update dataset UI

app.py

@@ -63,6 +63,7 @@ DATASETS = {
     'Pose': [
         ('sayakpaul/poses-controlnet-dataset', None),
         ('razdab/sign_pose_M', None),
+        ('Marqo/deepfashion-multimodal', None),
         ('Fiacre/small-animal-poses-controlnet-dataset', None),
         ('junjuice0/vtuber-tachi-e', None),
     ],
@@ -77,11 +78,11 @@ DATASETS = {
         ('efoley/sar_tile_512', None),
     ],
     'Medical': [
-        ('Mahadih534/Chest_CT-Scan_images-Dataset', 4),
-        ('Falah/Alzheimer_MRI', 4),
-        ('sartajbhuvaji/Brain-Tumor-Classification', 4),
+        ('Mahadih534/Chest_CT-Scan_images-Dataset', None),
         ('TrainingDataPro/chest-x-rays', None),
         ('hongrui/mimic_chest_xray_v_1', None),
+        ('sartajbhuvaji/Brain-Tumor-Classification', 4),
+        ('Falah/Alzheimer_MRI', 4),
         ('Leonardo6/path-vqa', None),
         ('Itsunori/path-vqa_jap', None),
         ('ruby-jrl/isic-2024-2', None),
@@ -95,7 +96,6 @@ DATASETS = {
         ('jlbaker361/dcgan-eval-creative_gan_256_256', None),
         ('Francesco/csgo-videogame', None),
         ('Francesco/apex-videogame', None),
-        ('Marqo/deepfashion-multimodal', None),
         ('huggan/pokemon', None),
         ('huggan/few-shot-universe', None),
         ('huggan/flowers-102-categories', None),
@@ -1282,9 +1282,97 @@ def make_input_video_section():
     return input_gallery, submit_button, clear_images_button, max_frames_number
 
 
+def load_dataset_images(is_advanced, dataset_name, num_images=10, 
+                        is_filter=False, filter_by_class_text="0,1,2", 
+                        is_random=False, seed=1):
+    progress = gr.Progress()
+    progress(0, desc="Loading Images")
+    
+    if dataset_name == "EgoExo":
+        is_advanced = "Basic"
+        
+    if is_advanced == "Basic":
+        gr.Info(f"Loaded images from EgoExo")
+        return default_images
+    try:
+        progress(0.5, desc="Downloading Dataset")
+        if 'EgoThink' in dataset_name:
+            dataset = load_dataset(dataset_name, 'Activity', trust_remote_code=True)
+        else:
+            dataset = load_dataset(dataset_name, trust_remote_code=True)
+        key = list(dataset.keys())[0]
+        dataset = dataset[key]
+    except Exception as e:
+        raise gr.Error(f"Error loading dataset {dataset_name}: {e}")
+    if num_images > len(dataset):
+        num_images = len(dataset)
+    
+    if len(filter_by_class_text) == 0:
+        is_filter = False
+    
+    if is_filter:
+        progress(0.8, desc="Filtering Images")
+        classes = [int(i) for i in filter_by_class_text.split(",")]
+        labels = np.array(dataset['label'])
+        unique_labels = np.unique(labels)
+        valid_classes = [i for i in classes if i in unique_labels]
+        invalid_classes = [i for i in classes if i not in unique_labels]
+        if len(invalid_classes) > 0:
+            gr.Warning(f"Classes {invalid_classes} not found in the dataset.")
+        if len(valid_classes) == 0:
+            raise gr.Error(f"Classes {classes} not found in the dataset.")
+        # shuffle each class
+        chunk_size = num_images // len(valid_classes)
+        image_idx = []
+        for i in valid_classes:
+            idx = np.where(labels == i)[0]
+            if is_random:
+                idx = np.random.RandomState(seed).choice(idx, chunk_size, replace=False)
+            else:
+                idx = idx[:chunk_size]
+            image_idx.extend(idx.tolist())
+    if not is_filter:
+        if is_random:
+            image_idx = np.random.RandomState(seed).choice(len(dataset), num_images, replace=False).tolist()
+        else:
+            image_idx = list(range(num_images))
+    key = 'image' if 'image' in dataset[0] else list(dataset[0].keys())[0]
+    images = [dataset[i][key] for i in image_idx]
+    gr.Info(f"Loaded {len(images)} images from {dataset_name}")
+    del dataset
+    
+    if dataset_name in CENTER_CROP_DATASETS:
+        def center_crop_image(img):
+            # image: PIL image
+            w, h = img.size
+            min_hw = min(h, w)
+            # center crop
+            left = (w - min_hw) // 2
+            top = (h - min_hw) // 2
+            right = left + min_hw
+            bottom = top + min_hw
+            img = img.crop((left, top, right, bottom))
+            return img
+        images = [center_crop_image(image) for image in images]
+    
+    return images 
+
+def load_and_append(existing_images, *args, **kwargs):
+    new_images = load_dataset_images(*args, **kwargs)
+    if new_images is None:
+        return existing_images
+    if len(new_images) == 0:
+        return existing_images
+    if existing_images is None:
+        existing_images = []
+    existing_images += new_images
+    gr.Info(f"Total images: {len(existing_images)}")
+    return existing_images
+
 def make_input_images_section(rows=1, cols=3, height="auto", advanced=False, is_random=False, allow_download=False):
     gr.Markdown('### Input Images')
-    input_gallery = gr.Gallery(value=None, label="Input images", show_label=True, elem_id="input_images", columns=[cols], rows=[rows], object_fit="contain", height=height, type="pil", show_share_button=False)
+    input_gallery = gr.Gallery(value=None, label="Input images", show_label=True, elem_id="input_images", columns=[cols], rows=[rows], object_fit="contain", height=height, type="pil", show_share_button=False,
+                               format="webp")
     
