Merge branch 'master' of https://git.squad51.us/jamie/ucsinfer

ucsinfer/__main__.py

@@ -2,6 +2,8 @@ import os
 import sys
 import csv
 
+from typing import Optional
+
 from sentence_transformers import SentenceTransformer
 import tqdm
 import click
@@ -11,19 +13,60 @@ from .inference import InferenceContext, load_ucs
 from .util import ffmpeg_description, parse_ucs
 
 
-@click.group()
+def recommend_text(text: str, ctx: InferenceContext):
+    return None
+
+@click.group(epilog="For more information see "
+             "<https://git.squad51.us/jamie/ucsinfer>")
 # @click.option('--verbose', flag_value='verbose', help='Verbose output')
 def ucsinfer():
+    """
+    Tools for applying UCS categories to sounds using large-language Models 
+
+    """
     pass
 
 
 @ucsinfer.command('recommend')
-def recommend():
+@click.option('--text', type=Optional[str], default=None,
+              help="Recommend a category for given text instead of reading "
+              "from a file")
+@click.argument('files', nargs=-1)
+@click.option('--model', type=str, metavar="<model-name>", 
+              default="paraphrase-multilingual-mpnet-base-v2",
+              show_default=True, 
+              help="Select the sentence_transformer model to use")
+def recommend(text, paths, model):
     """
     Infer a UCS category for a text description
 
     """
-    pass
+    m = SentenceTransformer(model)
+    ctx = InferenceContext(m, model)
+
+    recommendations = []
+    if text is not None:
+        recommendations.append({
+            "text": text,
+            "recommendations": recommend_text(text, ctx)
+            })
+    
+    for path in paths:
+        text = ffmpeg_description(path)
+        if text:
+            recommendations.append({
+                "path":path,
+                "text":text,
+                "recommendations":recommend_text(text, ctx)
+                })
+        else:
+            recommendations.append({
+                "path":path, 
+                "text":None,
+                "recommendations":None
+                })
+                    
+
 
 
 @ucsinfer.command('gather')
@@ -77,13 +120,17 @@ def finetune():
 
 
 @ucsinfer.command('evaluate')
-@click.option('--offset', type=int, default=0)
+@click.option('--offset', type=int, default=0, metavar="<int>", 
-@click.option('--limit', type=int, default=-1)
+              help='Skip this many records in the dataset before processing')
+@click.option('--limit', type=int, default=-1, metavar="<int>",
+              help='Process this many records and then exit')
 @click.option('--no-foley', 'no_foley', flag_value=True, default=False, 
               help="Ignore any data in the set with FOLYProp or FOLYFeet "
               "category")
-@click.option('--model', type=str, 
+@click.option('--model', type=str, metavar="<model-name>", 
-              default="paraphrase-multilingual-mpnet-base-v2")
+              default="paraphrase-multilingual-mpnet-base-v2",
+              show_default=True, 
+              help="Select the sentence_transformer model to use")
 @click.argument('dataset', type=click.File('r', encoding='utf8'),
                 default='dataset.csv')
 def evaluate(dataset, offset, limit, model, no_foley):
@@ -110,8 +157,17 @@ def evaluate(dataset, offset, limit, model, no_foley):
     ctx = InferenceContext(m, model)
     reader = csv.reader(dataset)
 
+    print(f"Evaluating model {model}...")
     results = []
-    for i, row in enumerate(tqdm.tqdm(reader)):
+    
+    if offset > 0:
+        print(f"Skipping {offset} records...")
+
+    if limit > 0:
+        print(f"Will only evaluate {limit} records...")
+
+    progress_bar = tqdm.tqdm(total=limit)
+    for i, row in enumerate(reader):
         if i < offset:
             continue
 
@@ -131,6 +187,8 @@ def evaluate(dataset, offset, limit, model, no_foley):
             results.append({'catid': cat_id, 'result': "TOP_10"})
         else:
             results.append({'catid': cat_id, 'result': "MISS"})
+        
+        progress_bar.update(1)
 
     total = len(results)
     total_top = len([x for x in results if x['result'] == 'TOP'])