Update README.md

README.md

@@ -41,7 +41,7 @@ For the SDO team: this model is the first version for demonstration purposes. It
 We will include more technical details here soon. 
 
 ## Example Images
---> Use one of the images below for the inference API field on the upper right.
+>>> Use one of the images below for the inference API field on the upper right.
 
 #### High_Energy_Ion_Fe_Nuclei
 
@@ -58,26 +58,75 @@ The ViT model was pretrained on a dataset consisting of 14 million images and 21
 More information on the base model used can be found here: (https://huggingface.co/google/vit-base-patch16-224-in21k)
 
 ## How to use this Model 
-(quick snippet to work on Google Colab - comment the pip install for local use if you have transformers already installed)
+(quick snippets to work on Google Colab)
 
+First a snippet to downnload test images from an online repository:
 ```python
-!pip install transformers --quiet
+import urllib.request
+
+def download_image(url, filename):
+    try:
+        # Define custom headers
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'
+        }
+        
+        # Create a request with custom headers
+        req = urllib.request.Request(url, headers=headers)
+        
+        # Open the URL and read the content
+        with urllib.request.urlopen(req) as response:
+            img_data = response.read()
+            
+        # Write the content to a file
+        with open(filename, 'wb') as handler:
+            handler.write(img_data)
+        
+        print(f"Image '{filename}' downloaded successfully")
+    except Exception as e:
+        print(f"Error downloading the image '{filename}':", e)
+
+# List of URLs and corresponding filenames
+urls = [
+    ('https://roosevelt.devron-systems.com/HF/P242_73665006707-A6_002_008_proj.tif', 'P242_73665006707-A6_002_008_proj.tif'),
+    ('https://roosevelt.devron-systems.com/HF/P278_73668090728-A7_003_027_proj.tif', 'P278_73668090728-A7_003_027_proj.tif')
+]
+
+# Download each image
+for url, filename in urls:
+    download_image(url, filename)
+```
+
+Then use the images for inference:
+
+```python
+#!pip install transformers --quiet # uncomment this pip install for local use if you do not have transformers installed
 from transformers import AutoFeatureExtractor, AutoModelForImageClassification
 from PIL import Image
-import requests
 
-url = 'https://roosevelt.devron-systems.com/HF/P242_73665006707-A6_002_008_proj.tif'
-image = Image.open(requests.get(url, stream=True).raw)
+# Load the image
+#image = Image.open('P242_73665006707-A6_002_008_proj.tif') #First Image
+image = Image.open('P278_73668090728-A7_003_027_proj.tif')  #Second Image
 
+# Convert grayscale image to RGB
+image_rgb = image.convert("RGB")
+
+# Load the pre-trained feature extractor and classification model
 feature_extractor = AutoFeatureExtractor.from_pretrained("kenobi/NASA_GeneLab_MBT")
 model = AutoModelForImageClassification.from_pretrained("kenobi/NASA_GeneLab_MBT")
-inputs = feature_extractor(images=image, return_tensors="pt")
 
+# Extract features from the image
+inputs = feature_extractor(images=image_rgb, return_tensors="pt")
+
+# Perform classification
 outputs = model(**inputs)
 logits = outputs.logits
-# model predicts one of the two fine-tuned classes (High_Energy_Ion_Fe_Nuclei or XRay_irradiated_Nuclei) 
+
+# Obtain the predicted class index and label
 predicted_class_idx = logits.argmax(-1).item()
-print("Predicted class:", model.config.id2label[predicted_class_idx])
+predicted_class_label = model.config.id2label[predicted_class_idx]
+
+print("Predicted class:", predicted_class_label)
 ```
 
 ## BibTeX & References