diff --git a/.gitignore b/.gitignore index d4d793cf48f4f76d9bf9f3506d208dd45e285c85..18aa2bc0e6b3ce90773afd054f2f0b06e1870427 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ benchmark_images_generations/ code/ results/ +backup/ +__pycache__/ \ No newline at end of file diff --git a/app.py b/app.py index eb41f46c5e9b1fbbec0ce38cbf2ccd7f3cafe4c6..f9cffcdd0ca04bfe0ae6e3bc2f3dc047b67a5562 100644 --- a/app.py +++ b/app.py @@ -1,396 +1,371 @@ import gradio as gr +import pandas as pd import os import random -import csv -from pathlib import Path -from datetime import datetime, timedelta -import tempfile -from huggingface_hub import HfApi, hf_hub_download, login -from huggingface_hub.utils import RepositoryNotFoundError, EntryNotFoundError +from datetime import datetime from apscheduler.schedulers.background import BackgroundScheduler -import atexit -import threading -import time -import shutil - -# --- Configuration --- -DATASET_REPO_ID = os.getenv("DATASET_REPO_ID", "matsant01/user-study-collected-preferences") -HF_TOKEN = os.getenv("HF_TOKEN") -RESULTS_FILENAME_IN_REPO = "preferences.csv" -TEMP_DIR = tempfile.mkdtemp() -LOCAL_RESULTS_FILE = Path(TEMP_DIR) / RESULTS_FILENAME_IN_REPO -UPLOAD_INTERVAL_HOURS = 0.1 - -DATA_DIR = Path("data") -IMAGE_EXTENSIONS = [".png", ".jpg", ".jpeg", ".webp"] - -# --- Global State for Upload Logic --- -hf_api = None -scheduler = BackgroundScheduler(daemon=True) -upload_lock = threading.Lock() -new_preferences_recorded_since_last_upload = threading.Event() - -# --- Hugging Face Hub Login & Initialization --- -def initialize_hub_and_results(): - global hf_api - if HF_TOKEN: - print("Logging into Hugging Face Hub...") +from PIL import Image + +import config +import utils + +# --- Global Variables & Initial Setup --- +# Attempt to log in to Hugging Face Hub at startup +utils.login_hugging_face() + +# Load preferences: Try from Hub, then local, then empty +preferences_df = utils.load_preferences_from_hf_hub(config.HF_DATASET_REPO_ID, config.RESULTS_CSV_FILE) +if preferences_df is None: + if os.path.exists(config.RESULTS_CSV_FILE): + print(f"Loading preferences from local file: {config.RESULTS_CSV_FILE}") try: - login(token=HF_TOKEN) - hf_api = HfApi() - print(f"Attempting initial download of {RESULTS_FILENAME_IN_REPO} from {DATASET_REPO_ID}") - hf_hub_download( - repo_id=DATASET_REPO_ID, - filename=RESULTS_FILENAME_IN_REPO, - repo_type="dataset", - token=HF_TOKEN, - local_dir=TEMP_DIR, - local_dir_use_symlinks=False - ) - print(f"Successfully downloaded existing {RESULTS_FILENAME_IN_REPO} to {LOCAL_RESULTS_FILE}") - except EntryNotFoundError: - print(f"{RESULTS_FILENAME_IN_REPO} not found in repo. Will create locally.") - except RepositoryNotFoundError: - print(f"Error: Dataset repository {DATASET_REPO_ID} not found or token lacks permissions.") - print("Results saving will be disabled.") - hf_api = None + preferences_df = pd.read_csv(config.RESULTS_CSV_FILE) + except pd.errors.EmptyDataError: + print(f"Local preferences file {config.RESULTS_CSV_FILE} is empty. Starting fresh.") + preferences_df = pd.DataFrame(columns=config.CSV_HEADERS) except Exception as e: - print(f"Error during initial download/login: {e}") - print("Proceeding without initial download. File will be created locally.") + print(f"Error loading local {config.RESULTS_CSV_FILE}: {e}. Starting fresh.") + preferences_df = pd.DataFrame(columns=config.CSV_HEADERS) else: - print("Warning: HF_TOKEN secret not found. Results will not be saved to the Hub.") - hf_api = None - -# --- Data Loading --- - -def find_image(folder_path: Path, base_name: str) -> Path | None: - for ext in IMAGE_EXTENSIONS: - file_path = folder_path / f"{base_name}{ext}" - if file_path.exists(): - return file_path - return None - -def get_sample_ids() -> list[str]: - sample_ids = [] - if DATA_DIR.is_dir(): - for item in DATA_DIR.iterdir(): - if item.is_dir(): - prompt_file = item / "prompt.txt" - input_bg = find_image(item, "input_bg") - input_fg = find_image(item, "input_fg") - output_baseline = find_image(item, "baseline") - output_tficon = find_image(item, "tf-icon") - if prompt_file.exists() and input_bg and input_fg and output_baseline and output_tficon: - sample_ids.append(item.name) - return sample_ids - -def load_sample_data(sample_id: str) -> dict | None: - sample_path = DATA_DIR / sample_id - if not sample_path.is_dir(): - return None - - prompt_file = sample_path / "prompt.txt" - input_bg_path = find_image(sample_path, "input_bg") - input_fg_path = find_image(sample_path, "input_fg") - output_baseline_path = find_image(sample_path, "baseline") - output_tficon_path = find_image(sample_path, "tf-icon") - - if not all([prompt_file.exists(), input_bg_path, input_fg_path, output_baseline_path, output_tficon_path]): - print(f"Warning: Missing files in sample {sample_id}") - return None - - try: - prompt = prompt_file.read_text().strip() - except Exception as e: - print(f"Error reading