Update config.json

config.json

@@ -1,3 +1,32 @@
 {
-  "num_classes": 7
-}
+  "model_name": "facial_emotion_simplecnn",
+  "model_type": "simple_cnn",
+  "framework": "pytorch",
+  "task": "image-classification",
+
+  "num_classes": 7,
+  "labels": {
+    "0": "angry",
+    "1": "disgust",
+    "2": "fear",
+    "3": "happy",
+    "4": "sad",
+    "5": "surprise",
+    "6": "neutral"
+  },
+
+  "in_channels": 1,
+  "input_size": [48, 48],
+  "preprocessing": {
+    "resize": [48, 48],
+    "normalize_mean": [0.5],
+    "normalize_std": [0.5],
+    "color_mode": "grayscale"
+  },
+
+  "training_dataset": "FER2013 (or similar facial emotion dataset)",
+  "author": "sreenathsree1578",
+  "license": "mit",
+
+  "example_usage": "from facial_emotion import SimpleCNN\nfrom huggingface_hub import hf_hub_download\nimport torch\n\n# Load config\nimport json\nconfig = json.load(open('config.json'))\n\n# Build model\nmodel = SimpleCNN(num_classes=config['num_classes'], in_channels=config['in_channels'])\n\n# Load weights from hub\ncheckpoint = hf_hub_download(repo_id='sreenathsree1578/facial_emotion', filename='pytorch_model.bin')\nmodel.load_state_dict(torch.load(checkpoint, map_location='cpu'))\nmodel.eval()\n\n# Example inference\ntensor = torch.randn(1, 1, 48, 48)  # dummy grayscale image\nwith torch.no_grad():\n    output = model(tensor)\n    pred = torch.argmax(output, dim=1).item()\n    print('Predicted class:', config['labels'][str(pred)])"
+}