     submit_button = gr.Button("🔴 RUN", elem_id="submit_button", variant='primary')
     with gr.Row():
@@ -1311,11 +1399,30 @@ def make_input_images_section(rows=1, cols=3, height="auto", advanced=False, is_
         create_file_button, download_button = add_download_button(input_gallery, "input_images")
     
     gr.Markdown('### Load Datasets')
-    load_images_button = gr.Button("🔴 Load Images", elem_id="load-images-button", variant='primary')
-    advanced_radio = gr.Radio(["Basic", "Advanced"], label="Datasets", value="Advanced" if advanced else "Basic", elem_id="advanced-radio", show_label=True)
+    advanced_radio = gr.Radio(["Basic", "Advanced"], label="Datasets Menu", value="Advanced" if advanced else "Basic", elem_id="advanced-radio", show_label=True)
     with gr.Column() as basic_block:
-        example_gallery = gr.Gallery(value=example_items, label="Example Images", show_label=True, columns=[3], rows=[2], object_fit="scale-down", height="200px", show_share_button=False, elem_id="example-gallery")
+        # gr.Markdown('### Example Image Sets')
+        def make_example(name, images, dataset_name):
+            with gr.Row():
+                button = gr.Button("Load\n"+name, elem_id=f"example-{name}", elem_classes="small-button", variant='secondary', size="sm", scale=1, min_width=60)
+                gallery = gr.Gallery(value=images, label=name, show_label=True, columns=[3], rows=[1], interactive=False, height=80, scale=8, object_fit="cover", min_width=140)
+            button.click(fn=lambda: gr.update(value=load_dataset_images(True, dataset_name, 100, is_random=True, seed=42)), outputs=[input_gallery])
+            return gallery, button
+        example_items = [
+            ("EgoExo", ['./images/egoexo1.jpg', './images/egoexo3.jpg', './images/egoexo2.jpg'], "EgoExo"),
+            ("Ego", ['./images/egothink1.jpg', './images/egothink2.jpg', './images/egothink3.jpg'], "EgoThink/EgoThink"),
+            ("Face", ['./images/face1.jpg', './images/face2.jpg', './images/face3.jpg'], "nielsr/CelebA-faces"),
+            ("Pose", ['./images/pose1.jpg', './images/pose2.jpg', './images/pose3.jpg'], "sayakpaul/poses-controlnet-dataset"),
+            # ("CatDog", ['./images/catdog1.jpg', './images/catdog2.jpg', './images/catdog3.jpg'], "microsoft/cats_vs_dogs"),
+            # ("Bird", ['./images/bird1.jpg', './images/bird2.jpg', './images/bird3.jpg'], "Multimodal-Fatima/CUB_train"),
+            # ("ChestXray", ['./images/chestxray1.jpg', './images/chestxray2.jpg', './images/chestxray3.jpg'], "hongrui/mimic_chest_xray_v_1"),
+            ("BrainMRI", ['./images/brain1.jpg', './images/brain2.jpg', './images/brain3.jpg'], "sartajbhuvaji/Brain-Tumor-Classification"),
+            ("Kanji", ['./images/kanji1.jpg', './images/kanji2.jpg', './images/kanji3.jpg'], "yashvoladoddi37/kanjienglish"),
+        ]
+        for name, images, dataset_name in example_items:
+            make_example(name, images, dataset_name)
     with gr.Column() as advanced_block:
+        load_images_button = gr.Button("🔴 Load Images", elem_id="load-images-button", variant='primary')
         # dataset_names = DATASET_NAMES
         # dataset_classes = DATASET_CLASSES
         dataset_categories = list(DATASETS.keys())
@@ -1344,7 +1451,7 @@ def make_input_images_section(rows=1, cols=3, height="auto", advanced=False, is_
                 random_seed_slider = gr.Slider(0, 1000, step=1, label="Random seed", value=42, elem_id="random_seed", visible=True)
         