prompt for {sample_id}: {e}") - return None - - return { - "id": sample_id, - "prompt": prompt, - "input_bg": str(input_bg_path), - "input_fg": str(input_fg_path), - "output_baseline": str(output_baseline_path), - "output_tficon": str(output_tficon_path), - } - -# --- State and UI Logic --- - -INITIAL_SAMPLE_IDS = get_sample_ids() - -def get_next_sample(available_ids: list[str]) -> tuple[dict | None, list[str]]: - if not available_ids: - return None, [] - chosen_id = random.choice(available_ids) - remaining_ids = [id for id in available_ids if id != chosen_id] - sample_data = load_sample_data(chosen_id) - return sample_data, remaining_ids - -def display_new_sample(state: dict, available_ids: list[str]): - sample_data, remaining_ids = get_next_sample(available_ids) - - if not sample_data: - return { - prompt_display: gr.update(value="**Prompt:** No more samples available. Thank you!"), - input_bg_display: gr.update(value=None, visible=False), - input_fg_display: gr.update(value=None, visible=False), - output_a_display: gr.update(value=None, visible=False), - output_b_display: gr.update(value=None, visible=False), - choice_button_a: gr.update(visible=False), - choice_button_b: gr.update(visible=False), - next_button: gr.update(visible=False), - status_display: gr.update(value="**Status:** Completed!"), - app_state: state, - available_samples_state: remaining_ids - } - - outputs = [ - {"model_name": "baseline", "path": sample_data["output_baseline"]}, - {"model_name": "tf-icon", "path": sample_data["output_tficon"]}, - ] - random.shuffle(outputs) - output_a = outputs[0] - output_b = outputs[1] - - state = { - "current_sample_id": sample_data["id"], - "output_a_model_name": output_a["model_name"], - "output_b_model_name": output_b["model_name"], - } - - return { - prompt_display: gr.update(value=f"**Prompt:** {sample_data['prompt']}"), - input_bg_display: gr.update(value=sample_data["input_bg"], visible=True), - input_fg_display: gr.update(value=sample_data["input_fg"], visible=True), - output_a_display: gr.update(value=output_a["path"], visible=True), - output_b_display: gr.update(value=output_b["path"], visible=True), - choice_button_a: gr.update(visible=True, interactive=True), - choice_button_b: gr.update(visible=True, interactive=True), - next_button: gr.update(visible=False), - status_display: gr.update(value="**Status:** Please choose the image you prefer."), - app_state: state, - available_samples_state: remaining_ids - } - -def record_preference(choice: str, state: dict, request: gr.Request): - if not request: - print("Error: Request object is None. Cannot get session ID.") - session_id = "unknown_session" + print("No existing preferences found on Hub or locally. Starting with an empty table.") + preferences_df = pd.DataFrame(columns=config.CSV_HEADERS) + +# Scan for available data +ALL_SAMPLES_BY_DOMAIN = utils.scan_data_directory(config.DATA_FOLDER) +if not ALL_SAMPLES_BY_DOMAIN: + print(f"CRITICAL: No data found in {config.DATA_FOLDER}. The app might not function correctly.") + # Potentially raise an error or display a message in the UI if no data + +# --- Scheduler for Periodic Uploads --- +def scheduled_upload_job(): + global preferences_df + print(f"Running scheduled job: Saving and uploading preferences at {datetime.now()}") + if preferences_df is not None and not preferences_df.empty: + utils.save_preferences_to_hf_hub(preferences_df, config.HF_DATASET_REPO_ID, config.RESULTS_CSV_FILE, commit_message="Periodic background update") else: + print("Scheduled job: Preferences DataFrame is empty. Nothing to upload.") + +scheduler = BackgroundScheduler() +scheduler.add_job(scheduled_upload_job, 'interval', hours=config.PUSH_INTERVAL_HOURS) +scheduler.start() +print(f"Scheduler started. Will attempt to upload preferences every {config.PUSH_INTERVAL_HOURS} hour(s).") + + +# --- Core Gradio App Functions --- +def start_new_session(): + """Initializes a new user session.""" + session_id = utils.generate_session_id() + sample_queue = utils.prepare_session_samples(ALL_SAMPLES_BY_DOMAIN, config.SAMPLES_PER_DOMAIN) + current_sample_index = 0 + if not sample_queue: + no_samples_msg = f"# 😥 No Samples Available!\n\n### Please check the data folder configuration or try again later." + return session_id, sample_queue, current_sample_index, no_samples_msg, None, None, None, [], [], True + + print(f"New session started: {session_id}, with {len(sample_queue)} samples.") + domain_prompt_md, bg, fg, s_data, out_imgs, disp_info, end_flag = load_and_display_sample(sample_queue, current_sample_index) + return session_id, sample_queue, current_sample_index, domain_prompt_md, bg, fg, s_data, out_imgs, disp_info, end_flag + + +def load_and_display_sample(sample_queue, current_sample_index): + """Loads and prepares a single sample for display.""" + if not sample_queue or current_sample_index >= len(sample_queue): + end_session_msg = f"# 🎉 All Rated! 