Update README.md

README.md

@@ -6,8 +6,8 @@ tags:
 ---
 ## Model Details
 
-This model is trained on 224X224 Grayscale images which are CT-scans
-that are transformed into JPEG. The model is a finetuned version of 
+This model is trained on 224X224 Grayscale images which were originally CT-scans
+that were transformed into JPG images. The model is a finetuned version of 
 [Swin Transformer (tiny-sized model)](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224).
 I also used this tutorial.[Swin Transformer (tiny-sized model)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb#scrollTo=UX6dwmT7GP91). 
 
@@ -15,7 +15,7 @@ I also used this tutorial.[Swin Transformer (tiny-sized model)](https://colab.re
 
 ## Uses
 
-The model can be used to classify JPEG images of CT scans into either cancer positive or
+The model can be used to classify JPG images of CT scans into either cancer positive or
 Cancer negative groups.
 I think it would work okay for any image classification task.
 
@@ -30,24 +30,7 @@ some of which contained Cancerous Nodules and some that did not.
 
 
 ## How to Use
-```python
-
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-abc= hf_hub_download(repo_id="oohtmeel/swin-tiny-patch4-finetuned-lung-cancer-ct-scans", 
-                filename="_X000a109d-56da-4c3f-8680-55afa04d6ae0.dcm.jpg.jpg")
-image = Image.open(abc)
-processor = AutoImageProcessor.from_pretrained("oohtmeel/swin-tiny-patch4-finetuned-lung-cancer-ct-scans")
-model = AutoModelForImageClassification.from_pretrained("oohtmeel/swin-tiny-patch4-finetuned-lung-cancer-ct-scans")
-inputs = processor(images=image, return_tensors="pt")
-outputs = model(**inputs)
-logits = outputs.logits
-predicted_class_idx = logits.argmax(-1).item()
-print("Predicted class:", model.config.id2label[predicted_class_idx])
-
-```
-
-
-Or you can load a grayscale JPG lung cancer image into the model inference section and it will attempt to identify it.
 
+Upload a grayscale JPG into the model inference section and it will cast a prediction.
+One comes included in this repo. If the image contains an X, it is a negative cancer image.
+If an image name contains a Y it is positive.