Quick update

Dockerfile

@@ -38,9 +38,6 @@ COPY src/ ./src/
 COPY app.py ./
 COPY config.toml ./
 
-# Copy models
-COPY models/ ./models/
-
 # Expose Streamlit port (Hugging Face Spaces uses 7860)
 EXPOSE 7860
 

pyproject.toml

@@ -23,6 +23,7 @@ dependencies = [
     "ipykernel>=6.30.1",
     "ipywidgets>=8.1.7",
     "matplotlib>=3.10.6",
+    "mkdocs>=1.6.1",
     "mlflow>=2,<3",
     "numpy>=2.2.6",
     "pandas>=2.3.2",

src/models/prithiv_ml_food101.py

@@ -4,7 +4,6 @@ from PIL import Image
 import io
 import os
 import tempfile
-from pathlib import Path
 
 from src.models.food_classification_model import FoodClassificationModel
 
@@ -20,61 +19,28 @@ class PrithivMlFood101(FoodClassificationModel):
 
     def __init__(self, model_name: str = "prithivMLmods/Food-101-93M"):
         """
-        Load the PrithivML Food-101 model.
-
-        Preference order:
-        1) Load from local repo snapshot at <repo_root>/models/prithivMLmods/Food-101-93M
-        2) If not present, prompt the user to download using Makefile
-           make download-hf-model MODEL_PATH=prithivMLmods/Food-101-93M
+        Always load from the Hugging Face Hub. No local model storage.
         """
 
         # Set up proper cache directory for HF Spaces (safe no-op locally)
         if not os.environ.get("HF_HOME"):
-            cache_dir = Path(tempfile.gettempdir()) / "transformers_cache"
-            cache_dir.mkdir(exist_ok=True)
+            cache_dir = tempfile.mkdtemp(prefix="transformers_cache_")
             os.environ["HF_HOME"] = str(cache_dir)
 
-        # Resolve repo root robustly from this file's location
-        repo_root = Path(__file__).resolve().parents[2]
-        local_model_dir = repo_root / "models" / "prithivMLmods" / "Food-101-93M"
+        cache_dir = os.environ.get("HF_HOME")
 
-        # Determine whether a local copy exists (safetensors or bin)
-        local_exists = local_model_dir.exists() and (
-            (local_model_dir / "model.safetensors").exists()
-            or (local_model_dir / "pytorch_model.bin").exists()
+        # Load from the Hub
+        self.model = SiglipForImageClassification.from_pretrained(
+            model_name,
+            cache_dir=cache_dir,
         )
-
-        if not local_exists:
-            # Provide a clear, actionable message to fetch the model snapshot
-            make_cmd = "make download-hf-model MODEL_PATH=prithivMLmods/Food-101-93M"
-            raise RuntimeError(
-                "Local model not found at 'models/prithivMLmods/Food-101-93M'. "
-                "Please download it first using:\n"
-                f"  {make_cmd}\n"
-                "After download completes, re-run your program."
-            )
-
-        # Load from local directory snapshot
-        try:
-            self.model = SiglipForImageClassification.from_pretrained(
-                str(local_model_dir),
-                cache_dir=os.environ.get("HF_HOME"),
-                local_files_only=True,
-                force_download=False,
-            )
-            self.processor = AutoImageProcessor.from_pretrained(
-                str(local_model_dir),
-                cache_dir=os.environ.get("HF_HOME"),
-                local_files_only=True,
-                force_download=False,
-                use_fast=True,  # Use fast processor to avoid warning
-            )
-            self.model_name = str(local_model_dir)
-        except Exception as e:
-            raise RuntimeError(
-                "Failed to load local model from 'models/prithivMLmods/Food-101-93M': "
-                f"{str(e)}"
-            )
+        self.processor = AutoImageProcessor.from_pretrained(
+            model_name,
+            cache_dir=cache_dir,
+            use_fast=True,
+        )
+        self.model_name = model_name
+        self.model_path = model_name
 
     def classify(self, image: bytes) -> int:
         pil_image = Image.open(io.BytesIO(image)).convert("RGB")

src/models/resnet18.py

@@ -3,7 +3,6 @@ from transformers import AutoImageProcessor, AutoModelForImageClassification
 from PIL import Image
 import io
 import os
-from pathlib import Path
 
 from src.models.food_classification_model import FoodClassificationModel
 
@@ -20,35 +19,23 @@ class Resnet18(FoodClassificationModel):
         model_path: str = "microsoft/resnet-18",
     ):
         """
-        Prefer loading from a local snapshot under models/microsoft/resnet-18.
-        If the local snapshot doesn't exist, prompt the user to download it via Makefile.
+        Always load from the Hugging Face Hub. No local model storage.
         """
 
-        # Resolve repo root and local model dir
-        repo_root = Path(__file__).resolve().parents[2]
-        local_model_dir = repo_root / "models" / "microsoft" / "resnet-18"
+        cache_dir = os.environ.get("HF_HOME")
 
