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app.py
CHANGED
@@ -13,21 +13,33 @@ DATASETS = [
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"Nunt/backup_leonardo_2024-02-01"
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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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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 =
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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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#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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#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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#st.write(f"Image classification: ", image['file'])
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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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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)
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