develop a preliminary version of experiment1

.gitignore

@@ -0,0 +1,24 @@
+/env
+__pycache__/
+
+/logs
+/outputs
+/.hydra
+/checkpoints
+/wandb
+/models
+/share
+/bin
+/lib
+/lib64
+/include
+pyvenv.cfg
+requirements.txt
+
+*.log
+*.pth
+*.png
+
+
+/lightning_logs
+__pycache__/

app.py

@@ -1,46 +1,191 @@
 import gradio as gr
-import pandas as pd
-import csv
-import os
-from huggingface_hub import HfApi, HfFolder
 import yaml
-from src.utils import load_images, load_words, save_results, load_global_variable
+from src.utils import load_words, save_results, load_global_variable, load_saliencies
+from src.style import css
+import random
 
-# set css style for gallery, specifically for the width of the container
-css = """
-    #gallery {
-        height: 300px;
-    }
-    """
+random_images = [
+    "https://picsum.photos/200",
+    "https://picsum.photos/201",
+    "https://picsum.photos/202",
+    "https://picsum.photos/203",
+    "https://picsum.photos/204",
+    "https://picsum.photos/205",
+    "https://picsum.photos/206",
+    "https://picsum.photos/207",
+    "https://picsum.photos/208",
+    "https://picsum.photos/209",
+    "https://picsum.photos/210",
+    "https://picsum.photos/211",
+    "https://picsum.photos/212",
+    "https://picsum.photos/213",
+    "https://picsum.photos/214",
+]
 
 
 
+def update_img_count(state):
+    count = state
+    print('oooooooo', count)
+    return gr.State(count + 1)
+
 def main():
-    
     config = yaml.safe_load(open("config/config.yaml"))
-    print(config)
-    
+
+    global_var = load_global_variable()
+
+    #images = load_images(global_var)
+    #saliency = load_saliencies(global_var)
+    words = ['grad-cam', 'lime', 'sidu', 'rise']
+    options = ['1', '2', '3', '4']
+
     with gr.Blocks(theme=gr.themes.Glass(), css=css) as demo:
-        title = gr.Markdown(f"# My Gradio App Title")
-        welcome_message = gr.Markdown("# Welcome to the Gradio App!")
-        
-        # Load global variable, images, and words after confirming alert
-        global_var = load_global_variable()
-        images = load_images(global_var)
-        words = load_words(global_var)
-        
-        image_gallery = gr.Gallery(images, columns=10, elem_classes="gallery-container", elem_id="gallery")
-        checkbox_group = gr.CheckboxGroup(words, label="Select 3 words")
-        
-        result_message = gr.Textbox(label="Result")
+        # Main App Components
+        title = gr.Markdown("# Saliency evaluation - experiment 1")
+        user_state = gr.State(0)
+        print('user_state', user_state)
+        #user_counter = gr.Textbox(str(global_var), visible=False)
+        #img_counter = gr.Textbox(str(0), visible=False)
+
+        with gr.Row():
+            gr.Markdown("### Target image")
+            gr.Markdown("### Grad-cam")
+            gr.Markdown("### Lime")
+            gr.Markdown("### Sidu")
+            gr.Markdown("### Rise")
+
+        with gr.Row():
+            # generate random integer value
+            target_img = gr.Image(random_images[random.randint(0, 5)])
+            saliency_gradcam = gr.Image(random_images[random.randint(0, 5)])
+            saliency_lime = gr.Image(random_images[random.randint(0, 5)])
+            saliency_rise = gr.Image(random_images[random.randint(0, 5)])
+            saliency_sidu = gr.Image(random_images[random.randint(0, 5)])
         
