add examples samples

app.py

@@ -1,29 +1,49 @@
 import gradio as gr
 from transformers import pipeline
 from PIL import Image
+import os
+
+def load_images_from_current_directory():
+    images = []
+    current_directory = os.getcwd()
+    for filename in os.listdir(current_directory):
+        if filename.endswith(".jpg") or filename.endswith(".png"):
+            img_path = os.path.join(current_directory, filename)
+            img = Image.open(img_path)
+            if img is not None:
+                images.append(img)
+    return images
+
+# Example: Load images from the current directory
+example_images = load_images_from_current_directory()
 
 # Define the image classification function
 def classify_image(image):
     try:
         # Convert the Gradio image input (which is a NumPy array) to a PIL image
         image = Image.fromarray(image)
-        
+
         # Create the image classification pipeline
         img_class = pipeline(
             "image-classification", model="AMfeta99/vit-base-oxford-brain-tumor"
         )
-        
+
         # Perform image classification
         results = img_class(image)
-        
+
         # Find the result with the highest score
         max_score_result = max(results, key=lambda x: x['score'])
-        
+
         # Extract the predicted label
         predictions = max_score_result['label']
         
-        return predictions
-    
+        if predictions==1:
+          text_pred='Tumor'
+        else:
+          text_pred='Normal'
+
+        return text_pred
+
     except Exception as e:
         # Handle any errors that occur during classification
         return f"Error: {str(e)}"
@@ -34,7 +54,14 @@ label = gr.Label(num_top_classes=1)
 title = "Brain Tumor X-ray Classification"
 description = "Worried about whether your brain scan is normal or not? Upload your x-ray and the algorithm will give you an expert opinion. Check out [the original algorithm](https://huggingface.co/AMfeta99/vit-base-oxford-brain-tumor) that this demo is based off of."
 article = "<p style='text-align: center'>Image Classification | Demo Model</p>"
-demo = gr.Interface(fn=classify_image, inputs=image, outputs=label, description=description, article=article, title=title)
+
+
+# Prepare examples with loaded images
+examples = []
+for img in example_images:
+    examples.append([np.array(img), os.path.basename(os.path.splitext(img.filename)[0])])
+
+demo = gr.Interface(fn=classify_image, inputs=image, outputs=label, description=description, article=article, title=title, examples=examples)
 
 # Launch the Gradio interface
-demo.launch(share=True)
+demo.launch()