implement user space, counter system to assign ad id to each user

app.py

@@ -1,67 +1,42 @@
 import gradio as gr
 import yaml
-from src.utils import load_words, save_results, load_global_variable, load_saliencies
-from src.style import css
 import random
+import os
 
-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)
+from src.utils import load_words, save_results, load_image_and_saliency, load_example_images
+from src.style import css
+from src.user import UserID
 
 def main():
     config = yaml.safe_load(open("config/config.yaml"))
-
-    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']
+    class_names = config['dataset'][config['dataset']['name']]['class_names']
+    data_dir = os.path.join(config['dataset']['path'], config['dataset']['name'])
+    id_generator = UserID()
 
     with gr.Blocks(theme=gr.themes.Glass(), css=css) as demo:
         # 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)
+        user_id = gr.State(0)
 
         with gr.Row():
-            gr.Markdown("### Target image")
+            target_img_label = gr.Markdown(f"### Target image: {class_names[user_state.value]}")
             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)])
-        
+            count = user_state if isinstance(user_state, int) else user_state.value
+            images = load_image_and_saliency(count, data_dir)
+            target_img = gr.Image(images[0], elem_classes="main-image")
+            saliency_gradcam = gr.Image(images[1], elem_classes="main-image")
+            saliency_lime = gr.Image(images[2], elem_classes="main-image")
+            saliency_sidu = gr.Image(images[3], elem_classes="main-image")
+            saliency_rise = gr.Image(images[4], elem_classes="main-image")
+            
         with gr.Row():
             dropdown1 = gr.Dropdown(choices=options, label="grad-cam")
             dropdown2 = gr.Dropdown(choices=options, label="lime")
@@ -71,103 +46,98 @@ def main():
         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)])
-
-
+            count = user_state if isinstance(user_state, int) else user_state.value
+            images = load_example_images(count, data_dir)
+            img1 = gr.Image(images[0])
+            img2 = gr.Image(images[1])
+            img3 = gr.Image(images[2])
+            img4 = gr.Image(images[3])
+            img5 = gr.Image(images[4])
+            img6 = gr.Image(images[5])
+            img7 = gr.Image(images[6])
+            img8 = gr.Image(images[7])
+            img9 = gr.Image(images[8])
+            img10 = gr.Image(images[9])
+            img11 = gr.Image(images[10])
+            img12 = gr.Image(images[11])
+            img13 = gr.Image(images[12])
+            img14 = gr.Image(images[13])
+            img15 = gr.Image(images[14])
+            img16 = gr.Image(images[15])
+            
         submit_button = gr.Button("Submit")
         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
+            # image target and saliency images
+            count = user_state if isinstance(user_state, int) else user_state.value
+            print(count, config['dataset'][config['dataset']['name']]['n_classes'])
+            if count < config['dataset'][config['dataset']['name']]['n_classes']:
+                images = load_image_and_saliency(count, data_dir)
+                target_img = gr.Image(images[0], elem_classes="main-image")
+                saliency_gradcam = gr.Image(images[1], elem_classes="main-image")
+                saliency_lime = gr.Image(images[2], elem_classes="main-image")
+                saliency_sidu = gr.Image(images[3], elem_classes="main-image")
+                saliency_rise = gr.Image(images[4], elem_classes="main-image")
+
+                # image examples
+                images = load_example_images(count, data_dir)
+                img1 = gr.Image(images[0])
+                img2 = gr.Image(images[1])
+                img3 = gr.Image(images[2])
+                img4 = gr.Image(images[3])
+                img5 = gr.Image(images[4])
+                img6 = gr.Image(images[5])
+                img7 = gr.Image(images[6])
+                img8 = gr.Image(images[7])
+                img9 = gr.Image(images[8])
+                img10 = gr.Image(images[9])
+                img11 = gr.Image(images[10])
+                img12 = gr.Image(images[11])
+                img13 = gr.Image(images[12])
+                img14 = gr.Image(images[13])
+                img15 = gr.Image(images[14])
+                img16 = gr.Image(images[15])
+                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
             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
 
-
-            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
-        
         def update_state(state):
-            
             count = state if isinstance(state, int) else state.value
-            print('\n\ncount', count)
             return gr.State(count + 1)
 
+        def update_img_label(state):
+            count = state if isinstance(state, int) else state.value
+            return f"### Target image: {class_names[count]}"
+
         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)
-            
+            max_images = config['dataset'][config['dataset']['name']]['n_classes']
+            finish_button = gr.Button("Finish", visible=(count == max_images-1))
+            submit_button = gr.Button("Submit", visible=(count != max_images-1))
             return submit_button, finish_button
 