         # add functionality, save and load images to profile
-        with gr.Accordion("Saved Image Profiles", open=True) as profile_accordion:
+        with gr.Accordion("Saved Image Profiles", open=False) as profile_accordion:
             with gr.Row():
                 profile_text = gr.Textbox(label="Profile name", placeholder="Type here: Profile name to save/load/delete", elem_id="profile-name", scale=6, show_label=False)
                 list_profiles_button = gr.Button("📋 List", elem_id="list-profile-button", variant='secondary', scale=3)
@@ -1445,87 +1552,7 @@ def make_input_images_section(rows=1, cols=3, height="auto", advanced=False, is_
         return gr.Slider(0, 1000, step=1, label="Random seed", value=1, elem_id="random_seed", visible=is_random)
     is_random_checkbox.change(fn=change_random_seed, inputs=is_random_checkbox, outputs=random_seed_slider)
     
-        
-    def load_dataset_images(is_advanced, dataset_name, num_images=10, 
-                            is_filter=True, filter_by_class_text="0,1,2", 
-                            is_random=False, seed=1):
-        progress = gr.Progress()
-        progress(0, desc="Loading Images")
-        if is_advanced == "Basic":
-            gr.Info("Loaded images from Ego-Exo4D")
-            return default_images
-        try:
-            progress(0.5, desc="Downloading Dataset")
-            if 'EgoThink' in dataset_name:
-                dataset = load_dataset(dataset_name, 'Activity', trust_remote_code=True)
-            else:
-                dataset = load_dataset(dataset_name, trust_remote_code=True)
-            key = list(dataset.keys())[0]
-            dataset = dataset[key]
-        except Exception as e:
-            raise gr.Error(f"Error loading dataset {dataset_name}: {e}")
-        if num_images > len(dataset):
-            num_images = len(dataset)
-        
-        if is_filter:
-            progress(0.8, desc="Filtering Images")
-            classes = [int(i) for i in filter_by_class_text.split(",")]
-            labels = np.array(dataset['label'])
-            unique_labels = np.unique(labels)
-            valid_classes = [i for i in classes if i in unique_labels]
-            invalid_classes = [i for i in classes if i not in unique_labels]
-            if len(invalid_classes) > 0:
-                gr.Warning(f"Classes {invalid_classes} not found in the dataset.")
-            if len(valid_classes) == 0:
-                gr.Error(f"Classes {classes} not found in the dataset.")
-                return None
-            # shuffle each class
-            chunk_size = num_images // len(valid_classes)
-            image_idx = []
-            for i in valid_classes:
-                idx = np.where(labels == i)[0]
-                if is_random:
-                    idx = np.random.RandomState(seed).choice(idx, chunk_size, replace=False)
-                else:
-                    idx = idx[:chunk_size]
-                image_idx.extend(idx.tolist())
-        if not is_filter:
-            if is_random:
-                image_idx = np.random.RandomState(seed).choice(len(dataset), num_images, replace=False).tolist()
-            else:
-                image_idx = list(range(num_images))
-        key = 'image' if 'image' in dataset[0] else list(dataset[0].keys())[0]
-        images = [dataset[i][key] for i in image_idx]
-        gr.Info(f"Loaded {len(images)} images from {dataset_name}")
-        del dataset
-        
-        if dataset_name in CENTER_CROP_DATASETS:
-            def center_crop_image(img):
-                # image: PIL image
-                w, h = img.size
-                min_hw = min(h, w)
-                # center crop
-                left = (w - min_hw) // 2
-                top = (h - min_hw) // 2
-                right = left + min_hw
-                bottom = top + min_hw
-                img = img.crop((left, top, right, bottom))
-                return img
-            images = [center_crop_image(image) for image in images]
-        
-        return images 
-    
-    def load_and_append(existing_images, *args, **kwargs):
-        new_images = load_dataset_images(*args, **kwargs)
-        if new_images is None:
-            return existing_images
-        if len(new_images) == 0:
-            return existing_images
-        if existing_images is None:
-            existing_images = []
-        existing_images += new_images
-        gr.Info(f"Total images: {len(existing_images)}")
-        return existing_images
+    
     
     load_images_button.click(load_and_append, 
                         inputs=[input_gallery, advanced_radio, dataset_dropdown, num_images_slider,
@@ -1864,7 +1891,7 @@ with demo:
         with gr.Row():
             with gr.Column(scale=5, min_width=200):
                 input_gallery, submit_button, clear_images_button, dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_input_images_section(allow_download=True)
-                num_images_slider.value = 30
+                num_images_slider.value = 100
                 logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information", autofocus=False, autoscroll=False, lines=20)
                 
             with gr.Column(scale=5, min_width=200):
@@ -1881,7 +1908,7 @@ with demo:
                     perplexity_slider, n_neighbors_slider, min_dist_slider, 
                     sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                 ] = make_parameters_section()
-                num_eig_slider.value = 30
+                num_eig_slider.value = 100
         
         false_placeholder = gr.Checkbox(label="False", value=False, elem_id="false_placeholder", visible=False)
         no_prompt = gr.Textbox("", label="", elem_id="empty_placeholder", type="text", placeholder="", visible=False)