-        # Check if a local HF snapshot exists (config + weights)
-        local_exists = local_model_dir.exists() and (
-            (local_model_dir / "pytorch_model.bin").exists()
-            or (local_model_dir / "model.safetensors").exists()
+        # Load from the Hub (will cache under HF_HOME if set)
+        self.image_processor = AutoImageProcessor.from_pretrained(
+            preprocessor_path, cache_dir=cache_dir
         )
-
-        if not local_exists:
-            make_cmd = "make download-hf-model MODEL_PATH=microsoft/resnet-18"
-            raise RuntimeError(
-                "Local model not found at 'models/microsoft/resnet-18'. "
-                "Please download it first using:\n"
-                f"  {make_cmd}\n"
-                "After download completes, re-run your program."
-            )
-
-        # Load from local folder
-        self.image_processor = AutoImageProcessor.from_pretrained(str(local_model_dir))
         self.model = AutoModelForImageClassification.from_pretrained(
-            str(local_model_dir)
+            model_path, cache_dir=cache_dir
         )
 
+        # For metadata/logging
+        self.model_path = model_path
+        self.preprocessor_path = preprocessor_path
+
     def classify(self, image: bytes) -> int:
         pil_image = Image.open(io.BytesIO(image))
         inputs = self.image_processor(pil_image, return_tensors="pt")

src/models/vgg16.py

@@ -4,7 +4,6 @@ import torchvision.transforms as transforms
 import torchvision.models as models
 from PIL import Image
 import io
-from pathlib import Path
 from typing import Dict, Any
 from src.models.food_classification_model import FoodClassificationModel
 
@@ -14,93 +13,16 @@ class VGG16(FoodClassificationModel):
 
     def __init__(self, weights: str = "IMAGENET1K_V1", num_classes: int = 101):
         """
-        Initialize VGG-16. Prefer loading local fine-tuned weights if available.
-
-        Priority:
-        1) Load ImageNet base and replace classifier, then load local fine-tuned checkpoint
-           from <repo_root>/models/vgg16/vgg16-397923af.pth (if exists).
-        2) Otherwise, fall back to ImageNet weights only (not Food-101 trained), and
-           instruct user to provide or train a .pth for Food-101 fine-tuning.
+        Initialize VGG-16 strictly from torchvision weights (no local checkpoints).
+        Note: This will not be Food-101 fine-tuned unless you use a hub-published
+        VGG-16 checkpoint. Consider switching to hub-based models for best results.
         """
-        repo_root = Path(__file__).resolve().parents[2]
-        local_weights = repo_root / "models" / "vgg16/vgg16-397923af.pth"
-
         # Base model with ImageNet weights
         self.model = models.vgg16(weights=weights)
 
         num_features = self.model.classifier[6].in_features
         self.model.classifier[6] = nn.Linear(num_features, num_classes)
 
-        # If local fine-tuned weights exist, load them
-        if local_weights.exists():
-            try:
-                raw_ckpt: Dict[str, Any] = torch.load(local_weights, map_location="cpu")
-
-                # Unwrap common checkpoint formats
-                if isinstance(raw_ckpt, dict) and "state_dict" in raw_ckpt:
-                    ckpt = raw_ckpt["state_dict"]
-                else:
-                    ckpt = raw_ckpt
-
-                # Normalize key prefixes commonly introduced by wrappers
-                def strip_prefix(
-                    sd: Dict[str, torch.Tensor], prefix: str
-                ) -> Dict[str, torch.Tensor]:
-                    if all(k.startswith(prefix) for k in sd.keys()):
-                        return {k[len(prefix) :]: v for k, v in sd.items()}
-                    return sd
-
-                for p in ("module.", "model.", "net."):
-                    ckpt = strip_prefix(ckpt, p)
-
-                # Filter out mismatched keys (e.g., classifier.6 for 1000->101 classes)
-                model_sd = self.model.state_dict()
-                filtered_ckpt = {}
-                skipped = []
-                for k, v in ckpt.items():
-                    if k in model_sd and isinstance(v, torch.Tensor):
-                        if model_sd[k].shape == v.shape:
-                            filtered_ckpt[k] = v
-                        else:
-                            skipped.append(
-                                (k, tuple(v.shape), tuple(model_sd[k].shape))
-                            )
-                    # Silently ignore keys not present in the current model
-
-                missing_before = set(model_sd.keys()) - set(filtered_ckpt.keys())
-                self.model.load_state_dict(filtered_ckpt, strict=False)
-
-                # Optional: print a brief summary to logs for transparency
-                if skipped:
-                    skipped_str = ", ".join(
-                        [f"{k}: {src} -> {dst}" for k, src, dst in skipped[:5]]
-                    )
-                    more = "" if len(skipped) <= 5 else f" (+{len(skipped)-5} more)"
-                    print(
-                        f"[VGG16] Partially loaded checkpoint from '{local_weights}'. "
-                        f"Skipped mismatched keys: {skipped_str}{more}"
-                    )
-
-                if (
-                    "classifier.6.weight" in missing_before
-                    or "classifier.6.bias" in missing_before
-                ):
-                    print(
-                        "[VGG16] Final classifier layer initialized for 101 classes and was not loaded from checkpoint."
-                    )
-
-            except Exception as e:
-                raise RuntimeError(
-                    f"Failed to load local VGG16 weights from '{local_weights}': {e}"
-                )
-        else:
-            # No local fine-tuned weights: keep ImageNet weights but warn with action
-            raise RuntimeError(
-                "Local fine-tuned weights not found at 'models/vgg16/vgg16-397923af.pth'.\n"
-                "Please place your fine-tuned checkpoint there, or train/export one.\n"
-                "Alternatively, switch to a HF model with a Makefile download target."
-            )
-
         self.model.eval()
 