🎉\n\n### All samples for this session have been rated. Thank you!" + return end_session_msg, None, None, None, [], [], True # End of session + + domain, sample_id = sample_queue[current_sample_index] + sample_data = utils.load_sample_data(domain, sample_id) + + if sample_data is None: + print(f"Error loading sample {domain}/{sample_id}. Skipping.") + error_msg = f"## ⚠️ Error Loading Sample\n\nCould not load data for {domain}/{sample_id}. Skipping to the next one." + return error_msg, None, None, None, [], [], False + + prompt_text = sample_data["prompt"] + bg_img_path = sample_data["background_img_path"] + fg_img_path = sample_data["foreground_img_path"] + + # Load input bg/fg images without forcing them to be square + # The gr.Image component will handle scaling to the specified height while preserving aspect ratio. + bg_image_to_display = Image.open(bg_img_path) + fg_image_to_display = Image.open(fg_img_path) + + output_model_keys = list(sample_data["output_image_paths"].keys()) + random.shuffle(output_model_keys) + + displayed_models_info = [] + output_images_for_display = [] + + # square_size is still used for output option images + square_size = (config.IMAGE_DISPLAY_SIZE[0], config.IMAGE_DISPLAY_SIZE[0]) + + for model_key in output_model_keys: + img_path = sample_data["output_image_paths"][model_key] try: - session_id = request.client.host - except AttributeError: - print("Error: request.client is None or has no 'host' attribute.") - session_id = "unknown_client" - - if not state or "current_sample_id" not in state: - print("Warning: State missing, cannot record preference.") - return { - choice_button_a: gr.update(interactive=False), - choice_button_b: gr.update(interactive=False), - next_button: gr.update(visible=True, interactive=True), - status_display: gr.update(value="**Status:** Error: Session state lost. Click Next Sample."), - app_state: state - } - - chosen_model_name = state["output_a_model_name"] if choice == "A" else state["output_b_model_name"] - baseline_display = "A" if state["output_a_model_name"] == "baseline" else "B" - tficon_display = "B" if state["output_a_model_name"] == "baseline" else "A" - - new_row = { - "timestamp": datetime.now().isoformat(), - "session_id": session_id, - "sample_id": state["current_sample_id"], - "baseline_displayed_as": baseline_display, - "tficon_displayed_as": tficon_display, - "chosen_display": choice, - "chosen_model_name": chosen_model_name - } - header = list(new_row.keys()) - - try: - with upload_lock: - file_exists = LOCAL_RESULTS_FILE.exists() - mode = 'a' if file_exists else 'w' - with open(LOCAL_RESULTS_FILE, mode, newline='', encoding='utf-8') as f: - writer = csv.DictWriter(f, fieldnames=header) - if not file_exists or os.path.getsize(LOCAL_RESULTS_FILE) == 0: - writer.writeheader() - print(f"Created or wrote header to {LOCAL_RESULTS_FILE}") - writer.writerow(new_row) - print(f"Appended preference for {state['current_sample_id']} to local file.") - new_preferences_recorded_since_last_upload.set() - - except Exception as e: - print(f"Error writing local results file {LOCAL_RESULTS_FILE}: {e}") - return { - choice_button_a: gr.update(interactive=False), - choice_button_b: gr.update(interactive=False), - next_button: gr.update(visible=True, interactive=True), - status_display: gr.update(value=f"**Status:** Error saving preference locally: {e}. Click Next."), - app_state: state - } - - return { - choice_button_a: gr.update(interactive=False), - choice_button_b: gr.update(interactive=False), - next_button: gr.update(visible=True, interactive=True), - status_display: gr.update(value=f"**Status:** Preference recorded (Chose {choice}). Click Next Sample."), - app_state: state - } - -def upload_preferences_to_hub(): - print("Periodic upload check triggered.") - if not hf_api: - print("Upload check skipped: Hugging Face API not available.") - return - - if not new_preferences_recorded_since_last_upload.is_set(): - print("Upload check skipped: No new preferences recorded since last upload.") - return - - with upload_lock: - if not new_preferences_recorded_since_last_upload.is_set(): - print("Upload check skipped (race condition avoided): No new preferences.") - return - - if not LOCAL_RESULTS_FILE.exists() or os.path.getsize(LOCAL_RESULTS_FILE) == 0: - print("Upload check skipped: Local results file is missing or empty.") - new_preferences_recorded_since_last_upload.clear() - return - - try: - print(f"Attempting to upload {LOCAL_RESULTS_FILE} to {DATASET_REPO_ID}/{RESULTS_FILENAME_IN_REPO}") - start_time = time.time() - hf_api.upload_file( - path_or_fileobj=str(LOCAL_RESULTS_FILE), - path_in_repo=RESULTS_FILENAME_IN_REPO, - repo_id=DATASET_REPO_ID, - repo_type="dataset", - commit_message=f"Periodic upload of preferences - {datetime.now().isoformat()}" - ) - end_time = time.time() - print(f"Successfully uploaded preferences. Took {end_time - start_time:.2f} seconds.") - new_preferences_recorded_since_last_upload.clear() + img = Image.open(img_path).resize(square_size) # Output images remain square + output_images_for_display.append(img) + displayed_models_info.append((model_key, img_path)) + except FileNotFoundError: + print(f"Image not found: {img_path} for model {model_key}. Skipping this option.") except Exception as e: - print(f"Error uploading results file: {e}") - -def handle_choice_a(state: dict, request: gr.Request): - return record_preference("A", state, request) - -def handle_choice_b(state: dict, request: gr.Request): - return record_preference("B", state, request) - -with gr.Blocks(title="Image Composition User Study") as demo: - gr.Markdown("# Image Composition User Study") - gr.Markdown( - "> Please look at the input images and the prompt below. " - "Then, compare the two output images (Output A and Output B) and click the button below the one you prefer." + print(f"Error loading or resizing image {img_path}: {e}. Skipping this option.") + + blank_image = Image.new('RGB', square_size, (200, 200, 200)) + while len(output_images_for_display) < 4: + output_images_for_display.append(blank_image) + displayed_models_info.append(("BLANK_SLOT", "N/A")) + + domain_prompt_markdown = f""" +
+ + ### Domain: {domain} + +
+
+
+ + ## Prompt + + ### _"{prompt_text}"_ + +
+ """ + + return ( + domain_prompt_markdown, + bg_image_to_display, # Pass the PIL image directly + fg_image_to_display, # Pass the PIL image directly + sample_data, + output_images_for_display[:4], + displayed_models_info[:4], + False ) - app_state = gr.State({}) - available_samples_state = gr.State(INITIAL_SAMPLE_IDS) - - status_display = gr.Markdown("**Status:** Loading first sample...") +def process_vote(choice_index, session_id, sample_queue, current_sample_index, current_sample_data, displayed_models_info_for_sample): + global preferences_df + + if current_sample_data is None or not displayed_models_info_for_sample or choice_index >= len(displayed_models_info_for_sample): + print("Error: Invalid data for processing vote. Skipping.") + current_sample_index += 1 + if current_sample_index >= len(sample_queue): + error_end_msg = f"# ⚠️ Error Processing Vote ⚠️\n\n### An issue occurred. The session has ended." + return preferences_df, current_sample_index, error_end_msg, None, None, None, [], [], True + else: + next_prompt_md, next_bg, next_fg, next_s_data, next_out_imgs, next_disp_info, next_hide = load_and_display_sample(sample_queue, current_sample_index) + return preferences_df, current_sample_index, next_prompt_md, next_bg, next_fg, next_s_data, next_out_imgs, next_disp_info, next_hide + + domain, sample_id = sample_queue[current_sample_index] + preferred_model_key, _ = displayed_models_info_for_sample[choice_index] + + if preferred_model_key == "BLANK_SLOT": + print("User clicked on a blank slot. Vote not recorded. Please select a valid image.") + _prompt_md, _bg, _fg, _s_data, _out_imgs, _disp_info, _hide = load_and_display_sample(sample_queue, current_sample_index) + return preferences_df, current_sample_index, _prompt_md, _bg, _fg, _s_data, _out_imgs, _disp_info, _hide + + print(f"Session {session_id}: Voted for model '{config.MODEL_DISPLAY_NAMES.get(preferred_model_key, preferred_model_key)}' (key: {preferred_model_key}) for sample {domain}/{sample_id}") + + preferences_df = utils.record_preference( + df=preferences_df, + session_id=session_id, + domain=domain, + sample_id=sample_id, + prompt=current_sample_data["prompt"], + bg_path=current_sample_data["background_img_path"], + fg_path=current_sample_data["foreground_img_path"], + displayed_models_info=displayed_models_info_for_sample, + preferred_model_key=preferred_model_key + ) + + try: + preferences_df.to_csv(config.RESULTS_CSV_FILE, index=False) + print(f"Preferences saved locally to {config.RESULTS_CSV_FILE}") + except Exception as e: + print(f"Error saving preferences locally: {e}") + + current_sample_index += 1 + if current_sample_index >= len(sample_queue): + utils.save_preferences_to_hf_hub(preferences_df, config.HF_DATASET_REPO_ID, config.RESULTS_CSV_FILE, commit_message="Session end update") + final_msg = f"# 🎉 Session Complete! 🎉\n\n### All samples have been rated. Thank you for your participation!" + return preferences_df, current_sample_index, final_msg, None, None, None, [], [], True + + next_prompt_md, next_bg, next_fg, next_s_data, next_out_imgs, next_disp_info, next_hide = load_and_display_sample(sample_queue, current_sample_index) + return preferences_df, current_sample_index, next_prompt_md, next_bg, next_fg, next_s_data, next_out_imgs, next_disp_info, next_hide + + +# --- Gradio UI Definition --- +custom_css = """ +.custom-vote-button { + background-color: #FFA500 !important; /* Light Orange for normal state */ + border-color: #FFA500 !important; /* Light Orange for normal state */ + color: white !important; +} +.custom-vote-button:hover { + background-color: #FF8C00 !important; /* Dark Orange for hover state */ + border-color: #FF8C00 !important; /* Dark Orange for hover state */ + color: white !important; +} +""" + +with gr.Blocks(title=config.APP_TITLE, theme=gr.themes.Soft(primary_hue=gr.themes.colors.blue), css=custom_css) as demo: + session_id_state = gr.State() + sample_queue_state = gr.State([]) + current_sample_index_state = gr.State(0) + current_sample_data_state = gr.State() + displayed_models_info_state = gr.State([]) + preferences_df_state = gr.State(value=preferences_df) + + gr.Markdown(f"# {config.APP_TITLE}") + gr.Markdown(config.APP_DESCRIPTION) - gr.Markdown("## Inputs") - with gr.Row(): - prompt_display = gr.Markdown("**Prompt:** Loading...") with gr.Row(): - with gr.Column(): - gr.Markdown("
