Add files for demo

app.py

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+import os
+os.environ['GRADIO_TEMP_DIR'] = "tmp/"
+
+import gradio as gr
+import json
+import random
+from PIL import Image
+from tqdm import tqdm
+from collections import OrderedDict
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+from matplotlib.offsetbox import OffsetImage, AnnotationBbox
+import numpy as np
+import torch
+
+from coda import CODA
+from coda.datasets import Dataset
+from coda.options import LOSS_FNS
+from coda.oracle import Oracle
+
+
+with open('iwildcam_demo_annotations.json', 'r') as f:
+    data = json.load(f)
+
+SPECIES_MAP = OrderedDict([
+    (24, "Jaguar"),           # panthera onca
+    (10, "Ocelot"),           # leopardus pardalis
+    (6, "Mountain Lion"),     # puma concolor
+    (101, "Common Eland"),    # tragelaphus oryx
+    (102, "Waterbuck"),       # kobus ellipsiprymnus
+])
+NAME_TO_ID = {name: id for id, name in SPECIES_MAP.items()}
+
+# Class names in order (0-4) from classes.txt
+CLASS_NAMES = ["Jaguar", "Ocelot", "Mountain Lion", "Common Eland", "Waterbuck"]
+NAME_TO_CLASS_IDX = {name: idx for idx, name in enumerate(CLASS_NAMES)}
+
+# Model information from models.txt
+MODEL_INFO = [
+    {"org": "Google", "name": "SigLIP2", "logo": "logos/google.png"},
+    {"org": "OpenAI", "name": "CLIPViT-L", "logo": "logos/openai.png"},
+    {"org": "Imageomics", "name": "BioCLIP", "logo": "logos/imageomics.png"}
+]
+
+# load image metadata
+images_data = []
+for annotation in tqdm(data['annotations'], desc='Loading annotations'):
+    image_id = annotation['image_id']
+    category_id = annotation['category_id']
+    image_info = next((img for img in data['images'] if img['id'] == image_id), None)
+    if image_info:
+        images_data.append({
+            'filename': image_info['file_name'],
+            'species_id': category_id,
+            'species_name': SPECIES_MAP[category_id]
+        })
+print(f"Loaded {len(images_data)} images for the quiz")
+
+# Load image filenames list
+with open('images.txt', 'r') as f:
+    image_filenames = [line.strip() for line in f.readlines() if line.strip()]
+
+# Initialize CODA
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+dataset = Dataset("iwildcam_demo.pt", device=device)
+loss_fn = LOSS_FNS['acc']
+oracle = Oracle(dataset, loss_fn=loss_fn)
+
+# Create CODA selector with default parameters
+class Args:
+    def __init__(self):
+        self.alpha = 0.9
+        self.learning_rate = 0.01
+        self.multiplier = 2.0
+        self.prefilter_n = 0
+        self.no_diag_prior = False
+        self.q = "eig"
+        self.method = "coda"
+        self.loss = "acc"
+
+args = Args()
+coda_selector = CODA.from_args(dataset, args)
+
+print(f"Initialized CODA with {dataset.preds.shape[1]} samples and {dataset.preds.shape[0]} models")
+
+# Global state
+current_image_info = None
+# coda_selector already initialized above
+# oracle already initialized above
+# dataset already initialized above
+# image_filenames already initialized above
+iteration_count = 0
+
+def get_model_predictions(chosen_idx):
+    """Get model predictions and scores for a specific image"""
+    global dataset
+
+    if dataset is None or chosen_idx >= dataset.preds.shape[1]:
+        return "No predictions available"
+
+    # Get predictions for this image (shape: [num_models, num_classes])
+    image_preds = dataset.preds[:, chosen_idx, :].detach().cpu().numpy()
+
+    predictions_list = []
+
+    for model_idx in range(image_preds.shape[0]):
+        model_scores = image_preds[model_idx]
+        predicted_class_idx = model_scores.argmax()
+        predicted_class_name = CLASS_NAMES[predicted_class_idx]
+        confidence = model_scores[predicted_class_idx]
+
+        model_info = MODEL_INFO[model_idx]
+        predictions_list.append(f"**{model_info['org']} {model_info['name']}:** {predicted_class_name} *({confidence:.3f})*")
