Update app.py

app.py

@@ -24,53 +24,59 @@ def generate_caption_vitCoAtt(image):
   return caption
 
 
-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():
+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():
          
-        image = gr.Image(label="Upload Chest X-ray", type="pil")   
+    image = gr.Image(label="Upload Chest X-ray", type="pil")   
          
-        sample_images_gallery = gr.Gallery(value = [
-        "https://imgur.com/W1pIr9b",
-        "https://imgur.com/MLJaWnf",
-        "https://imgur.com/6XymFW1",
-        "https://imgur.com/zdPjZZ1",
-        "https://imgur.com/DKUlZbF"], label="Sample Images", columns = 5)
-        
+           
     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
-            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") 
-        
-    
+with gr.Row():
+ 
+    with gr.Column(): # Column for dropdowns and model choice
+        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") 
     
-    caption = gr.Textbox(label="Generated Caption") 
     
-    def predict(img, model_name, max_tokens, temperature):
-        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."   
-        
+caption = gr.Textbox(label="Generated Caption") 
     
-    # Event handlers 
-    generate_button.click(predict, [image, model_choice, max_tokens, temperature], caption) 
-    sample_images_gallery.select(predict, [sample_images_gallery, model_choice, max_tokens, temperature], caption)  
+def predict(img, model_name, max_tokens, temperature):
+    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."   
+
+
+sample_images = [
+        'https://imgur.com/W1pIr9b',
+        'https://imgur.com/MLJaWnf',
+        'https://imgur.com/6XymFW1',
+        'https://imgur.com/zdPjZZ1',
+        'https://imgur.com/DKUlZbF'
+            ]
+
+# examples = [f"example{i}.jpg" for i in range(1,7)]
+
+interface = gr.Interface(
+        fn=predict,
+        inputs = [image, model_choice, max_tokens, temperature],
+        theme="gradio/monochrome"",
+        outputs=caption,
+        examples = sample_images,
+    )
+
+interface.launch(debug=True)
     
 
-demo.launch()