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@@ -33,3 +33,87 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
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.gitignore

@@ -0,0 +1 @@
+.env

README.md

@@ -1,8 +1,8 @@
 ---
-title: Background Removal Arena
-emoji: 🦀
-colorFrom: gray
-colorTo: indigo
+title: background-removal-arena
+emoji: ⚡
+colorFrom: pink
+colorTo: yellow
 sdk: gradio
 sdk_version: 5.7.1
 app_file: app.py

app.py

@@ -0,0 +1,266 @@
+import os
+import logging
+from typing import Tuple
+from dotenv import load_dotenv
+import gradio as gr
+import numpy as np
+from PIL import Image
+import random
+from db import compute_elo_scores, get_all_votes
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Access the API key
+PHOTOROOM_API_KEY = os.getenv('PHOTOROOM_API_KEY')
+CLIPDROP_API_KEY = os.getenv('CLIPDROP_API_KEY')
+
+
+def fetch_elo_scores():
+    """Fetch and log Elo scores."""
+    try:
+        elo_scores = compute_elo_scores()
+        logging.info("Elo scores successfully computed.")
+        return elo_scores
+    except Exception as e:
+        logging.error("Error computing Elo scores: %s", str(e))
+        return None
+
+def update_rankings_table():
+    """Update and return the rankings table based on Elo scores."""
+    elo_scores = fetch_elo_scores()
+    if elo_scores:
+        rankings = [
+            ["Photoroom", int(elo_scores.get("Photoroom", 1000))],
+            #["Clipdrop", int(elo_scores.get("Clipdrop", 1000))],
+            ["RemoveBG", int(elo_scores.get("RemoveBG", 1000))],
+            ["BRIA RMBG 2.0", int(elo_scores.get("BRIA RMBG 2.0", 1000))],
+        ]
+        rankings.sort(key=lambda x: x[1], reverse=True)
+        return rankings
+    else:
+        return [
+            ["Photoroom", -1],
+            #["Clipdrop", -1],
+            ["RemoveBG", -1],
+            ["BRIA RMBG 2.0", -1],
+        ]
+
+def select_new_image():
+    """Select a new image and its segmented versions."""
+    image_paths = load_images_from_directory("data/web-original-images")
+    random.shuffle(image_paths)  # Shuffle images to ensure randomness on reload
+    last_image_path = None
+    max_attempts = 10
+
+    for _ in range(max_attempts):
+        available_images = [path for path in image_paths if path != last_image_path]
+        
+        if not available_images:
+            logging.error("No available images to select from.")
+            return None
+
+        random_image_path = random.choice(available_images)
+        input_image = Image.open(random_image_path)
+        
+        image_filename = os.path.splitext(os.path.basename(random_image_path))[0] + ".png"
+        segmented_image_paths = {
+            "Photoroom": os.path.join("data/resized/photoroom", image_filename),
+            #"Clipdrop": os.path.join("data/processed/clipdrop", image_filename),
+            "RemoveBG": os.path.join("data/resized/removebg", image_filename),
+            "BRIA RMBG 2.0": os.path.join("data/resized/bria", image_filename)
+        }
+        
+        try:
+            selected_models = random.sample(list(segmented_image_paths.keys()), 2)
+            model_a_name, model_b_name = selected_models
+            model_a_output_path = segmented_image_paths[model_a_name]
+            model_b_output_path = segmented_image_paths[model_b_name]
+            model_a_output_image = Image.open(model_a_output_path)
+            model_b_output_image = Image.open(model_b_output_path)
+            return (random_image_path, input_image, model_a_output_path, model_a_output_image,
+                    model_b_output_path, model_b_output_image, model_a_name, model_b_name)
+        except FileNotFoundError as e:
+            logging.error("File not found: %s. Resampling another image.", e)
+            last_image_path = random_image_path
+
+    logging.error("Failed to select a new image after %d attempts.", max_attempts)
+    return None
+
+def get_notice_markdown():
+    """Generate the notice markdown with dynamic vote count."""
+    total_votes = len(get_all_votes())
+    return f"""
+    # ⚔️  Background Removal Arena: Compare & Test the Best Background Removal Models
+
+    ## 📜 How It Works
+    - **Blind Test**: You will see two images with their background removed from two anonymous background removal models (Clipdrop, RemoveBG, Photoroom, BRIA RMBG 2.0).
+    - **Vote for the Best**: Choose the best result, if none stand out choose "Tie". 
+
+    ## 📊 Stats
+    - **Total #votes**: {total_votes}
+
+    ## 👇 Test now!
+    """
+
+def compute_mask_difference(segmented_a, segmented_b):
+    """Compute the absolute difference between two image masks."""
+    mask_a = np.asarray(segmented_a)
+    mask_b = np.asarray(segmented_b)
+
+    # Create a binary mask where non-transparent pixels are marked as 1
+    mask_a_1d = np.where(mask_a[..., 3] != 0, 1, 0)
+    mask_b_1d = np.where(mask_b[..., 3] != 0, 1, 0)
+
+    # Compute the absolute difference between the masks
+    return np.abs(mask_a_1d - mask_b_1d)
+
+def gradio_interface():
+    """Create and return the Gradio interface."""
+    with gr.Blocks() as demo:
+        gr.Markdown("# Background Removal Arena")
+
+        with gr.Tabs() as tabs:
+            with gr.Tab("⚔️ Arena (battle)", id=0):
+                notice_markdown = gr.Markdown(get_notice_markdown(), elem_id="notice_markdown")
+
+                (fpath_input, input_image, fpath_a, segmented_a, fpath_b, segmented_b,
+                 a_name, b_name) = select_new_image()
+                model_a_name = gr.State(a_name)
+                model_b_name = gr.State(b_name)
+                fpath_input = gr.State(fpath_input)
+                fpath_a = gr.State(fpath_a)
+                fpath_b = gr.State(fpath_b)
+
+                # Compute the absolute difference between the masks
+                mask_difference = compute_mask_difference(segmented_a, segmented_b)
+
+                with gr.Row():