Input Background
") - input_bg_display = gr.Image(type="filepath", height=250, width=250, interactive=False, show_label=False) - with gr.Column(): - gr.Markdown("
Input Foreground
") - input_fg_display = gr.Image(type="filepath", height=250, width=250, interactive=False, show_label=False) + start_button = gr.Button("Start New Session / Load First Sample", variant="primary") + + with gr.Row(equal_height=False): + with gr.Column(scale=1): + domain_prompt_info_display = gr.Markdown(value="### Click 'Start New Session' to begin.") + + with gr.Column(scale=2): + with gr.Row(): + input_bg_image_display = gr.Image(label="Input Background", type="pil", height=config.IMAGE_DISPLAY_SIZE[0], interactive=False) + input_fg_image_display = gr.Image(label="Input Foreground", type="pil", height=config.IMAGE_DISPLAY_SIZE[0], interactive=False) gr.Markdown("---") - gr.Markdown("## Choose your preferred output") + gr.Markdown("## Choose your preferred composed image:") + output_image_displays = [] + vote_buttons = [] with gr.Row(): - with gr.Column(): - output_a_display = gr.Image(label="Output A", type="filepath", height=400, width=400, interactive=False) - choice_button_a = gr.Button("Choose Output A", variant="primary") - with gr.Column(): - output_b_display = gr.Image(label="Output B", type="filepath", height=400, width=400, interactive=False) - choice_button_b = gr.Button("Choose Output B", variant="primary") - - next_button = gr.Button("🔁 Next Sample 🔁", visible=False) - - demo.load( - fn=display_new_sample, - inputs=[app_state, available_samples_state], + for i in range(4): + with gr.Column(): + img_display = gr.Image(label=f"Option {i+1}", type="pil", height=config.IMAGE_DISPLAY_SIZE[0], width=config.IMAGE_DISPLAY_SIZE[0], interactive=False) + output_image_displays.append(img_display) + vote_btn = gr.Button(f"Select Option {i+1}", elem_id=f"vote_btn_{i}", elem_classes=["custom-vote-button"]) + vote_buttons.append(vote_btn) + + end_of_session_msg_display = gr.Markdown("", visible=True) + + def handle_start_session(): + s_id, s_queue, s_idx, domain_prompt_or_end_msg, bg, fg, s_data, out_imgs, disp_info, end = start_new_session() + + while len(out_imgs) < 4: out_imgs.append(None) + while len(disp_info) < 4: disp_info.append(("BLANK_SLOT", "N/A")) + + updates = { + session_id_state: s_id, + sample_queue_state: s_queue, + current_sample_index_state: s_idx, + domain_prompt_info_display: domain_prompt_or_end_msg if not end else "", + input_bg_image_display: bg, + input_fg_image_display: fg, + current_sample_data_state: s_data, + displayed_models_info_state: disp_info, + end_of_session_msg_display: domain_prompt_or_end_msg if end else "" + } + for i in range(4): + updates[output_image_displays[i]] = out_imgs[i] if i < len(out_imgs) else None + num_actual_outputs = 0 + if s_data and "output_image_paths" in s_data and s_data["output_image_paths"]: + num_actual_outputs = sum(1 for m_key, _ in disp_info if m_key != "BLANK_SLOT" and m_key is not None) + updates[vote_buttons[i]] = gr.Button(interactive=not end and i < num_actual_outputs) + return updates + + start_button.click( + fn=handle_start_session, + inputs=[], outputs=[ - prompt_display, input_bg_display, input_fg_display, - output_a_display, output_b_display, - choice_button_a, choice_button_b, next_button, status_display, - app_state, available_samples_state + session_id_state, sample_queue_state, current_sample_index_state, + domain_prompt_info_display, + input_bg_image_display, input_fg_image_display, + current_sample_data_state, displayed_models_info_state, end_of_session_msg_display, + *output_image_displays, *vote_buttons ] ) - choice_button_a.click( - fn=handle_choice_a, - inputs=[app_state], - outputs=[choice_button_a, choice_button_b, next_button, status_display, app_state], - api_name=False, - ) + def make_vote_fn(choice_idx): + def vote_action(s_id, s_queue, s_idx, current_s_data, disp_info_for_sample, prefs_df_val): + global preferences_df + preferences_df = prefs_df_val - choice_button_b.click( - fn=handle_choice_b, - inputs=[app_state], - outputs=[choice_button_a, choice_button_b, next_button, status_display, app_state], - api_name=False, - ) - - next_button.click( - fn=display_new_sample, - inputs=[app_state, available_samples_state], - outputs=[ - prompt_display, input_bg_display, input_fg_display, - output_a_display, output_b_display, - choice_button_a, choice_button_b, next_button, status_display, - app_state, available_samples_state - ], - api_name=False, - ) - -def cleanup_temp_dir(): - if Path(TEMP_DIR).exists(): - print(f"Cleaning up temporary directory: {TEMP_DIR}") - shutil.rmtree(TEMP_DIR, ignore_errors=True) - -def shutdown_hook(): - print("Application shutting down. Performing final upload check...") - upload_preferences_to_hub() - if scheduler.running: - print("Shutting down scheduler...") - scheduler.shutdown(wait=False) - cleanup_temp_dir() - print("Shutdown complete.") - -atexit.register(shutdown_hook) + new_prefs_df, new_s_idx, domain_prompt_or_end_msg, bg, fg, new_s_data, out_imgs, new_disp_info, end = process_vote( + choice_idx, s_id, s_queue, s_idx, current_s_data, disp_info_for_sample + ) + + while len(out_imgs) < 4: out_imgs.append(None) + while len(new_disp_info) < 4: new_disp_info.append(("BLANK_SLOT", "N/A")) + + updates = { + preferences_df_state: new_prefs_df, + current_sample_index_state: new_s_idx, + domain_prompt_info_display: domain_prompt_or_end_msg if not end else "", + input_bg_image_display: bg, + input_fg_image_display: fg, + current_sample_data_state: new_s_data, + displayed_models_info_state: new_disp_info, + end_of_session_msg_display: domain_prompt_or_end_msg if end else "" + } + for i in range(4): + updates[output_image_displays[i]] = out_imgs[i] if i < len(out_imgs) else None + num_actual_outputs = 0 + if new_s_data and "output_image_paths" in new_s_data and new_s_data["output_image_paths"]: + num_actual_outputs = sum(1 for m_key, _ in new_disp_info if m_key != "BLANK_SLOT" and m_key is not None) + updates[vote_buttons[i]] = gr.Button(interactive=not end and i < num_actual_outputs) + return updates + return vote_action + + for i, btn in enumerate(vote_buttons): + btn.click( + fn=make_vote_fn(i), + inputs=[ + session_id_state, sample_queue_state, current_sample_index_state, + current_sample_data_state, displayed_models_info_state, preferences_df_state + ], + outputs=[ + preferences_df_state, current_sample_index_state, + domain_prompt_info_display, + input_bg_image_display, input_fg_image_display, + current_sample_data_state, displayed_models_info_state, end_of_session_msg_display, + *output_image_displays, *vote_buttons + ] + ) + + gr.Markdown(config.FOOTER_MESSAGE) if __name__ == "__main__": - initialize_hub_and_results() - - if not INITIAL_SAMPLE_IDS: - print("Error: No valid samples found in the 'data' directory.") - print("Please ensure the 'data' directory exists and contains subdirectories") - print("named like 'sample_id', each with 'prompt.txt', 'input_bg.*',") - print("'input_fg.*', 'baseline.*', and 'tf-icon.*' files.") - elif not DATASET_REPO_ID: - print("Error: DATASET_REPO_ID environment variable is not set or is set to the default placeholder.") - print("Please set the DATASET_REPO_ID environment variable or update the script.") - elif hf_api: - print(f"Starting periodic upload scheduler (every {UPLOAD_INTERVAL_HOURS} hours)...") - scheduler.add_job(upload_preferences_to_hub, 'interval', hours=UPLOAD_INTERVAL_HOURS) - scheduler.start() - print(f"Found {len(INITIAL_SAMPLE_IDS)} samples.") - print(f"Configured to save results periodically to Hugging Face Dataset: {DATASET_REPO_ID}") - print("Starting Gradio app...") - demo.launch(server_name="0.0.0.0") - else: - print("Warning: Running without Hugging Face Hub integration (HF_TOKEN or DATASET_REPO_ID missing/invalid).") - print(f"Found {len(INITIAL_SAMPLE_IDS)} samples.") - print("Starting Gradio app...") - demo.launch(server_name="0.0.0.0") \ No newline at end of file + if not os.path.exists(config.DATA_FOLDER): + print(f"Creating dummy data folder: {config.DATA_FOLDER}") + os.makedirs(config.DATA_FOLDER, exist_ok=True) + + dummy_domains = ["Real-Cartoon", "Real-Painting"] + dummy_model_keys = list(config.MODEL_OUTPUT_IMAGE_NAMES.keys()) + + for domain in dummy_domains: + domain_path = os.path.join(config.DATA_FOLDER, domain) + os.makedirs(domain_path, exist_ok=True) + for i in range(config.SAMPLES_PER_DOMAIN + 2): + sample_id = f"sample_{i:03d}" + sample_path = os.path.join(domain_path, sample_id) + os.makedirs(sample_path, exist_ok=True) + + with open(os.path.join(sample_path, config.PROMPT_FILE_NAME), "w") as f: + f.write(f"This is a dummy prompt for {domain} sample {sample_id}.") + + colors = [(255,0,0), (0,255,0), (0,0,255), (255,255,0), (0,255,255)] + try: + img_bg = Image.new('RGB', config.IMAGE_DISPLAY_SIZE, color='gray') + img_bg.save(os.path.join(sample_path, config.BACKGROUND_IMAGE_NAME)) + + img_fg = Image.new('RGB', config.IMAGE_DISPLAY_SIZE, color='lightgray') + img_fg.save(os.path.join(sample_path, config.FOREGROUND_IMAGE_NAME)) + + for idx, model_key in enumerate(dummy_model_keys): + model_img_name = config.MODEL_OUTPUT_IMAGE_NAMES[model_key] + img_model = Image.new('RGB', config.IMAGE_DISPLAY_SIZE, color=colors[idx % len(colors)]) + img_model.save(os.path.join(sample_path, model_img_name)) + except Exception as e: + print(f"Error creating dummy image: {e}") + print("Dummy data creation complete.") + ALL_SAMPLES_BY_DOMAIN = utils.scan_data_directory(config.DATA_FOLDER) + + demo.launch() + +import atexit +atexit.register(lambda: scheduler.shutdown() if scheduler.running else None) diff --git a/clean_preferences.py b/clean_preferences.py new file mode 100644 index 0000000000000000000000000000000000000000..a63c9d96c9907e4dcac9a08b3593de38d00ac3b3 --- /dev/null +++ b/clean_preferences.py @@ -0,0 +1,104 @@ +import os +import datasets +import pandas as pd +from datetime import datetime + +from config import BACKUP_FOLDER, HF_DATASET_REPO_ID, HF_TOKEN, RESULTS_CSV_FILE, CSV_HEADERS + +def main(): + """ + Gets the dataset from HF Hub where preferences are being collected, + save it locally to a backup folder with a timestamp. + Then creates an empty dataset with the same structure and saves it to the HF Hub. + """ + print(f"Attempting to load dataset '{HF_DATASET_REPO_ID}' from Hugging Face Hub (file: {RESULTS_CSV_FILE})...") + try: + # 1. Get the dataset from HF Hub + # Ensure the token has write permissions for pushing later. + dataset = datasets.load_dataset(HF_DATASET_REPO_ID, data_files=RESULTS_CSV_FILE, token=HF_TOKEN, split='train') + print(f"Successfully loaded dataset. It has {len(dataset)} entries.") + dataset_df = dataset.to_pandas() + except Exception as e: + print(f"Error loading dataset from Hugging Face Hub: {e}") + print("This could be due to the dataset/file not existing, or token issues.") + print("Attempting to proceed by creating an empty structure for backup and remote reset.") + # If loading fails, we might still want to try to clear the remote + # or at least create an empty local backup structure. + dataset_df = pd.DataFrame(columns=CSV_HEADERS) # Use predefined headers + + # 2. Save it locally to a backup folder with a timestamp + if not os.path.exists(BACKUP_FOLDER): + os.makedirs(BACKUP_FOLDER) + print(f"Created backup folder: {BACKUP_FOLDER}") + + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + backup_filename = f"preferences_backup_{timestamp}.csv" + backup_filepath = os.path.join(BACKUP_FOLDER, backup_filename) + + try: + dataset_df.to_csv(backup_filepath, index=False) + print(f"Successfully backed up current preferences (or empty structure) to: {backup_filepath}") + except Exception as e: + print(f"Error saving backup to {backup_filepath}: {e}") + # Decide if to return or continue to try clearing remote + # For now, let's continue to try clearing remote if backup fails + + # 3. Create an empty dataset with the same structure (using config.CSV_HEADERS) + print(f"Creating an empty dataset structure using predefined CSV_HEADERS: {CSV_HEADERS}") + empty_df = pd.DataFrame(columns=CSV_HEADERS) + empty_dataset = datasets.Dataset.from_pandas(empty_df) + + # 4. Save the empty dataset to the HF Hub + print(f"Attempting to push the empty dataset to '{HF_DATASET_REPO_ID}' (file: {RESULTS_CSV_FILE}) on Hugging Face Hub...") + try: + # To push a specific CSV file and overwrite it, we can push a dictionary + # where the key is the name of the file in the repo (without .csv extension if that's how load_dataset names splits) + # or more robustly, save to a local temp CSV and use that path in push_to_hub. + + # Create a DatasetDict. The key 'train' is a common default split name. + # If your dataset on the Hub uses a different split name for this CSV, adjust accordingly. + # Or, if RESULTS_CSV_FILE is the exact filename on the hub, that's what we want to replace. + dataset_dict_to_push = datasets.DatasetDict({"train": empty_dataset}) + + # The push_to_hub for a DatasetDict will typically create Parquet files by default. + # To ensure it's a CSV, we might need to save it locally first and then push that file. + # However, let's try pushing the DatasetDict directly first, as it might handle CSVs + # if the original dataset was loaded as such. + # For more direct control over pushing a CSV file: + temp_empty_csv_path = "_temp_empty_prefs.csv" + empty_df.to_csv(temp_empty_csv_path, index=False) + + # The `push_to_hub` method on a Dataset object itself can be used. + # To ensure it overwrites the correct file, it's often best to structure it as a DatasetDict + # or manage file uploads more directly if the library offers it for specific file types. + + # Let's use a method that's common for replacing a dataset with a new version from a local file. + # We'll upload our temporary empty CSV. + # This requires the `huggingface_hub` library to be installed and logged in. + from huggingface_hub import HfApi + api = HfApi(token=os.getenv("HF_HUB_TOKEN", HF_TOKEN)) + + api.upload_file( + path_or_fileobj=temp_empty_csv_path, + path_in_repo=RESULTS_CSV_FILE, # This