Update app.py

app.py

@@ -29,38 +29,31 @@ 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>")
 
+    sample_data = [
+        {'image': 'https://imgur.com/W1pIr9b', 'max token, temp': '75, 0.7', 'model supported': 'CLIP-GPT2, ViT-GPT2, ViT-CoAttention', 'ground truth': '...'},
+        {'image': 'https://imgur.com/MLJaWnf', 'max token, temp': '50, 0.8', 'model supported': 'CLIP-GPT2, ViT-CoAttention', 'ground truth': '...'},
+    ]
 
     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")
+        image = gr.Image(label="Upload Chest X-ray", type="pil")  
+        image_table = gr.Dataframe(sample_data, headers=['image', 'max token, temp', 'model supported', 'ground truth'], datatype=['picture', 'str', 'str', 'str'])
 
     gr.HTML("<p style='text-align: center;'> Please select the Number of Max Tokens and Temperature setting, if you are testing CLIP GPT2 and VIT GPT2 Models</p>")
-        
-            
+    
     with gr.Row():
-        
-        with gr.Column():  # Column for dropdowns and model choice
+        with gr.Column(): 
             max_tokens = gr.Dropdown(list(range(50, 101)), label="Max Tokens", value=75)
             temperature = gr.Slider(0.5, 0.9, step=0.1, label="Temperature", value=0.7) 
-            
-        model_choice = gr.Radio(["CLIP-GPT2", "ViT-GPT2", "ViT-CoAttention"], label="Select Model")
-
-        generate_button = gr.Button("Generate Caption") 
-    
+        
+        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") 
 
+
+    gr.HTML("<p style='text-align: center;'> Please select the Number of Max Tokens and Temperature setting, if you are testing CLIP GPT2 and VIT GPT2 Models</p>")
+        
+
     def predict(img,  model_name, max_tokens, temperature):
         if model_name == "CLIP-GPT2":
             return generate_caption_clipgpt(img, max_tokens, temperature)
@@ -69,12 +62,25 @@ with gr.Blocks() as demo:
         elif model_name == "ViT-CoAttention":
             return generate_caption_vitCoAtt(img)
         else:
-            return "Caption generation for this model is not yet implemented."     
+            return "Caption generation for this model is not yet implemented."  
 
+    def predict_from_table(row, model_name):  
+        img_url = row['image']
+        img = Image.open(io.imread(img_url))  
+        if model_name == "CLIP-GPT2":
+            return generate_caption_clipgpt(img, max_tokens, temperature)
+        elif model_name == "ViT-GPT2":
+            return generate_caption_vitgpt(img, max_tokens, temperature)
+        elif model_name == "ViT-CoAttention":
+            return generate_caption_vitCoAtt(img)
+        else:
+            return "Caption generation for this model is not yet implemented."  
 
+        
     # Event handlers 
     generate_button.click(predict, [image, model_choice, max_tokens, temperature], caption) 
-    sample_images_gallery.change(predict, [sample_images_gallery, model_choice, max_tokens, temperature], caption)  
+    image_table.click(predict_from_table, [image_table, model_choice], caption)  
+
 
 
 demo.launch()