+
+    predictions_text = "### Model Predictions\n\n" + " | ".join(predictions_list)
+
+    return predictions_text
+
+def add_logo_to_x_axis(ax, x_pos, logo_path, model_name, height_px=35):
+    """Add a logo image to x-axis next to model name"""
+    try:
+        img = mpimg.imread(logo_path)
+        # Calculate zoom to achieve desired height in pixels
+        # Rough conversion: height_px / image_height / dpi * 72
+        zoom = height_px / img.shape[0] / ax.figure.dpi * 72
+        imagebox = OffsetImage(img, zoom=zoom)
+
+        # Position logo to the left of the x-tick
+        logo_offset = -0.2  # Adjust this to move logo left/right relative to tick
+        y_offset = -0.08
+        ab = AnnotationBbox(imagebox, (x_pos + logo_offset, y_offset),
+                           xycoords=('data', 'axes fraction'), frameon=False)
+        ax.add_artist(ab)
+    except Exception as e:
+        print(f"Could not load logo {logo_path}: {e}")
+
+def get_next_coda_image():
+    """Get the next image that CODA wants labeled"""
+    global current_image_info, coda_selector, iteration_count
+
+    # Get next item from CODA
+    chosen_idx, selection_prob = coda_selector.get_next_item_to_label()
+
+    # Get the corresponding image filename
+    if chosen_idx < len(image_filenames):
+        filename = image_filenames[chosen_idx]
+        image_path = os.path.join('iwildcam_demo_images', filename)
+
+        # Find the corresponding annotation for this image
+        current_image_info = None
+        for annotation in data['annotations']:
+            image_id = annotation['image_id']
+            image_info = next((img for img in data['images'] if img['id'] == image_id), None)
+            if image_info and image_info['file_name'] == filename:
+                current_image_info = {
+                    'filename': filename,
+                    'species_id': annotation['category_id'],
+                    'species_name': SPECIES_MAP[annotation['category_id']],
+                    'chosen_idx': chosen_idx,
+                    'selection_prob': selection_prob
+                }
+                break
+
+        try:
+            image = Image.open(image_path)
+            predictions = get_model_predictions(chosen_idx)
+            return image, f"Iteration {iteration_count}: CODA selected this image for labeling", predictions
+        except Exception as e:
+            print(f"Error loading image {image_path}: {e}")
+            return None, f"Error loading image: {e}", "No predictions available"
+    else:
+        return None, "Image index out of range", "No predictions available"
+
+def check_answer(user_choice):
+    """Process user's label and update CODA"""
+    global current_image_info, coda_selector, iteration_count
+
+    if current_image_info is None:
+        return "Please load an image first!", "", None, "No predictions", None, None
+
+    correct_species = current_image_info['species_name']
+    chosen_idx = current_image_info['chosen_idx']
+    selection_prob = current_image_info['selection_prob']
+
+    # Convert user choice to class index (0-5)
+    if user_choice == "I don't know":
+        # For "I don't know", just remove from sampling without providing label
+        coda_selector.unlabeled_idxs.remove(chosen_idx)
+        result = f"The last image was skipped and will not be used for model selection. The correct species was {correct_species}. "
+    else:
+        user_class_idx = NAME_TO_CLASS_IDX.get(user_choice, NAME_TO_CLASS_IDX[correct_species])
+        if user_choice == correct_species:
+            result = f"🎉 Correct! The last image was indeed a {correct_species}!"
+        else:
+            result = f"❌ Incorrect. The last image was a {correct_species}, not a {user_choice}. This may mislead the model selection process!"
