Update README.md

README.md

@@ -19,14 +19,36 @@ I also used this tutorial.[Swin Transformer (tiny-sized model)](https://colab.re
 
 The model can be used to classify JPEG images of CT scans into either cancer positive or
 Cancer negative groups.
+I think it would work okay for any image classification task.
 
 
 
-### Out-of-Scope Use
+## Training Data 
 
-This model is not meant to be used in place of medical diagnosis
+The model was trained on data originally obtained from the National Cancer Institute
+Imaging Data Commons. https://portal.imaging.datacommons.cancer.gov/explore/
+The data set used consisted of about 11,000 images which were transformed CT scans
+some of which contained Cancerous Nodules and some that did not.
 
 
 ## How to Use
-from transformers import pipeline, set_seed
+```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("microsoft/swin-tiny-patch4-window7-224")
+model = AutoModelForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
+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])
+
+```
+
+