Handle differences in how open_clip does prompt truncation, run_gradio support for all the open_clip models and --share option.

clip_interrogator/clip_interrogator.py

@@ -187,15 +187,13 @@ class Interrogator():
 
         extended_flavors = set(flaves)
         for _ in tqdm(range(max_flavors), desc="Flavor chain", disable=self.config.quiet):
-            try:
-                best = self.rank_top(image_features, [f"{best_prompt}, {f}" for f in extended_flavors])
-                flave = best[len(best_prompt)+2:]
-                if not check(flave):
-                    break
-                extended_flavors.remove(flave)
-            except:
-                # exceeded max prompt length
+            best = self.rank_top(image_features, [f"{best_prompt}, {f}" for f in extended_flavors])
+            flave = best[len(best_prompt)+2:]
+            if not check(flave):
                 break
+            if _prompt_at_max_len(best_prompt, self.tokenize):
+                break
+            extended_flavors.remove(flave)
 
         return best_prompt
 
@@ -306,11 +304,15 @@ def _merge_tables(tables: List[LabelTable], config: Config) -> LabelTable:
         m.embeds.extend(table.embeds)
     return m
 
+def _prompt_at_max_len(text: str, tokenize) -> bool:
+    tokens = tokenize([text])
+    return tokens[0][-1] != 0
+
 def _truncate_to_fit(text: str, tokenize) -> str:
-    while True:
-        try:
-            _ = tokenize([text])
-            return text
-        except:
-            text = ",".join(text.split(",")[:-1])
-        
+    parts = text.split(', ')
+    new_text = parts[0]
+    for part in parts[1:]:
+        if _prompt_at_max_len(new_text + part, tokenize):
+            break
+        new_text += ', ' + part
+    return new_text

run_gradio.py

@@ -1,8 +1,13 @@
 #!/usr/bin/env python3
-import clip
+import argparse
 import gradio as gr
+import open_clip
 from clip_interrogator import Interrogator, Config
 
+parser = argparse.ArgumentParser()
+parser.add_argument('-s', '--share', action='store_true', help='Create a public link')
+args = parser.parse_args()
+
 ci = Interrogator(Config())
 
 def inference(image, mode, clip_model_name, blip_max_length, blip_num_beams):
@@ -19,11 +24,13 @@ def inference(image, mode, clip_model_name, blip_max_length, blip_num_beams):
         return ci.interrogate_classic(image)
     else:
         return ci.interrogate_fast(image)
-    
+
+models = ['/'.join(x) for x in open_clip.list_pretrained()]
+
 inputs = [
     gr.inputs.Image(type='pil'),
     gr.Radio(['best', 'classic', 'fast'], label='Mode', value='best'),
-    gr.Dropdown(clip.available_models(), value='ViT-L/14', label='CLIP Model'),
+    gr.Dropdown(models, value='ViT-H-14/laion2b_s32b_b79k', label='CLIP Model'),
     gr.Number(value=32, label='Caption Max Length'),
     gr.Number(value=64, label='Caption Num Beams'),
 ]
@@ -38,4 +45,5 @@ io = gr.Interface(
     title="🕵️‍♂️ CLIP Interrogator 🕵️‍♂️",
     allow_flagging=False,
 )
-io.launch()
+io.launch(share=args.share)
+