+
+        # Update CODA with the label
+        coda_selector.add_label(chosen_idx, user_class_idx, selection_prob)
+
+    iteration_count += 1
+
+    # Get updated plots
+    prob_plot = create_probability_chart()
+    accuracy_plot = create_accuracy_chart()
+
+    # Load next image
+    next_image, status, predictions = get_next_coda_image()
+    # Create HTML with inline help button for status
+    status_html = f'{status} <span class="inline-help-btn" title="What is this?">?</span>'
+    return result, status_html, next_image, predictions, prob_plot, accuracy_plot
+
+def create_probability_chart():
+    """Create a bar chart showing probability each model is best"""
+    global coda_selector
+
+    if coda_selector is None:
+        # Fallback for initial state
+        model_labels = [info['name'] for info in MODEL_INFO]
+        probabilities = np.ones(len(MODEL_INFO)) / len(MODEL_INFO)  # Uniform prior
+    else:
+        probs_tensor = coda_selector.get_pbest()
+        probabilities = probs_tensor.detach().cpu().numpy().flatten()
+        model_labels = ["   " + info['name'] for info in MODEL_INFO[:len(probabilities)]]
+
+    # Find the index of the highest probability
+    best_idx = np.argmax(probabilities)
+
+    fig, ax = plt.subplots(figsize=(8, 2.8), dpi=150)
+
+    # Create colors array - highlight the best model
+    colors = ['orange' if i == best_idx else 'steelblue' for i in range(len(model_labels))]
+    bars = ax.bar(range(len(model_labels)), probabilities, color=colors, alpha=0.7)
+
+    # Add text above the highest bar
+    ax.text(best_idx, probabilities[best_idx] + 0.0025, 'Current best guess',
+            ha='center', va='bottom', fontsize=12, fontweight='bold')
+
+    ax.set_ylabel('Probability model is best', fontsize=12)
+    ax.set_title(f'CODA Model Selection Probabilities (Iteration {iteration_count})', fontsize=12)
+    ax.set_ylim(np.min(probabilities) - 0.01, np.max(probabilities) + 0.02)
+
+    # Set x-axis labels and ticks
+    ax.set_xticks(range(len(model_labels)))
+    ax.set_xticklabels(model_labels, fontsize=12, ha='center')
+
+    # Add logos to x-axis
+    for i, model_info in enumerate(MODEL_INFO[:len(probabilities)]):
+        add_logo_to_x_axis(ax, i, model_info['logo'], model_info['name'])
+    plt.yticks(fontsize=12)
+    plt.tight_layout()
+
+    # Save the figure and close it to prevent memory leaks
+    temp_fig = fig
+    plt.close(fig)
+    return temp_fig
+
+def create_accuracy_chart():
+    """Create a bar chart showing true accuracy of each model"""
+    global oracle, dataset
+
+    if oracle is None or dataset is None:
+        # Fallback for initial state
+        model_labels = [info['name'] for info in MODEL_INFO]
+        accuracies = np.random.random(len(MODEL_INFO))  # Random accuracies for now
+    else:
+        true_losses = oracle.true_losses(dataset.preds)
+        # Convert losses to accuracies (assuming loss is 1 - accuracy)
+        accuracies = (1 - true_losses).detach().cpu().numpy().flatten()
+        model_labels = ["   " + info['name'] for info in MODEL_INFO[:len(accuracies)]]
+
+    # Find the index of the highest accuracy
+    best_idx = np.argmax(accuracies)
+
+    fig, ax = plt.subplots(figsize=(8, 2.8), dpi=150)
+
+    # Create colors array - highlight the best model
+    colors = ['red' if i == best_idx else 'forestgreen' for i in range(len(model_labels))]
+    bars = ax.bar(range(len(model_labels)), accuracies, color=colors, alpha=0.7)
+
+    # Add text above the highest bar
+    ax.text(best_idx, accuracies[best_idx] + 0.005, 'True best model',
+            ha='center', va='bottom', fontsize=12, fontweight='bold')
+
+    ax.set_ylabel('True (oracle) \naccuracy of model', fontsize=12)
+    ax.set_title('True Model Accuracies', fontsize=12)
+    ax.set_ylim(np.min(accuracies) - 0.025, np.max(accuracies) + 0.05)
+
+    # Set x-axis labels and ticks
+    ax.set_xticks(range(len(model_labels)))
+    ax.set_xticklabels(model_labels, fontsize=12, ha='center')
+
+    # Add logos to x-axis
+    for i, model_info in enumerate(MODEL_INFO[:len(accuracies)]):
+        add_logo_to_x_axis(ax, i, model_info['logo'], model_info['name'])
+    plt.yticks(fontsize=12)
+    plt.tight_layout()
+
+    # Save the figure and close it to prevent memory leaks
+    temp_fig = fig
+    plt.close(fig)
+    return temp_fig
+
+# Create the Gradio interface
+with gr.Blocks(title="CODA: Wildlife Photo Classification Challenge", 
+               theme=gr.themes.Base(),
+               css="""
+               .subtle-outline {
+                   border: 1px solid var(--border-color-primary) !important;
+                   background: transparent !important;
