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Browse files
app.py
CHANGED
@@ -22,34 +22,36 @@ SCROLLABLE_TEXT = COLS[1].container(height=500)
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def classify_full_dataset(shosen_dataset_name, chosen_model_name):
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image_count = 0
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for i in range(len(dataset)):
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image_object = dataset['pasta'][i]["image"]
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SCROLLABLE_TEXT.image(image_object, caption="Uploaded Image", width=300)
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#Image teste load
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image_object = dataset['pasta'][0]["image"]
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SCROLLABLE_TEXT.image(image_object, caption="Uploaded Image", width=300)
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#modle instance
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classifier_pipeline = pipeline('image-classification', model=chosen_model_name)
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classification_result = classifier_pipeline(image_object)
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SCROLLABLE_TEXT.write(classification_result)
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#TODO save classification
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SCROLLABLE_TEXT.write(image_count)
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def main():
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def classify_full_dataset(shosen_dataset_name, chosen_model_name):
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image_count = 0
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#modle instance
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classifier_pipeline = pipeline('image-classification', model=chosen_model_name)
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#dataset
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dataset = load_dataset(shosen_dataset_name,"testedata_readme")
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for i in range(len(dataset)):
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image_object = dataset['pasta'][i]["image"]
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SCROLLABLE_TEXT.image(image_object, caption="Uploaded Image", width=300)
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#classification
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classification_result = classifier_pipeline(image_object)
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SCROLLABLE_TEXT.write(classification_result)
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#TODO save classification result in dataset
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image_count += 1
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SCROLLABLE_TEXT.write(f"Image count" + str(image_count))
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#SCROLLABLE_TEXT.write(image_count)
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#Image teste load
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#image_object = dataset['pasta'][0]["image"]
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#SCROLLABLE_TEXT.image(image_object, caption="Uploaded Image", width=300)
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def main():
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