Update app.py

app.py

@@ -1,7 +1,11 @@
+
+
 import gradio as gr
 from PIL import Image
 import clipGPT
 import vitGPT
+import skimage.io as io
+import PIL.Image
 import difflib
 
 
@@ -32,30 +36,38 @@ def generate_caption_vitgpt(image):
 
 
 
-# Sample image paths
-sample_images = [
-    "CXR191_IM-0591-1001.jpg",
-    "CXR191_IM-0598-1001.jpg",
-    "CXR191_IM-0601-1001.jpg",
-    "CXR191_IM-0609-1001.jpg",
-    "CXR191_IM-0618-1001.jpg"
-]
-
-# Gradio interface 
 with gr.Blocks() as demo:
-    with gr.Row():
-        image = gr.Image(label="Upload Chest X-ray", source="upload")
-        sample_image_gallery = gr.ImageGallery(sample_images, label="Sample Images")
-    with gr.Row():  
-        model_choice = gr.Radio(["CLIP-GPT2", "ViT-GPT2", "ViT-CoAttention"], label="Select Model")
-    with gr.Row():
-        caption = gr.Textbox(label="Generated Caption") 
-
+    
     generated_captions = {  
     "CLIP-GPT2": "",
     "ViT-GPT2": "",
     }
 
+    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 = [
+        "CXR191_IM-0591-1001.png",
+        "CXR192_IM-0598-1001.png",
+        "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",
+        # )
+    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") 
+
     def predict(img, model_name):
         if model_name == "CLIP-GPT2":
             return generate_caption_clipgpt(img)
@@ -75,74 +87,11 @@ with gr.Blocks() as demo:
     # Compare captions on button click
     compare_button.click(lambda: compare_and_highlight(
         generated_captions["CLIP-GPT2"], generated_captions["ViT-GPT2"]
-    ), [], comparison_result) 
+        ), [], comparison_result) 
 
-    # Handle changes for both uploaded and sample images
-    gr.Image.change(predict, [image, model_choice], caption)
-    sample_image_gallery.change(predict, [sample_image_gallery, model_choice], caption)
 
-demo.launch() 
-
-
-
-# import gradio as gr
-# from PIL import Image
-# import clipGPT
-# import vitGPT
-# import skimage.io as io
-# import PIL.Image
-
-
-# # Caption generation functions
-# def generate_caption_clipgpt(image):
-#     caption = clipGPT.generate_caption_clipgpt(image)
-#     return caption
+    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
 
-# def generate_caption_vitgpt(image):
-#     caption = vitGPT.generate_caption(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():
-#         sample_images = [
-#         "CXR191_IM-0591-1001.png",
-#         "CXR192_IM-0598-1001.png",
-#         "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",
-#         # )
-#     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") 
-
-#     def predict(img, model_name):
-#         if model_name == "CLIP-GPT2":
-#             return generate_caption_clipgpt(img)  
-#         elif model_name == "ViT-GPT2":
-#             return generate_caption_vitgpt(img)
-#         else:
-#             return "Caption generation for this model is not yet implemented."
-
-#     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() 
 
+demo.launch()