just upload through git

.dockerignore.cog.bak

@@ -0,0 +1,43 @@
+# generated by replicate/cog
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+env
+pip-log.txt
+pip-delete-this-directory.txt
+.tox
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.log
+.git
+.mypy_cache
+.pytest_cache
+.hypothesis
+
+# generated by replicate/cog
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+env
+pip-log.txt
+pip-delete-this-directory.txt
+.tox
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.log
+.git
+.mypy_cache
+.pytest_cache
+.hypothesis

.envrc

@@ -1 +1 @@
-HF_USERNAME=bawolf
+export HF_USERNAME=bawolf

.gitignore

@@ -25,6 +25,7 @@ wheels/
 venv/
 ENV/
 .env
+.envrc
 
 # IDE
 .idea/

.sample.envrc

@@ -0,0 +1 @@
+export HF_USERNAME=bawolf

scripts/upload_to_hub.py

@@ -1,44 +0,0 @@
-from transformers import CLIPProcessor
-from huggingface_hub import HfApi
-import os
-from dotenv import load_dotenv
-import torch
-from src.models.model import create_model
-
-def upload_model_to_hub(hf_username):
-    # Initialize huggingface api
-    api = HfApi()
-    
-    # Load your custom model
-    num_classes = 3  # windmills, halos, and swipes
-    model = create_model(num_classes, "openai/clip-vit-large-patch14")
-    
-    # Load your trained weights
-    state_dict = torch.load("./checkpoints/model.pth", map_location="cpu")
-    model.load_state_dict(state_dict, strict=False)
-    
-    # Get the processor from the base CLIP model
-    processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
-    
-    repo_id = f"{hf_username}/breaking-vision-clip-classifier"
-    
-    # Save model configuration and architecture
-    config = {
-        "num_classes": num_classes,
-        "base_model": "openai/clip-vit-large-patch14",
-        "class_labels": ["windmill", "halo", "swipe"],
-        "model_type": "VariableLengthCLIP"
-    }
-    
-    # Push to hub with config
-    model.push_to_hub(
-        repo_id,
-        config_dict=config,
-        commit_message="Upload custom CLIP-based dance classifier"
-    )
-    processor.push_to_hub(repo_id)
-
-if __name__ == "__main__":
-    load_dotenv()
-    hf_username = os.getenv("HF_USERNAME")
-    upload_model_to_hub(hf_username)