Fix Python usage examples with working code

README.md

@@ -65,10 +65,11 @@ pip install open-clip-torch torch pillow
 ### Python Usage
 
 ```python
+# Clone or download this repo first, then:
 from calorie_clip import CalorieCLIP
 
-# Load model
-model = CalorieCLIP.from_pretrained("jc-builds/CalorieCLIP")
+# Load model from local directory
+model = CalorieCLIP.from_pretrained(".")
 
 # Predict calories
 calories = model.predict("food_photo.jpg")
@@ -79,6 +80,42 @@ images = ["breakfast.jpg", "lunch.jpg", "dinner.jpg"]
 results = model.predict_batch(images)
 ```
 
+### Direct Usage (no wrapper)
+
+```python
+import torch
+import open_clip
+from PIL import Image
+
+# Load CLIP
+clip, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='openai')
+checkpoint = torch.load('calorie_clip.pt', map_location='cpu', weights_only=False)
+clip.load_state_dict(checkpoint['clip_state'], strict=False)
+
+# Load regression head
+import torch.nn as nn
+class RegressionHead(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(512, 512), nn.BatchNorm1d(512), nn.ReLU(), nn.Dropout(0.4),
+            nn.Linear(512, 256), nn.BatchNorm1d(256), nn.ReLU(), nn.Dropout(0.3),
+            nn.Linear(256, 64), nn.ReLU(), nn.Linear(64, 1)
+        )
+    def forward(self, x): return self.net(x)
+
+head = RegressionHead()
+head.load_state_dict(checkpoint['regressor_state'])
+clip.eval(); head.eval()
+
+# Predict
+img = preprocess(Image.open('food.jpg')).unsqueeze(0)
+with torch.no_grad():
+    features = clip.encode_image(img)
+    calories = head(features).item()
+print(f"{calories:.0f} calories")
+```
+
 ### Command Line
 
 ```bash