+                   border-radius: var(--radius-lg);
+                   padding: 1rem;
+               }
+               .subtle-outline .flex {
+                   background-color: white !important;
+               }
+
+               /* Popup overlay styles */
+               .popup-overlay {
+                   position: fixed;
+                   top: 0;
+                   left: 0;
+                   width: 100%;
+                   height: 100%;
+                   background-color: rgba(0, 0, 0, 0.5);
+                   z-index: 1000;
+                   display: flex;
+                   justify-content: center;
+                   align-items: center;
+               }
+
+               .popup-overlay > div {
+                   background: transparent !important;
+                   border: none !important;
+                   padding: 0 !important;
+                   margin: 0 !important;
+               }
+
+               .popup-content {
+                   background: white !important;
+                   padding: 2rem !important;
+                   border-radius: 1rem !important;
+                   max-width: 850px;
+                   width: 90%;
+                   max-height: 80vh;
+                   overflow-y: auto;
+                   box-shadow: 0 10px 25px rgba(0, 0, 0, 0.3);
+                   border: none !important;
+                   margin: 0 !important;
+               }
+
+               .popup-content > div {
+                   background: white !important;
+                   border: none !important;
+                   padding: 0 !important;
+                   margin: 0 !important;
+                   overflow-y: visible !important;
+                   max-height: none !important;
+               }
+
+               /* Ensure gradio column components don't interfere with scrolling */
+               .popup-content .gradio-column {
+                   overflow-y: visible !important;
+                   max-height: none !important;
+               }
+
+               /* Ensure images in popup are responsive */
+               .popup-content img {
+                   max-width: 100% !important;
+                   height: auto !important;
+               }
+
+               /* Center title */
+               .text-center {
+                   text-align: center !important;
+               }
+
+               /* Subtitle styling */
+               .subtitle {
+                   text-align: center !important;
+                   font-weight: 300 !important;
+                   color: #666 !important;
+                   margin-top: -0.5rem !important;
+               }
+
+               /* Question mark icon styling */
+               .panel-container {
+                   position: relative;
+               }
+
+               .help-icon {
+                   position: absolute;
+                   top: 5px;
+                   right: 5px;
+                   width: 25px;
+                   height: 25px;
+                   background-color: #f8f9fa;
+                   color: #6c757d;
+                   border: 1px solid #dee2e6;
+                   border-radius: 50%;
+                   display: flex;
+                   align-items: center;
+                   justify-content: center;
+                   cursor: pointer;
+                   font-size: 13px;
+                   font-weight: 600;
+                   z-index: 10;
+                   transition: all 0.2s ease;
+                   box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
+               }
+
+               .help-icon:hover {
+                   background-color: #e9ecef;
+                   color: #495057;
+                   border-color: #adb5bd;
+                   box-shadow: 0 2px 6px rgba(0, 0, 0, 0.15);
+               }
+
+               /* Help popup styles */
+               .help-popup-overlay {
+                   position: fixed;
+                   top: 0;
+                   left: 0;
+                   width: 100%;
+                   height: 100%;
+                   background-color: rgba(0, 0, 0, 0.5);
+                   z-index: 1001;
+                   display: flex;
+                   justify-content: center;
+                   align-items: center;
+               }
+
+               .help-popup-overlay > div {
+                   background: transparent !important;
+                   border: none !important;
+                   padding: 0 !important;
+                   margin: 0 !important;
+               }
+
+               .help-popup-content {
+                   background: white !important;
