Expose LabelTable and load_list and give example in README how they can be used to rank your own list of terms.

README.md

@@ -40,7 +40,7 @@ Install with PIP
 pip3 install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu117
 
 # install clip-interrogator
-pip install clip-interrogator==0.5.4
+pip install clip-interrogator==0.5.5
 ```
 
 You can then use it in your script
@@ -67,3 +67,17 @@ The `Config` object lets you configure CLIP Interrogator's processing.
 On systems with low VRAM you can call `config.apply_low_vram_defaults()` to reduce the amount of VRAM needed (at the cost of some speed and quality). The default settings use about 6.3GB of VRAM and the low VRAM settings use about 2.7GB.
 
 See the [run_cli.py](https://github.com/pharmapsychotic/clip-interrogator/blob/main/run_cli.py) and [run_gradio.py](https://github.com/pharmapsychotic/clip-interrogator/blob/main/run_gradio.py) for more examples on using Config and Interrogator classes.
+
+
+## Ranking against your own list of terms
+
+```python
+from clip_interrogator import Config, Interrogator, LabelTable, load_list
+from PIL import Image
+
+ci = Interrogator(Config(blip_model_type=None))
+image = Image.open(image_path).convert('RGB')
+table = LabelTable(load_list('terms.txt'), 'terms', ci)
+best_match = table.rank(ci.image_to_features(image), top_count=1)[0]
+print(best_match)
+```

clip_interrogator/__init__.py

@@ -1,4 +1,4 @@
-from .clip_interrogator import Interrogator, Config
+from .clip_interrogator import Config, Interrogator, LabelTable, load_list
 
-__version__ = '0.5.4'
+__version__ = '0.5.5'
 __author__ = 'pharmapsychotic'

clip_interrogator/clip_interrogator.py

@@ -29,20 +29,20 @@ CACHE_URL_BASE = 'https://huggingface.co/pharma/ci-preprocess/resolve/main/'
 @dataclass 
 class Config:
     # models can optionally be passed in directly
-    blip_model: BLIP_Decoder = None
+    blip_model: Optional[BLIP_Decoder] = None
     clip_model = None
     clip_preprocess = None
 
     # blip settings
     blip_image_eval_size: int = 384
     blip_max_length: int = 32
-    blip_model_type: str = 'large' # choose between 'base' or 'large'
+    blip_model_type: Optional[str] = 'large' # use 'base', 'large' or None
     blip_num_beams: int = 8
     blip_offload: bool = False
 
     # clip settings
     clip_model_name: str = 'ViT-L-14/openai'
-    clip_model_path: str = None
+    clip_model_path: Optional[str] = None
     clip_offload: bool = False
 
     # interrogator settings
@@ -68,7 +68,7 @@ class Interrogator():
         self.blip_offloaded = True
         self.clip_offloaded = True
 
-        if config.blip_model is None:
+        if config.blip_model is None and config.blip_model_type:
             if not config.quiet:
                 print("Loading BLIP model...")
             blip_path = os.path.dirname(inspect.getfile(blip_decoder))
@@ -121,17 +121,17 @@ class Interrogator():
         trending_list.extend(["featured on "+site for site in sites])
         trending_list.extend([site+" contest winner" for site in sites])
 
-        raw_artists = _load_list(config.data_path, 'artists.txt')
+        raw_artists = load_list(config.data_path, 'artists.txt')
         artists = [f"by {a}" for a in raw_artists]
         artists.extend([f"inspired by {a}" for a in raw_artists])
 
         self._prepare_clip()
-        self.artists = LabelTable(artists, "artists", self.clip_model, self.tokenize, config)
+        self.artists = LabelTable(artists, "artists", self)
-        self.flavors = LabelTable(_load_list(config.data_path, 'flavors.txt'), "flavors", self.clip_model, self.tokenize, config)
+        self.flavors = LabelTable(load_list(config.data_path, 'flavors.txt'), "flavors", self)
-        self.mediums = LabelTable(_load_list(config.data_path, 'mediums.txt'), "mediums", self.clip_model, self.tokenize, config)
+        self.mediums = LabelTable(load_list(config.data_path, 'mediums.txt'), "mediums", self)
-        self.movements = LabelTable(_load_list(config.data_path, 'movements.txt'), "movements", self.clip_model, self.tokenize, config)
+        self.movements = LabelTable(load_list(config.data_path, 'movements.txt'), "movements", self)
-        self.trendings = LabelTable(trending_list, "trendings", self.clip_model, self.tokenize, config)
+        self.trendings = LabelTable(trending_list, "trendings", self)
-        self.negative = LabelTable(_load_list(config.data_path, 'negative.txt'), "negative", self.clip_model, self.tokenize, config)
+        self.negative = LabelTable(load_list(config.data_path, 'negative.txt'), "negative", self)
 
         end_time = time.time()
         if not config.quiet:
@@ -183,6 +183,7 @@ class Interrogator():
         return best_prompt
 
     def generate_caption(self, pil_image: Image) -> str:
+        assert self.blip_model is not None, "No BLIP model loaded."
         self._prepare_blip()
 
         size = self.config.blip_image_eval_size
@@ -310,13 +311,14 @@ class Interrogator():
 
 
 class LabelTable():
-    def __init__(self, labels:List[str], desc:str, clip_model, tokenize, config: Config):
+    def __init__(self, labels:List[str], desc:str, ci: Interrogator):
+        clip_model, config = ci.clip_model, ci.config
         self.chunk_size = config.chunk_size
         self.config = config
         self.device = config.device
         self.embeds = []
         self.labels = labels
-        self.tokenize = tokenize
+        self.tokenize = ci.tokenize
 
         hash = hashlib.sha256(",".join(labels).encode()).hexdigest()
         sanitized_name = self.config.clip_model_name.replace('/', '_').replace('@', '_')
@@ -423,11 +425,6 @@ def _download_file(url: str, filepath: str, chunk_size: int = 4*1024*1024, quiet
                 progress.update(len(chunk))
     progress.close()
 
-def _load_list(data_path: str, filename: str) -> List[str]:
-    with open(os.path.join(data_path, filename), 'r', encoding='utf-8', errors='replace') as f:
-        items = [line.strip() for line in f.readlines()]
-    return items
-
 def _merge_tables(tables: List[LabelTable], config: Config) -> LabelTable:
     m = LabelTable([], None, None, None, config)
     for table in tables:
@@ -447,3 +444,11 @@ def _truncate_to_fit(text: str, tokenize) -> str:
             break
         new_text += ', ' + part
     return new_text
+
+def load_list(data_path: str, filename: Optional[str] = None) -> List[str]:
+    """Load a list of strings from a file."""
+    if filename is not None:
+        data_path = os.path.join(data_path, filename)
+    with open(data_path, 'r', encoding='utf-8', errors='replace') as f:
+        items = [line.strip() for line in f.readlines()]
+    return items

setup.py

@@ -5,7 +5,7 @@ from setuptools import setup, find_packages
 
 setup(
     name="clip-interrogator",
-    version="0.5.4",
+    version="0.5.5",
     license='MIT',
     author='pharmapsychotic',
     author_email='me@pharmapsychotic.com',