+        with gr.Row():
+            dropdown1 = gr.Dropdown(choices=options, label="grad-cam")
+            dropdown2 = gr.Dropdown(choices=options, label="lime")
+            dropdown3 = gr.Dropdown(choices=options, label="sidu")
+            dropdown4 = gr.Dropdown(choices=options, label="rise")
+
+        gr.Markdown("### Image examples of the same class")
+        with gr.Row():
+            # generate random integer value
+            img1 = gr.Image(random_images[random.randint(0, 5)])
+            img2 = gr.Image(random_images[random.randint(0, 5)])
+            img3 = gr.Image(random_images[random.randint(0, 5)])
+            img4 = gr.Image(random_images[random.randint(0, 5)])
+            img5 = gr.Image(random_images[random.randint(0, 5)])
+            img6 = gr.Image(random_images[random.randint(0, 5)])
+            img7 = gr.Image(random_images[random.randint(0, 5)])
+            img8 = gr.Image(random_images[random.randint(0, 5)])
+            img9 = gr.Image(random_images[random.randint(0, 5)])
+            img10 = gr.Image(random_images[random.randint(0, 5)])
+            img11 = gr.Image(random_images[random.randint(0, 5)])
+            img12 = gr.Image(random_images[random.randint(0, 5)])
+            img13 = gr.Image(random_images[random.randint(0, 5)])
+            img14 = gr.Image(random_images[random.randint(0, 5)])
+            img15 = gr.Image(random_images[random.randint(0, 5)])
+            img16 = gr.Image(random_images[random.randint(0, 5)])
+            img17 = gr.Image(random_images[random.randint(0, 5)])
+            img18 = gr.Image(random_images[random.randint(0, 5)])
+
+
         submit_button = gr.Button("Submit")
-        submit_button.click(save_results, inputs=checkbox_group, outputs=result_message)
+        finish_button = gr.Button("Finish", visible=False)
+
+        def update_images(dropdown1, dropdown2, dropdown3, dropdown4, user_state):
+
+            #print('dropdowns', dropdowns)
+            #str_dropdowns = str(dropdowns)
+            # remove the curly braces
+            #dropdowns = str_dropdowns[1:-1]
+            #dropdowns = [r.split(":")[1].strip().replace("'", "") for r in dropdowns.split(",")]
+
+            print('dropdowns', dropdown1, dropdown2, dropdown3, dropdown4)
+
+            rank = [dropdown1,dropdown2,dropdown3,dropdown4]
+            print('rank', rank)
+            # image target and saliency images
+            target_img = gr.Image(random_images[random.randint(0, 5)])
+            saliency_gradcam = gr.Image(random_images[random.randint(0, 5)])
+            saliency_lime = gr.Image(random_images[random.randint(0, 5)])
+            saliency_rise = gr.Image(random_images[random.randint(0, 5)])
+            saliency_sidu = gr.Image(random_images[random.randint(0, 5)])
+
+            # image examples
+            img1 = gr.Image(random_images[random.randint(0, 5)])
+            img2 = gr.Image(random_images[random.randint(0, 5)])
+            img3 = gr.Image(random_images[random.randint(0, 5)])
+            img4 = gr.Image(random_images[random.randint(0, 5)])
+            img5 = gr.Image(random_images[random.randint(0, 5)])
+            img6 = gr.Image(random_images[random.randint(0, 5)])
+            img7 = gr.Image(random_images[random.randint(0, 5)])
+            img8 = gr.Image(random_images[random.randint(0, 5)])
+            img9 = gr.Image(random_images[random.randint(0, 5)])
+            img10 = gr.Image(random_images[random.randint(0, 5)])
+            img11 = gr.Image(random_images[random.randint(0, 5)])
+            img12 = gr.Image(random_images[random.randint(0, 5)])
+            img13 = gr.Image(random_images[random.randint(0, 5)])
+            img14 = gr.Image(random_images[random.randint(0, 5)])
+            img15 = gr.Image(random_images[random.randint(0, 5)])
+            img16 = gr.Image(random_images[random.randint(0, 5)])
+            img17 = gr.Image(random_images[random.randint(0, 5)])
+            img18 = gr.Image(random_images[random.randint(0, 5)]) 
+
+            
+            if not isinstance(user_state, int):
+                if user_state.value == 5:
+                    finish_button.visible = True
+                    submit_button.visible = False
+            else:
+                finish_button.visible = False
+                submit_button.visible = True
+
+
+            return target_img, saliency_gradcam, saliency_lime, saliency_rise, saliency_sidu, img1, img2, img3, img4, img5, img6, img7, img8, img9, img10, img11, img12, img13, img14, img15, img16, img17, img18
         
-        demo.load(lambda: None, inputs=None, outputs=welcome_message)
+        def update_state(state):
+            
+            count = state if isinstance(state, int) else state.value
+            print('\n\ncount', count)
+            return gr.State(count + 1)
 