+                   padding: 1.5rem !important;
+                   border-radius: 0.5rem !important;
+                   max-width: 600px;
+                   width: 90%;
+                   box-shadow: 0 10px 25px rgba(0, 0, 0, 0.3);
+                   border: none !important;
+                   margin: 0 !important;
+               }
+
+               .help-popup-content > div {
+                   background: white !important;
+                   border: none !important;
+                   padding: 0 !important;
+                   margin: 0 !important;
+               }
+
+               /* Inline help button */
+               .inline-help-btn {
+                   display: inline-block;
+                   width: 20px;
+                   height: 20px;
+                   background-color: #f8f9fa;
+                   color: #6c757d;
+                   border: 1px solid #dee2e6;
+                   border-radius: 50%;
+                   text-align: center;
+                   line-height: 18px;
+                   cursor: pointer;
+                   font-size: 11px;
+                   font-weight: 600;
+                   margin-left: 8px;
+                   vertical-align: middle;
+                   transition: all 0.2s ease;
+                   box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
+               }
+
+               .inline-help-btn:hover {
+                   background-color: #e9ecef;
+                   color: #495057;
+                   border-color: #adb5bd;
+                   box-shadow: 0 2px 6px rgba(0, 0, 0, 0.15);
+               }
+
+               #hidden-selection-help-btn {
+                   display: none;
+               }
+
+               /* Reduce spacing around status text */
+               .status-text {
+                   margin: 0 !important;
+                   padding: 0 !important;
+               }
+
+               .status-text > div {
+                   margin: 0 !important;
+                   padding: 0 !important;
+               }
+
+               /* Compact model predictions panel */
+               .compact-predictions {
+                   line-height: 1.1 !important;
+                   margin: 0 !important;
+                   padding: 0.1rem !important;
+               }
+
+               .compact-predictions p {
+                   margin: 0.05rem 0 !important;
+               }
+
+               .compact-predictions h3 {
+                   margin: 0 0 0.1rem 0 !important;
+               }
+
+               /* Target the subtle-outline group that contains predictions */
+               .subtle-outline {
+                   padding: 0.3rem !important;
+                   margin: 0.2rem 0 !important;
+               }
+
+               /* Target the column inside the outline */
+               .subtle-outline .flex {
+                   padding: 0 !important;
+                   margin: 0 !important;
+               }
+               """) as demo:
+    # Main page title
+    gr.Markdown("# CODA: Consensus-Driven Active Model Selection", elem_classes="text-center")
+
+    # Popup component
+    with gr.Group(visible=True, elem_classes="popup-overlay") as popup_overlay:
+        with gr.Group(elem_classes="popup-content"):
+            # Main intro content
+            intro_content = gr.Markdown("""
+            # CODA: Consensus-Driven Active Model Selection
+
+            ## Wildlife Photo Classification Challenge
+
+            You are a wildlife ecologist who has just collected a season's worth of imagery from cameras
+            deployed in Africa and Central and South America. You want to know what species occur in this imagery,
+            and you hope to use a pre-trained classifier to give you answers quickly.
+            But which one should you use?
+
+            Instead of labeling a large validation set, our new method, **CODA**, enables you to perform **active model selection**.
+            That is, CODA uses predictions from candidate models to guide the labeling process, querying you (a species identification expert)
+            for labels on a select few images that will most efficiently differentiate between your candidate machine learning models.
+
+            This demo lets you try CODA yourself! First, become a species identification expert by reading our classification guide
+            so that you will be equipped to provide ground truth labels. Then, watch as CODA narrows down the best model over time
+            as you provide labels for the query images. You will see that with your input CODA is able to identify the best model candidate
+            with as few as ten (correctly) labeled images.
