Update app.py

app.py

@@ -5,26 +5,9 @@ import vitGPT
 import skimage.io as io
 import PIL.Image
 import difflib
+import tester
 
 
-def compare_and_highlight(text1, text2):
-    print("Triggered function")
-
-    matcher = difflib.SequenceMatcher(None, text1, text2)
-    output = ''
-    for op, a1, a2, b1, b2 in matcher.get_opcodes():
-        if op == 'equal':
-            output += f"**{text1[a1:a2]}**"  # Highlight matches in bold
-        elif op == 'insert':
-            output += f"<ins>{text2[b1:b2]}</ins>" 
-        elif op == 'delete':
-            output += f"<del>{text1[a1:a2]}</del>"
-        elif op == 'replace':  
-            # Handle replacements (more complex)
-            output += f"<del>{text1[a1:a2]}</del> <ins>{text2[b1:b2]}</ins>" 
-    print(output)
-    return output
-
 
 # Caption generation functions
 def generate_caption_clipgpt(image):
@@ -35,6 +18,9 @@ def generate_caption_vitgpt(image):
     caption = vitGPT.generate_caption(image)
     return caption
 
+def generate_caption_vitCoAtt(image):
+    caption = tester.main(image)
+    return caption
 
 
 with gr.Blocks() as demo:
@@ -50,19 +36,12 @@ with gr.Blocks() as demo:
         "CXR193_IM-0601-1001.png",
         "CXR194_IM-0609-1001.png",
         "CXR195_IM-0618-1001.png"
-    ]
+            ]
 
         
         image = gr.Image(label="Upload Chest X-ray")    
-        gr.Gallery(
-        value = sample_images,
-        label="Sample Images",
-        )
         
-    # sample_images_gallery = gr.Gallery(
-    #     value = sample_images,
-    #     label="Sample Images",
-    # )
+        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")
@@ -75,23 +54,15 @@ with gr.Blocks() as demo:
             return generate_caption_clipgpt(img)
         elif model_name == "ViT-GPT2":
             return generate_caption_vitgpt(img)
+        elif model_name == "ViT-CoAttention":
+            return generate_caption_vitCoAtt(img)
         else:
             return "Caption generation for this model is not yet implemented."     
 
-    with gr.Row():
-        text1 = gr.Textbox(label="Text 1")
-        text2 = gr.Textbox(label="Text 2")
-        compare_button = gr.Button("Compare Texts")
-    with gr.Row():
-        comparison_result = gr.Textbox(label="Comparison Result")
 
     # Event handlers
- 
     generate_button.click(predict, [image, model_choice], caption)  # Trigger prediction on button click 
-    compare_button.click(lambda: compare_and_highlight(text1.value, text2.value), [], comparison_result) 
-
-
-    # sample_images_gallery.change(predict, [sample_images_gallery, model_choice], caption)  # Handle sample images
+    sample_images_gallery.change(predict, [sample_images_gallery, model_choice], caption)  # Handle sample images
 
 
 demo.launch()