Update app.py

app.py

@@ -1,5 +1,3 @@
-
-
 import gradio as gr
 from PIL import Image
 import clipGPT
@@ -10,12 +8,7 @@ import difflib
 import ViTCoAtt
 from build_vocab import Vocabulary
 
-def render_image(image_path_or_url):  
-    img = Image.open(io.imread(image_path_or_url))
-    img = img.resize((80, 80))  # Adjust size as needed
-    buf = io.BytesIO()
-    img.save(buf, format='JPEG')
-    return buf.getvalue()
+
 
 # Caption generation functions
 def generate_caption_clipgpt(image):
@@ -37,14 +30,29 @@ with gr.Blocks() as demo:
     gr.HTML("<h1 style='text-align: center;'>MedViT: A Vision Transformer-Driven Method for Generating Medical Reports 🏥🤖</h1>")
     gr.HTML("<p style='text-align: center;'>You can generate captions by uploading an X-Ray and selecting a model of your choice below</p>")
 
+       
+    with gr.Row():
+        sample_images = [
+        'https://imgur.com/W1pIr9b',
+        'https://imgur.com/MLJaWnf',
+        'https://imgur.com/6XymFW1',
+        'https://imgur.com/zdPjZZ1',
+        'https://imgur.com/DKUlZbF'
+            ]
+
+        
+        image = gr.Image(label="Upload Chest X-ray", type="pil")    
+        
+        sample_images_gallery = gr.Gallery(value = sample_images,label="Sample Images")
 
     with gr.Row():
         model_choice = gr.Radio(["CLIP-GPT2", "ViT-GPT2", "ViT-CoAttention"], label="Select Model")
 
         generate_button = gr.Button("Generate Caption") 
     
-    caption = gr.Textbox(label="Generated Caption") 
+    
 
+    caption = gr.Textbox(label="Generated Caption") 
 
     def predict(img, model_name):
         if model_name == "CLIP-GPT2":
@@ -59,6 +67,7 @@ with gr.Blocks() as demo:
 
     # Event handlers
     generate_button.click(predict, [image, model_choice], caption)  # Trigger prediction on button click 
+    sample_images_gallery.change(predict, [sample_images_gallery, model_choice], caption)  # Handle sample images
 
 
 demo.launch()