+            """)
+
+            # Species guide content (initially hidden)
+            with gr.Column(visible=False) as species_guide_content:
+                gr.Markdown("""
+                # Species Classification Guide
+                 
+                ### Learn to identify the five wildlife species in this demo.
+                
+                ## Jaguar
+                """)
+
+                gr.Image("species_id/jaguar.jpg", label="Jaguar example image", show_label=False)
+
+                gr.Markdown("""       
+                The largest cat in the Americas, with a stocky, muscular build and a broad head; its golden coat is patterned with rosettes that often have central spots inside.
+
+                ----
+                
+                ## Ocelot
+                
+                """)
+
+                gr.Image("species_id/ocelot.jpg", label="Ocelot example image", show_label=False)
+
+                gr.Markdown("""
+                A medium-sized spotted cat about twice the size of a domestic cat, with a slender body, large eyes, and striking chain-link or stripe-like rosettes. It differs from jaguars by its smaller size, leaner build, and more elongated markings.
+                            
+                ----
+                            
+                ## Mountain Lion
+                """)
+
+                gr.Image("species_id/mountainlion.jpg", label="Mountain lion example image", show_label=False)
+
+                gr.Markdown("""
+                Also called cougar or puma, this cat has a plain tawny or grayish coat without spots or rosettes. Its long tail and uniformly colored fur distinguish it from jaguars and ocelots.
+                
+                ----
+                            
+                ## Common Eland
+
+                """)
+
+                gr.Image("species_id/commoneland.jpg", label="Eland example image", show_label=False)
+
+                gr.Markdown("""
+                The largest antelope species, with a robust body, spiraled horns on both sexes, and a characteristic dewlap hanging from the throat. It differs from waterbuck by its lighter tan coat, faint body stripes, and massive size.
+
+                ----
+                            
+                ## Waterbuck
+                """)
+
+                gr.Image("species_id/waterbuck.jpg", label="Waterbuck example image", show_label=False)
+
+                gr.Markdown("""
+                A shaggy, dark brown antelope recognized by its white rump ring and backward-curving horns in males. Smaller and darker than the common eland, waterbuck prefer wet habitats and lack the eland's throat dewlap.
+                
+                ----
+                            
+                """)
+
+            with gr.Row():
+                back_button = gr.Button("← Back to Intro", variant="secondary", size="lg", visible=False)
+                guide_button = gr.Button("View Species Classification Guide", variant="secondary", size="lg")
+                popup_start_button = gr.Button("Start Demo", variant="primary", size="lg")
+
+    # Help popups for panels
+    with gr.Group(visible=False, elem_classes="help-popup-overlay") as prob_help_popup:
+        with gr.Group(elem_classes="help-popup-content"):
+            gr.Markdown("""
+            ## CODA Model Selection Probabilities
+
+            This chart shows CODA's current confidence in each candidate model being the best performer.
+
+            **How to read this chart:**
+            - Each bar represents one of the candidate machine learning models
+            - The height of each bar shows the probability (0-100%) that this model is the best, according to CODA
+            - The orange bar indicates CODA's current best guess
+            - As you provide more labels, CODA updates these probabilities
+
+            **What you'll see:**
+            - CODA initializes these probabilities based on each model's agreement with the consensus, providing informative priors
+            - As you label images, some models will gain confidence while others lose it
+            - The goal is for one model to clearly emerge as the winner
+                        
+            """)
+            prob_help_close = gr.Button("Close", variant="secondary")
+
+    with gr.Group(visible=False, elem_classes="help-popup-overlay") as acc_help_popup:
+        with gr.Group(elem_classes="help-popup-content"):
+            gr.Markdown("""
+            ## True Model Accuracies
+
+            This chart shows the actual performance of each model on the complete dataset (only possible with oracle knowledge).
