add app

app.py

@@ -0,0 +1,82 @@
+# app.py
+
+import gradio as gr
+from utils import HF_REPO, ROIS, load_roi_image, load_images, get_hypotheses_for_selection
+
+# -------- CONFIGURATION --------
+MODELS = ["SAE", "SAE_ICA", "ICA", "PCA", "NMF", "Voxel"]
+
+# Initial values derived from the helper functions after the structure is loaded
+INITIAL_HYPOTHESES = get_hypotheses_for_selection(MODELS[0], ROIS[0])
+INITIAL_ROI_IMAGE = load_roi_image(ROIS[0]) 
+
+# -------- GRADIO UI AND CALLBACK LOGIC --------
+def update_hypotheses(model, roi):
+    new_hypotheses = get_hypotheses_for_selection(model, roi)
+    return gr.Dropdown(choices=new_hypotheses, value=new_hypotheses[0] if new_hypotheses else None)
+
+with gr.Blocks() as demo:
+    gr.Markdown("## BrainExplore: Visual Concept Explorer")
+
+    # 1. TOP ROW: Dropdowns (Menus)
+    with gr.Row():
+        model_dropdown = gr.Dropdown(label="Select Model", choices=MODELS, value=MODELS[0], scale=1)
+        roi_dropdown = gr.Dropdown(label="Select ROI", choices=ROIS, value=ROIS[0], scale=1)
+        
+        hyp_dropdown = gr.Dropdown(
+            label="Select Hypothesis", 
+            choices=INITIAL_HYPOTHESES,
+            value=INITIAL_HYPOTHESES[0] if INITIAL_HYPOTHESES else None,
+            scale=1
+        )
+
+    # 2. SECOND ROW: Visualizations (ROI Image and Gallery)
+    with gr.Row(equal_height=True):
+        
+        roi_image = gr.Image(
+            label="Selected Brain Region (ROI)",
+            value=INITIAL_ROI_IMAGE,
+            interactive=False, 
+            height=400, 
+            scale=1 
+        )
+        
+        image_gallery = gr.Gallery(
+            label="Top Activating Images", 
+            columns=None, rows=1, scale=3, 
+            object_fit="contain", preview=True, height=400
+        )
+
+    # -------- CALLBACKS --------
+    model_dropdown.change(
+        fn=update_hypotheses, 
+        inputs=[model_dropdown, roi_dropdown], 
+        outputs=[hyp_dropdown], 
+        queue=False 
+    ).then(
+        fn=load_images, 
+        inputs=[model_dropdown, roi_dropdown, hyp_dropdown], 
+        outputs=[image_gallery]
+    )
+
+    # ROI Change: Update Hypotheses, THEN update ROI Image, THEN update Activating Images
+    roi_dropdown.change(
+        fn=update_hypotheses, 
+        inputs=[model_dropdown, roi_dropdown], 
+        outputs=[hyp_dropdown],
+        queue=False
+    ).then(
+        fn=load_roi_image,
+        inputs=[roi_dropdown],
+        outputs=[roi_image]
+    ).then(
+        fn=load_images, 
+        inputs=[model_dropdown, roi_dropdown, hyp_dropdown], 
+        outputs=[image_gallery]
+    )
+
+    # Hypothesis Change: Update Activating Images only
+    hyp_dropdown.change(fn=load_images, inputs=[model_dropdown, roi_dropdown, hyp_dropdown], outputs=[image_gallery])
+
+if __name__ == "__main__":
+    demo.launch()

utils.py

@@ -0,0 +1,99 @@
+import json
+from datasets import load_dataset
+from PIL import Image
+from huggingface_hub import hf_hub_download
+import os
+
+# -------- CONFIG & STATE --------
+HF_REPO = "mcosarinsky/BrainExplore-Images"
+STRUCTURE_DATA = None
+ROIS = ["EBA","FBA-1","FBA-2","FFA-1","FFA-2","OFA","OPA","OWFA","PPA","RSC","VWFA-1","VWFA-2","hV4"]
+
+# -------- 1. JSON STRUCTURE HELPERS --------
+def load_structure(repo_id, filename="structure.json"):
+    """
+    Downloads the structure.json file from the Hugging Face Hub and loads it into memory.
+    """
+    global STRUCTURE_DATA
+    if STRUCTURE_DATA is not None:
+        return STRUCTURE_DATA
+        
+    try:
+        local_path = hf_hub_download(
+            repo_id=repo_id,
+            filename=filename,
+            repo_type="dataset"
+        )
+        with open(local_path, 'r') as f:
+            STRUCTURE_DATA = json.load(f)
+        
+        print(f"Successfully loaded {filename}.")
+        return STRUCTURE_DATA
+        
+    except Exception as e:
+        print(f"CRITICAL ERROR: Could not load {filename} via hf_hub_download: {e}")
+        return {}
+
+def get_hypotheses_for_selection(model, roi):
+    """
+    Retrieves the list of hypotheses based on the selected model and ROI 
+    from the pre-loaded STRUCTURE_DATA.
+    """
+    if not STRUCTURE_DATA:
+        return []
+        
+    try:
+        hypotheses = STRUCTURE_DATA.get(model, {}).get(roi, [])
+        return hypotheses
+    except Exception as e:
+        print(f"Error accessing structure data for {model}/{roi}: {e}")
+        return []
+
+# -------- 2. IMAGE PROCESSING HELPERS --------
+def pad_image(pil_img, padding_x=10, padding_y=0, color=(255, 255, 255)):
+    """
+    Adds padding to the sides of a PIL image.
+    """
+    old_width, old_height = pil_img.size
+    new_width = old_width + 2 * padding_x
+    new_height = old_height + 2 * padding_y
+    new_img = Image.new(pil_img.mode, (new_width, new_height), color)
+    new_img.paste(pil_img, (padding_x, padding_y))
+    return new_img
+
+
+# -------- 3. DATA LOADING HELPERS --------
+def load_images(model, roi, hyp):
+    """
+    Loads top activating images for the given parameters and applies padding.
+    """
+    folder_path = f"{model}/{roi}/{hyp}"
+    ds = load_dataset(HF_REPO, data_dir=folder_path, split="train", streaming=True)
+    image_paths = [img["image"] for img in ds.take(2)]
+    padded_images = [pad_image(img, padding_x=10, padding_y=0) for img in image_paths]
+    return padded_images
+
+def load_roi_image(roi_name):
+    """
+    Downloads the ROI PNG from the Hugging Face Hub, looking inside the 'ROI/' folder,
+    and loads it as a PIL Image.
+    """
+    if not roi_name:
+        return None
+        
+    file_name = f"ROI/{roi_name}.png"
+    
+    try:
+        local_path = hf_hub_download(
+            repo_id=HF_REPO,
+            filename=file_name,
+            repo_type="dataset"
+        )
+        return Image.open(local_path)
+    except Exception as e:
+        # A 404 error here likely means the file doesn't exist under that path
+        print(f"Error loading ROI image {roi_name} via hf_hub_download. Path used: {file_name}. Details: {e}")
+        return None
+
+# Perform initial structure load for dynamic dependency resolution in app.py
+load_structure(HF_REPO)