Update app.py

app.py

@@ -57,25 +57,6 @@ LABELS_MAP = ["Bat (baseball)", "Bat (mammal)",
 
 """
 
-# model_key  = "CLIP-large"
-
-# Load model (cache for speed)
-if model_key not in MODEL_CACHE:
-  MODEL_CACHE[model_key] = pipeline(task  = "zero-shot-image-classification",
-                      model = MODEL_OPTIONS[model_key])
-classifier = MODEL_CACHE[model_key]
-
-BASE_DIR = '/content/drive/MyDrive/ML Projects/Zero-shot Image Classification/Images'
-image_path = os.path.join(BASE_DIR, 'Mouse1_2.png')
-
-output = classifier(
-    image               = image_path,
-    candidate_labels    = CANDIDATE_LABELS,
-    hypothesis_template = "This image shows {}")
-
-# print("\n\n=============================================================================")
-# print(f"\nPrediction: This image shows {output[0]["label"]} | Confidence (probability): {100*output[0]["score"]: .1f}%")
-
 def run_classifer(model_key, image_path, prob_threshold = None):
     # model_key: name of backbone zero-shot-image-classification model to use
     # image_path: path to test image
@@ -107,16 +88,6 @@ def run_classifer(model_key, image_path, prob_threshold = None):
 
     return predicted_label_str, prob_dict
 
-# # example run
-# model_key  = "CLIP-large"
-# BASE_DIR   = '/content/drive/MyDrive/ML Projects/Zero-shot Image Classification/Images'
-# image_path = os.path.join(BASE_DIR, 'Nail2_1.png')
-
-# predicted_label_str, prob_dict = run_classifer(model_key, image_path, prob_threshold = 0.4)
-# print("\n\n=============================================================================")
-# # print(f"\nPrediction: {predicted_label_str} | Confidence (probability): {100*output[0]['score']:.1f}%")
-# print(f"\nPrediction: {predicted_label_str}")
-
 """### Gradio App
 
 ---