+
+            **How to read this chart:**
+            - Each bar represents the true accuracy of one model
+            - The red bar shows the actual best-performing model
+            - This information is hidden from CODA during the selection process
+            - You can compare this with CODA's estimates to see how well it's doing
+
+            **Why this matters:**
+            - This represents the "ground truth" that CODA is trying to discover
+            - In real scenarios, you wouldn't know these true accuracies beforehand
+            - The demo shows these to illustrate how CODA's estimates align with reality
+
+            """)
+            acc_help_close = gr.Button("Close", variant="secondary")
+
+    with gr.Group(visible=False, elem_classes="help-popup-overlay") as selection_help_popup:
+        with gr.Group(elem_classes="help-popup-content"):
+            gr.Markdown("""
+            ## How CODA Selects Images for Labeling
+
+            [Placeholder]
+            """)
+            selection_help_close = gr.Button("Close", variant="secondary")
+    
+    # Two panels with bar charts
+    with gr.Row():
+        with gr.Column(scale=1):
+            with gr.Group(elem_classes="panel-container"):
+                prob_help_button = gr.Button("?", elem_classes="help-icon", size="sm")
+                prob_plot = gr.Plot(
+                    value=None,
+                    show_label=False
+                )
+        with gr.Column(scale=1):
+            with gr.Group(elem_classes="panel-container"):
+                acc_help_button = gr.Button("?", elem_classes="help-icon", size="sm")
+                accuracy_plot = gr.Plot(
+                    value=create_accuracy_chart(),
+                    show_label=False
+                )
+    
+    # Status display with help button
+    status_with_help = gr.HTML("", visible=True, elem_classes="status-text")
+    selection_help_button = gr.Button("", visible=False, elem_id="hidden-selection-help-btn")
+
+    with gr.Row():
+        image_display = gr.Image(
+            label="Identify this animal:",
+            value=None,
+            height=400,
+            width=550
+        )
+
+    # Model predictions panel (full width, single line)
+    with gr.Group(elem_classes="subtle-outline"):
+        with gr.Column(elem_classes="flex items-center justify-center h-full"):
+            model_predictions_display = gr.Markdown(
+                "### Model Predictions\n\n*Start the demo to see model votes!*",
+                show_label=False,
+                elem_classes="text-center compact-predictions"
+            )
+    
+    gr.Markdown("### Which species is this?")
+    
+    with gr.Row():
+        # Create buttons for each species
+        species_buttons = []
+        for species_name in SPECIES_MAP.values():
+            btn = gr.Button(species_name, variant="secondary", size="lg")
+            species_buttons.append(btn)
+        
+        # Add "I don't know" button
+        idk_button = gr.Button("I don't know", variant="primary", size="lg")
+    
+    # Result display
+    result_display = gr.Markdown("", visible=True)
+    
+    # Add start over button
+    start_over_button = gr.Button("Start Over", variant="secondary", size="lg")
+
+    # Set up button interactions
+    def start_demo():
+        global iteration_count, coda_selector
+        # Reset the demo state
+        iteration_count = 0
+        coda_selector = CODA.from_args(dataset, args)
+
+        image, status, predictions = get_next_coda_image()
+        prob_plot = create_probability_chart()
+        acc_plot = create_accuracy_chart()
+        # Create HTML with inline help button
+        status_html = f'{status} <span class="inline-help-btn" title="What is this?">?</span>'
+        return image, status_html, predictions, prob_plot, acc_plot, gr.update(visible=False), "", gr.update(visible=True)
+
+    def start_over():
+        global iteration_count, coda_selector
+        # Reset the demo state
+        iteration_count = 0
+        coda_selector = CODA.from_args(dataset, args)
+
+        # Reset all displays
+        prob_plot = create_probability_chart()
+        acc_plot = create_accuracy_chart()
+        return None, "Demo reset. Click 'Start CODA Demo' to begin.", "### Model Predictions\n\n*Start the demo to see model votes!*", prob_plot, acc_plot, "", gr.update(visible=True), gr.update(visible=False)
