NeuronZero
/

WBC-Classifier

Image Classification

Inference Endpoints

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NeuronZero commited on Apr 4

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@@ -51,9 +51,9 @@ import requests
 processor = AutoImageProcessor.from_pretrained("NeuronZero/MRI-Reader")
 model = AutoModelForImageClassification.from_pretrained("NeuronZero/MRI-Reader")
-#dataset URL: https://www.kaggle.com/datasets/paultimothymooney/blood-cells
-image_url = https://storage.googleapis.com/kagglesdsdata/datasets/9232/29380/dataset-master/dataset-master/JPEGImages/BloodImage_00014.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=databundle-worker-v2%40kaggle-161607.iam.gserviceaccount.com%2F20240404%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240404T094650Z&X-Goog-Expires=345600&X-Goog-SignedHeaders=host&X-Goog-Signature=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
 image = Image.open(requests.get(image_url, stream=True).raw)
 inputs = processor(images=image, return_tensors="pt")

 processor = AutoImageProcessor.from_pretrained("NeuronZero/MRI-Reader")
 model = AutoModelForImageClassification.from_pretrained("NeuronZero/MRI-Reader")
+#dataset URL: "https://www.kaggle.com/datasets/paultimothymooney/blood-cells
+image_url = https://storage.googleapis.com/kagglesdsdata/datasets/9232/29380/dataset-master/dataset-master/JPEGImages/BloodImage_00014.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=databundle-worker-v2%40kaggle-161607.iam.gserviceaccount.com%2F20240404%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240404T094650Z&X-Goog-Expires=345600&X-Goog-SignedHeaders=host&X-Goog-Signature=8382f4e4b34038ad9176686e9d1155a757bbc41dcf99ee8cf5b5e049fa2994a9b877de41121c3dcea9b35c67076ddd5b3ff5ec970cbf5ac8f5a3eea1149eb68b8a0e79f91084a8598cefca35be190718b402bd6f581051f436ac771c85d3239834adc933e874fa31a6db696a968676610b6da955abd6145974b535b0509d196f68c8964c3dfb2404a0ad2248d1e80eb60d463e5ea58688820b46e6fc95f6fc3919e327905c2920912b8bda2241f8bcae8c886a66513ec62a8960188387322fbd1162caea76b1ecd04c433be5fbc5cd9f9a46e1a696df3cd0981b7e6243c6e5fd041ec928a080ea2845cdbea85fbfb38ff9024627c8a148c47ae50c4154197cfc"
 image = Image.open(requests.get(image_url, stream=True).raw)
 inputs = processor(images=image, return_tensors="pt")