         self.transform = transforms.Compose(
@@ -125,6 +47,6 @@ class VGG16(FoodClassificationModel):
 
         with torch.no_grad():
             outputs = self.model(input_batch)
-            predicted_idx = torch.argmax(outputs).item()
+            predicted_idx = torch.argmax(outputs, dim=1).item()
 
         return predicted_idx

uv.lock

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 ]
 
+[[package]]
+name = "ghp-import"
+version = "2.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "python-dateutil" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d9/29/d40217cbe2f6b1359e00c6c307bb3fc876ba74068cbab3dde77f03ca0dc4/ghp-import-2.1.0.tar.gz", hash = "sha256:9c535c4c61193c2df8871222567d7fd7e5014d835f97dc7b7439069e2413d343", size = 10943, upload-time = "2022-05-02T15:47:16.11Z" }
+wheels = [
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+]
+
 [[package]]
 name = "gitdb"
 version = "4.0.12"
@@ -2439,6 +2451,53 @@ wheels = [
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 ]
 
+[[package]]
+name = "mergedeep"
+version = "1.3.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3a/41/580bb4006e3ed0361b8151a01d324fb03f420815446c7def45d02f74c270/mergedeep-1.3.4.tar.gz", hash = "sha256:0096d52e9dad9939c3d975a774666af186eda617e6ca84df4c94dec30004f2a8", size = 4661, upload-time = "2021-02-05T18:55:30.623Z" }
+wheels = [
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+]
+
+[[package]]
+name = "mkdocs"
+version = "1.6.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "ghp-import" },
+    { name = "jinja2" },
+    { name = "markdown" },
+    { name = "markupsafe" },
+    { name = "mergedeep" },
+    { name = "mkdocs-get-deps" },
+    { name = "packaging" },
+    { name = "pathspec" },
+    { name = "pyyaml" },
+    { name = "pyyaml-env-tag" },
+    { name = "watchdog" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/bc/c6/bbd4f061bd16b378247f12953ffcb04786a618ce5e904b8c5a01a0309061/mkdocs-1.6.1.tar.gz", hash = "sha256:7b432f01d928c084353ab39c57282f29f92136665bdd6abf7c1ec8d822ef86f2", size = 3889159, upload-time = "2024-08-30T12:24:06.899Z" }
+wheels = [
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+]
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+[[package]]
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+version = "0.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+    { name = "platformdirs" },
+    { name = "pyyaml" },
+]
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+wheels = [
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+]
+
 [[package]]
 name = "mlflow"
 version = "2.22.2"
@@ -3763,6 +3822,18 @@ wheels = [
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 ]
 
+[[package]]
+name = "pyyaml-env-tag"
+version = "1.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pyyaml" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/eb/2e/79c822141bfd05a853236b504869ebc6b70159afc570e1d5a20641782eaa/pyyaml_env_tag-1.1.tar.gz", hash = "sha256:2eb38b75a2d21ee0475d6d97ec19c63287a7e140231e4214969d0eac923cd7ff", size = 5737, upload-time = "2025-05-13T15:24:01.64Z" }
+wheels = [
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+]
+
 [[package]]
 name = "pyzmq"
 version = "27.1.0"
@@ -4725,6 +4796,7 @@ dependencies = [
     { name = "ipykernel" },
     { name = "ipywidgets" },
     { name = "matplotlib" },
+    { name = "mkdocs" },
     { name = "mlflow" },
     { name = "numpy" },
     { name = "pandas" },
@@ -4758,6 +4830,7 @@ requires-dist = [
     { name = "ipykernel", specifier = ">=6.30.1" },
     { name = "ipywidgets", specifier = ">=8.1.7" },
     { name = "matplotlib", specifier = ">=3.10.6" },
+    { name = "mkdocs", specifier = ">=1.6.1" },
     { name = "mlflow", specifier = ">=2,<3" },
     { name = "numpy", specifier = ">=2.2.6" },
     { name = "pandas", specifier = ">=2.3.2" },
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 wheels = [
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