+
+    def show_species_guide():
+        # Show species guide, hide intro content, show back button, hide guide button
+        return gr.update(visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)
+
+    def show_intro():
+        # Show intro content, hide species guide, hide back button, show guide button
+        return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
+
+    def show_prob_help():
+        return gr.update(visible=True)
+
+    def hide_prob_help():
+        return gr.update(visible=False)
+
+    def show_acc_help():
+        return gr.update(visible=True)
+
+    def hide_acc_help():
+        return gr.update(visible=False)
+
+    def show_selection_help():
+        return gr.update(visible=True)
+
+    def hide_selection_help():
+        return gr.update(visible=False)
+
+    popup_start_button.click(
+        fn=start_demo,
+        outputs=[image_display, status_with_help, model_predictions_display, prob_plot, accuracy_plot, popup_overlay, result_display, selection_help_button]
+    )
+
+    start_over_button.click(
+        fn=start_over,
+        outputs=[image_display, status_with_help, model_predictions_display, prob_plot, accuracy_plot, result_display, popup_overlay, selection_help_button]
+    )
+
+    guide_button.click(
+        fn=show_species_guide,
+        outputs=[intro_content, species_guide_content, back_button, guide_button]
+    )
+
+    back_button.click(
+        fn=show_intro,
+        outputs=[intro_content, species_guide_content, back_button, guide_button]
+    )
+
+    # Help popup handlers
+    prob_help_button.click(
+        fn=show_prob_help,
+        outputs=[prob_help_popup]
+    )
+
+    prob_help_close.click(
+        fn=hide_prob_help,
+        outputs=[prob_help_popup]
+    )
+
+    acc_help_button.click(
+        fn=show_acc_help,
+        outputs=[acc_help_popup]
+    )
+
+    acc_help_close.click(
+        fn=hide_acc_help,
+        outputs=[acc_help_popup]
+    )
+
+    selection_help_button.click(
+        fn=show_selection_help,
+        outputs=[selection_help_popup]
+    )
+
+    selection_help_close.click(
+        fn=hide_selection_help,
+        outputs=[selection_help_popup]
+    )
+
+    for btn in species_buttons:
+        btn.click(
+            fn=check_answer,
+            inputs=[gr.State(btn.value)],
+            outputs=[result_display, status_with_help, image_display, model_predictions_display, prob_plot, accuracy_plot]
+        )
+
+    idk_button.click(
+        fn=check_answer,
+        inputs=[gr.State("I don't know")],
+        outputs=[result_display, status_with_help, image_display, model_predictions_display, prob_plot, accuracy_plot]
+    )
+
+    # Add JavaScript to handle inline help button clicks
+    demo.load(
+        lambda: None,
+        outputs=[],
+        js="""
+        () => {
+            setTimeout(() => {
+                document.addEventListener('click', function(e) {
+                    if (e.target && e.target.classList.contains('inline-help-btn')) {
+                        e.preventDefault();
+                        e.stopPropagation();
+                        const hiddenBtn = document.getElementById('hidden-selection-help-btn');
+                        if (hiddenBtn) {
+                            hiddenBtn.click();
+                        }
+                    }
+                });
+            }, 100);
+        }
+        """
+    )
+
+if __name__ == "__main__":
+    demo.launch(
+        # share=True,
+        server_port=7861,
+        allowed_paths=["/"]
+    )

classes.txt

@@ -0,0 +1,5 @@
+Jaguar
+Ocelot
+Mountain Lion
+Common Eland
+Waterbuck

images.txt

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iwildcam_demo.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:54b808c02fa7f6d6429682c55c8945ef2338607841ce4551a22253b8bc8778aa
+size 47187

iwildcam_demo_labels.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ab9fc383afd63aeacbccddf88a3f8c85ea8157c35b0b0e32d66a8d9a7054a5e4
+size 7748

models.txt

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+google/siglip2-so400m-patch16-naflex
+openai/clip-vit-large-patch14
+imageomics/bioclip

requirements.txt

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+gradio>=4.0.0
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+numpy>=1.21.0
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