_classes"], in_channels=config["in_channels"])
+
+# Load weights
+checkpoint = hf_hub_download(repo_id="sreenathsree1578/facial_emotion", filename="pytorch_model.bin")
+model.load_state_dict(torch.load(checkpoint, map_location="cpu"))
+model.eval()
+
+# Example inference
+dummy = torch.randn(1, 1, 48, 48)  # dummy grayscale image
+with torch.no_grad():
+    out = model(dummy)
+    pred = torch.argmax(out, dim=1).item()
+    print("Predicted emotion:", config["labels"][str(pred)])