+                    image_a_display = gr.Image(
+                        value=segmented_a,
+                        type="pil",
+                        label="Model A",
+                        width=500,
+                        height=500
+                    )
+                    input_image_display = gr.AnnotatedImage(
+                        value=(input_image, [(mask_difference > 0, "Difference Mask")]),
+                        label="Input Image",
+                        width=500,
+                        height=500
+                    )
+                    image_b_display = gr.Image(
+                        value=segmented_b,
+                        type="pil",
+                        label="Model B",
+                        width=500,
+                        height=500
+                    )
+                tie = gr.State("Tie")
+                with gr.Row():
+                    vote_a_btn = gr.Button("👈  A is better")
+                    vote_tie = gr.Button("🤝  Tie")
+                    vote_b_btn = gr.Button("👉  B is better")
+
+                
+                vote_a_btn.click(
+                    fn=lambda: vote_for_model("model_a", (fpath_input, fpath_a, fpath_b), model_a_name, model_b_name),
+                    outputs=[
+                        fpath_input, input_image_display, fpath_a, image_a_display, fpath_b, image_b_display, model_a_name, model_b_name, notice_markdown
+                    ]
+                )
+                vote_b_btn.click(
+                    fn=lambda: vote_for_model("model_b", (fpath_input, fpath_a, fpath_b), model_a_name, model_b_name),
+                    outputs=[
+                        fpath_input, input_image_display, fpath_a, image_a_display, fpath_b, image_b_display, model_a_name, model_b_name, notice_markdown
+                    ]
+                )
+                vote_tie.click(
+                    fn=lambda: vote_for_model("tie", (fpath_input, fpath_a, fpath_b), model_a_name, model_b_name),
+                    outputs=[
+                        fpath_input, input_image_display, fpath_a, image_a_display, fpath_b, image_b_display, model_a_name, model_b_name, notice_markdown
+                    ]
+                )
+            
+                def vote_for_model(choice, fpaths, model_a_name, model_b_name):
+                    """Submit a vote for a model and return updated images and model names."""
+                    logging.info("Voting for model: %s", choice)
+
+                    vote_data = {
+                        "image_id": fpaths[0].value,
+                        "model_a": model_a_name.value,
+                        "model_b": model_b_name.value,
+                        "winner": choice,
+                        "fpath_a": fpaths[1].value,  
+                        "fpath_b": fpaths[2].value, 
+                    }
+
+                    try:
+                        logging.debug("Adding vote data to the database: %s", vote_data)
+                        from db import add_vote
+                        result = add_vote(vote_data)
+                        logging.info("Vote successfully recorded in the database with ID: %s", result["id"])
+                    except Exception as e:
+                        logging.error("Error recording vote: %s", str(e))
+
+                    (new_fpath_input, new_input_image, new_fpath_a, new_segmented_a,
+                     new_fpath_b, new_segmented_b, new_a_name, new_b_name) = select_new_image()
+                    model_a_name.value = new_a_name
+                    model_b_name.value = new_b_name
+                    fpath_input.value = new_fpath_input
+                    fpath_a.value = new_fpath_a
+                    fpath_b.value = new_fpath_b
+
+                    mask_difference = compute_mask_difference(new_segmented_a, new_segmented_b)
+
+                    # Update the notice markdown with the new vote count
+                    new_notice_markdown = get_notice_markdown()
+
+                    return (fpath_input.value, (new_input_image, [(mask_difference, "Mask")]), fpath_a.value, new_segmented_a,
+                            fpath_b.value, new_segmented_b, model_a_name.value, model_b_name.value, new_notice_markdown)
+           
+            with gr.Tab("🏆 Leaderboard", id=1) as leaderboard_tab:
+                rankings_table = gr.Dataframe(
+                    headers=["Model", "Ranking"],
+                    value=update_rankings_table(),
+                    label="Current Model Rankings",
+                    column_widths=[180, 60],
+                    row_count=4
+                )
+
+                leaderboard_tab.select(
+                    fn=lambda: update_rankings_table(),
+                    outputs=rankings_table
+                )
+
+            with gr.Tab("📊 Vote Data", id=2) as vote_data_tab:
+                def update_vote_data():
+                    votes = get_all_votes()
+                    return [[vote.id, vote.image_id, vote.model_a, vote.model_b, vote.winner, vote.timestamp] for vote in votes]
+
+                vote_table = gr.Dataframe(
+                    headers=["ID", "Image ID", "Model A", "Model B", "Winner", "Timestamp"],
+                    value=update_vote_data(),
+                    label="Vote Data",
+                    column_widths=[20, 150, 100, 100, 100, 150],
+                    row_count=0
+                )
+
+                vote_data_tab.select(
+                    fn=lambda: update_vote_data(),
+                    outputs=vote_table
+                )
+
+    return demo
+
+def load_images_from_directory(directory):
+    """Load and return image paths from a directory."""
+    image_files = [f for f in os.listdir(directory) if f.endswith(('.png', '.jpg', '.jpeg'))]
+    return [os.path.join(directory, f) for f in image_files]
+
+if __name__ == "__main__":
+    demo = gradio_interface()
+    demo.launch()