Initial GoVed-AI model upload

Readme.md

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+# 🐄 GoVed-AI: Indian Cattle & Buffalo Breed Classifier
+
+GoVed-AI is a deep learning model trained to classify 40+ Indian cattle and buffalo breeds using **ResNet-18**.
+
+## 🚀 Usage
+You can use the Hugging Face Inference API:
+
+```python
+from huggingface_hub import InferenceClient
+from PIL import Image
+
+client = InferenceClient("username/GoVed-AI")
+
+result = client.post(
+    json={"inputs": "https://example.com/cow.jpg"}
+)
+
+print(result)

bovine_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0f56d9712d45af4f1e7dded9c6dc07243a95ae586445aa0e987057124669efea
+size 16540501

inference.py

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+import torch
+import torch.nn as nn
+import torchvision.models as models
+import torchvision.transforms as transforms
+from PIL import Image
+
+# ----------------------------
+# Labels (all breeds)
+# ----------------------------
+breeds = [
+    "Alambadi", "Amritmahal", "Ayrshire", "Banni", "Bargur", "Bhadawari", "Brown_Swiss",
+    "Dangi", "Deoni", "Gir", "Guernsey", "Hallikar", "Hariana", "Holstein_Friesian",
+    "Jaffrabadi", "Jersey", "Kangayam", "Kankrej", "Kasargod", "Kenkatha", "Kherigarh",
+    "Khillari", "Krishna_Valley", "Malnad_gidda", "Mehsana", "Murrah", "Nagori", "Nagpuri",
+    "Nili_Ravi", "Nimari", "Ongole", "Pulikulam", "Rathi", "Red_Dane", "Red_Sindhi",
+    "Sahiwal", "Surti", "Tharparkar", "Toda", "Umblachery", "Vechur"
+]
+
+# ----------------------------
+# Load Model
+# ----------------------------
+def load_model():
+    model = models.resnet18(pretrained=False)
+    model.fc = nn.Linear(model.fc.in_features, len(breeds))
+    model.load_state_dict(torch.load("bovine_model.pth", map_location="cpu"))
+    model.eval()
+    return model
+
+model = load_model()
+
+# ----------------------------
+# Image Preprocessing
+# ----------------------------
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+])
+
+# ----------------------------
+# Prediction Function
+# ----------------------------
+def predict(image: Image.Image):
+    img = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        outputs = model(img)
+        _, predicted = torch.max(outputs, 1)
+    return {"breed": breeds[predicted.item()]}