-    demo.launch()
+        def update_buttons(state):
+            count = state if isinstance(state, int) else state.value
+            finish_button, submit_button = None, None
+            if count == 5:
+                finish_button = gr.Button("Finish", visible=True)
+                submit_button = gr.Button("Submit", visible=False)
+            else:
+                finish_button = gr.Button("Finish", visible=False)
+                submit_button = gr.Button("Submit", visible=True)
+            
+            return submit_button, finish_button
+
+        
+        submit_button.click(
+            update_state,
+            inputs=user_state,
+            outputs=user_state
+        ).then(
+            update_buttons,
+            inputs=user_state,
+            outputs={submit_button, finish_button}
+        ).then(
+            update_images, 
+            inputs=[dropdown1, dropdown2, dropdown3, dropdown4, user_state], 
+            outputs={target_img, saliency_gradcam, saliency_lime, saliency_rise, saliency_sidu, img1, img2, img3, img4, img5, img6, img7, img8, img9, img10, img11, img12, img13, img14, img15, img16, img17, img18},
+        )
+
+        def redirect():
+            pass
+            
+        finish_button.click(redirect, js="window.location = 'https://marcoparola.github.io/saliency-evaluation-app/end'")
+
+        demo.load()
     
+    demo.launch()
 
 if __name__ == "__main__":
-    main()
+    main()

src/style.py

@@ -0,0 +1,20 @@
+css = """
+    #gallery {
+        height: 300px;
+    }
+
+    .gallery-textlabel > * {
+        h2 {
+            font-weight: medium;
+            text-align: center;
+            margin-top: 1px;
+            padding: 0px;
+            font-size: 1em; 
+        }
+        .svelte-i3tvor {
+            display:none;
+            visibility: hidden;
+            font-size: 0.02em;
+        }
+    }
+    """

src/utils.py

@@ -8,11 +8,18 @@ config = yaml.safe_load(open("./config/config.yaml"))
 
 # Function to load global variable from CSV
 def load_global_variable():
+    global global_counter
     if os.path.exists('global_variable.csv'):
         df = pd.read_csv('global_variable.csv')
-        return df['value'][0]
-    else:
-        return 0
+        global_counter = df['value'][0]
+
+    print('global_counter', global_counter)
+    
+    global_counter += 1
+    df = pd.DataFrame({'value': [global_counter]})
+    df.to_csv('global_variable.csv', index=False)
+    return global_counter
+        
 
 def load_images(global_var):
     image_dir = os.path.join(config["data_dir"], config["image_dir"])
@@ -20,23 +27,39 @@ def load_images(global_var):
     images = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if os.path.isfile(os.path.join(image_dir, f))]
     return images
 
+def load_saliencies(global_var):
+    image_dir = os.path.join(config["data_dir"], config["image_dir"])
+    #images = [f"image_{global_var}_{i}.jpg" for i in range(10)]
+    saliencies = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if os.path.isfile(os.path.join(image_dir, f))]
+    # select first 5 saliencies
+    saliencies = saliencies[:5]
+    return saliencies
+
 # Function to load words based on global variable
 def load_words(global_var):
     words = [f"word_{global_var}_{i}" for i in range(10)]
     return words
 
 # Function to save results and increment global variable
-def save_results(results):
+def save_results(dropdowns):
     
     filename = "results.txt"
-    results_str = "\n".join(results)
+    print('ooooooo', global_counter)
+    print(dropdowns)
+    str_dropdowns = str(dropdowns)
+    # remove the curly braces
+    dropdowns = str_dropdowns[1:-1]
+    # split by comma and select the number contained in the string
+    dropdowns = [r.split(":")[1].strip().replace("'", "") for r in dropdowns.split(",")]
+
+    str_dropdowns = "\n".join([str(r) for r in dropdowns])
     with open(filename, 'w') as f:
-        f.write(results_str)
+        f.write(str_dropdowns)
 
     # Upload the file to Hugging Face Hub
     api = HfApi()
     token = os.getenv("HUGGINGFACE_TOKEN")
-    #token = HfFolder.get_token()
+    #   token = HfFolder.get_token()
     
     if not token:
         print("Token not found. Please login to Hugging Face.")