+        def update_dropdowns(dropdowns):
+            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")
+            return dropdown1, dropdown2, dropdown3, dropdown4
         
         submit_button.click(
             update_state,
             inputs=user_state,
             outputs=user_state
+        ).then(
+            update_img_label,
+            inputs=user_state,
+            outputs=target_img_label
         ).then(
             update_buttons,
             inputs=user_state,
@@ -175,7 +145,11 @@ def main():
         ).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},
+            outputs={target_img, saliency_gradcam, saliency_lime, saliency_sidu, saliency_rise, img1, img2, img3, img4, img5, img6, img7, img8, img9, img10, img11, img12, img13, img14, img15, img16},
+        ).then(
+            update_dropdowns,
+            inputs={dropdown1, dropdown2, dropdown3, dropdown4},
+            outputs={dropdown1, dropdown2, dropdown3, dropdown4}
         )
 
         def redirect():
@@ -183,7 +157,11 @@ def main():
             
         finish_button.click(redirect, js="window.location = 'https://marcoparola.github.io/saliency-evaluation-app/end'")
 
-        demo.load()
+        def init(request: gr.Request):
+            user_id.value = id_generator.increment()
+            return user_id
+
+        demo.load(init, inputs=None, outputs=user_id)
     
     demo.launch()
 

config/config.yaml

@@ -2,12 +2,25 @@ data_dir: data
 image_dir: images
 saliency_dir: saliency
 
+gui:
+  max_img_examples: 16
+
 repo_id: "MarcoParola/saliency-evaluation"
 
 dataset: 
-  intel-image:
+  name: intel_image
+  path: data
+  intel_image:
     n_classes: 6
-    class_names: ['building', 'forest', 'glacier', 'mountain', 'sea', 'street']
+    class_names: ['BUILDING', 'FOREST', 'GLACIER', 'MOUNTAIN', 'SEA', 'STREET']
   imagenette:
     n_classes: 10
-    class_names: ['tench', 'English springer', 'cassette player', 'chain saw', 'church', 'French horn', 'garbage truck', 'gas pump', 'golf ball', 'parachute']
+    class_names: ['tench', 'English springer', 'cassette player', 'chain saw', 'church', 'French horn', 'garbage truck', 'gas pump', 'golf ball', 'parachute']
+
+ 
+  
+saliency_methods:
+  - gradcam
+  - lime
+  - sidu
+  - rise

src/style.py

@@ -3,6 +3,12 @@ css = """
         height: 300px;
     }
 
+    .main-image {
+        width: 200px;
+        height: 200px;
+        object-fit: cover;
+    }
+
     .gallery-textlabel > * {
         h2 {
             font-weight: medium;

src/user.py

@@ -0,0 +1,18 @@
+import os
+import pandas as pd
+from threading import Lock
+
+class UserID:
+    def __init__(self): 
+        self.lock = Lock()  
+        self.counter = 0
+        if os.path.exists('global_variable.csv'):
+            df = pd.read_csv('global_variable.csv')
+            self.counter = df['value'][0]
+        
+    def increment(self):
+        with self.lock:
+            self.counter += 1
+            df = pd.DataFrame({'value': [self.counter]})
+            df.to_csv('global_variable.csv', index=False)
+            return self.counter

src/utils.py

@@ -2,42 +2,35 @@ import os
 import pandas as pd
 from huggingface_hub import HfApi, HfFolder
 import yaml
-
+import numpy as np
 
 config = yaml.safe_load(open("./config/config.yaml")) 
 
-# Function to load global variable from CSV
-def load_global_variable():
-    global_counter = 0
-    if os.path.exists('global_variable.csv'):
-        df = pd.read_csv('global_variable.csv')
-        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_image_and_saliency(class_idx, data_dir):
+    path = os.path.join(data_dir, 'images', str(class_idx))
+    images = os.listdir(path)
+    # pick a random image
+    id = np.random.randint(0, len(images))
+    image = os.path.join(path, images[id])
+    gradcam_image = os.path.join(data_dir, 'saliency', 'gradcam', images[id])
+    lime_image = os.path.join(data_dir, 'saliency', 'lime', images[id])
+    sidu_image = os.path.join(data_dir, 'saliency', 'sidu', images[id])
+    rise_image = os.path.join(data_dir, 'saliency', 'rise', images[id])
+    return image, gradcam_image, lime_image, sidu_image, rise_image
 
-def load_images(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)]
-    images = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if os.path.isfile(os.path.join(image_dir, f))]
+def load_example_images(class_idx, data_dir, max_images=16):
+    path = os.path.join(data_dir, 'images', str(class_idx))
+    images = os.listdir(path)
+    # pick max_images random images
+    ids = np.random.choice(len(images), max_images, replace=False)
+    images = [os.path.join(path, images[id]) for id in ids]
     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)]
+def load_words(idx):
+    words = [f"word_{idx}_{i}" for i in range(20)]
     return words
 
 # Function to save results and increment global variable