Nuno-Tome commited on
Commit
73bea8b
1 Parent(s): a22a01f

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Files changed (1) hide show
  1. app.py +23 -11
app.py CHANGED
@@ -13,21 +13,33 @@ DATASETS = [
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  "Nunt/backup_leonardo_2024-02-01"
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  ]
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  MAX_N_LABELS = 5
 
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  #(image_object, classifier_pipeline)
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  #def classify_one_image(classifier_model, dataset_to_classify):
 
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  def classify_one_image(classifier_model, dataset_to_classify):
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- for image in dataset:
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- st("Image classification: ", image['file'])
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- '''
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- image_path = image['file']
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- img = Image.open(image_path)
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- st.image(img, caption="Original image", use_column_width=True)
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- results = classifier(image_path, top_k=MAX_N_LABELS)
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- st.write(results)
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- st.write("----")
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- '''
 
 
 
 
 
 
 
 
 
 
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  return "done"
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@@ -47,7 +59,7 @@ def classify_full_dataset(shosen_dataset_name, chosen_model_name):
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  st.write("### FLAG 4")
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  #classification
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- classification_result = classify_one_image(image_object, classifier_pipeline)
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  st.write(classification_result)
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  st.write("### FLAG 5")
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  #classification_array.append(classification_result)
 
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  "Nunt/backup_leonardo_2024-02-01"
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  ]
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  MAX_N_LABELS = 5
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+ SPLIT_TO_CLASSIFY = 'pasta'
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  #(image_object, classifier_pipeline)
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  #def classify_one_image(classifier_model, dataset_to_classify):
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+ #classify_one_image(image_object, classifier_pipeline)
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  def classify_one_image(classifier_model, dataset_to_classify):
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+
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+
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+ #image_object = dataset[SPLIT_TO_CLASSIFY][i]["image"]
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+ #st.image(image_object, caption="Uploaded Image", width=300)
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+
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+
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+ #for i in range(len(dataset_to_classify)):
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+ #for image in dataset_to_classify:
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+ #image_object = dataset[SPLIT_TO_CLASSIFY][i]["image"]
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+ #st.image(image_object, caption="Uploaded Image", width=300)
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+
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+ #st.write(f"Image classification: ", image['file'])
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+
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+ # image_path = image['file']
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+ # img = Image.open(image_path)
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+ # st.image(img, caption="Original image", use_column_width=True)
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+ # results = classifier(image_path, top_k=MAX_N_LABELS)
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+ # st.write(results)
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+ # st.write("----")
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+
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  return "done"
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  st.write("### FLAG 4")
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  #classification
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+ classification_result = classifier_pipeline(image_object)
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  st.write(classification_result)
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  st.write("### FLAG 5")
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  #classification_array.append(classification_result)