should be the path to the CSV file in the repo + repo_id=HF_DATASET_REPO_ID, + repo_type="dataset", + commit_message=f"Reset {RESULTS_CSV_FILE} to empty by script" + ) + + if os.path.exists(temp_empty_csv_path): + os.remove(temp_empty_csv_path) + + print(f"Successfully pushed empty dataset to replace {RESULTS_CSV_FILE} in Hugging Face Hub: {HF_DATASET_REPO_ID}") + print("The remote dataset CSV should now be empty but retain its structure based on CSV_HEADERS.") + print(f"IMPORTANT: The old data (if any) is backed up at {backup_filepath}") + + except Exception as e: + print(f"Error pushing empty dataset to Hugging Face Hub: {e}") + if os.path.exists(temp_empty_csv_path): + os.remove(temp_empty_csv_path) + print("The remote dataset might not have been cleared. Please check the Hugging Face Hub.") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/config.py b/config.py new file mode 100644 index 0000000000000000000000000000000000000000..b69de56b3386a4eb65496496cc509d744180b54c --- /dev/null +++ b/config.py @@ -0,0 +1,64 @@ +# Configuration for the Gradio User Study App + +# --- File and Folder Names --- +DATA_FOLDER = "data" # Main folder containing domain subfolders +BACKGROUND_IMAGE_NAME = "input_bg.jpg" # Standard name for background input +FOREGROUND_IMAGE_NAME = "input_fg.jpg" # Standard name for foreground input +PROMPT_FILE_NAME = "prompt.txt" # Standard name for the prompt file +# Names for the output images from different models. +# These should be actual filenames present in each sample's folder. +MODEL_OUTPUT_IMAGE_NAMES = { + "baseline": "cp_bg_fg.jpg", + "kv-edit": "kvedit.jpg", + "tf-icon": "tf-icon.png", + "dit-editor": "alphanoise0.05_timesteps50_QTrue_KTrue_VTrue_taua0.4_taub0.8_guidance3.0.png", +} +# Names to display for each model in the UI (can be different from filenames) +MODEL_DISPLAY_NAMES = { + "baseline": "Model A", + "kv-edit": "Model B", + "tf-icon": "Model C", + "dit-editor": "Model D", +} + +# --- Data Collection --- +RESULTS_CSV_FILE = "user_preferences.csv" +CSV_HEADERS = [ + "session_id", + "timestamp", + "domain", + "sample_id", + "prompt", + "input_background", + "input_foreground", + "displayed_order_model_1", # To store which model was shown in 1st position + "displayed_order_model_2", # To store which model was shown in 2nd position + "displayed_order_model_3", # To store which model was shown in 3rd position + "displayed_order_model_4", # To store which model was shown in 4th position + "preferred_model_key", # The key of the preferred model (e.g., "model_a") + "preferred_model_filename" # The filename of the preferred image +] +SAMPLES_PER_DOMAIN = 3 # Number of samples to show from each domain per user session + +# --- Hugging Face Hub --- +HF_DATASET_REPO_ID = "matsant01/dit-editor-collected-preferences" # Replace with your actual repo ID +HF_TOKEN = None # Set this if your dataset is private, or use HF_HUB_TOKEN env var +PUSH_INTERVAL_HOURS = 1 # Interval in hours to push results to the Hub + +# --- UI Configuration --- +IMAGE_DISPLAY_SIZE = (300, 300) # (width, height) for displaying images +APP_TITLE = "Image Composition User Study" +APP_DESCRIPTION = """ +Please look at the input foreground and background images, and the text prompt used for generation, then choose the composed image that you prefer the most. +You consider: +* 📸 **subject consistency**: does the subject resemble the one in the foreground image? Or is it just a similar object/animal? +* 🖼️ **background preservation**: is the background image correctly preserved? +* 🎨 **style blending**: is the subject style correctly adapted to the one of the background? +""" +FOOTER_MESSAGE = "Thank you for participating!" + +# --- Other --- +SESSION_ID_LENGTH = 16 # Length of the randomly generated session ID + +# --- Paths --- +BACKUP_FOLDER = "backup" \ No newline at end of file diff --git a/data/Real-Cartoon/sample_0/alphanoise0.05_timesteps50_QTrue_KTrue_VTrue_taua0.4_taub0.8_guidance3.0.png b/data/Real-Cartoon/sample_0/alphanoise0.05_timesteps50_QTrue_KTrue_VTrue_taua0.4_taub0.8_guidance3.0.png new file mode 100644 index 0000000000000000000000000000000000000000..2b9fafe15fe53c2a46edbf1cc2202820cb8a81c3 --- /dev/null +++ b/data/Real-Cartoon/sample_0/alphanoise0.05_timesteps50_QTrue_KTrue_VTrue_taua0.4_taub0.8_guidance3.0.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6ccf1e7287e72dac377184b6a428b11784f0483d06856ecaa093cc88e98491f6 +size 274767 diff --git a/data/Real-Cartoon/sample_0/cp_bg_fg.jpg b/data/Real-Cartoon/sample_0/cp_bg_fg.jpg new file mode 100644 index 0000000000000000000000000000000000000000..439d423c4405c70368e78de3761d51da2d5e4635 --- /dev/null +++ b/data/Real-Cartoon/sample_0/cp_bg_fg.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:090ba7c1b8d543131220c5d4e880cfe2f099aa20584495a464338970feb9b5d2 +size 33187 diff --git a/data/Real-Cartoon/sample_0/input_bg.jpg b/data/Real-Cartoon/sample_0/input_bg.jpg new file mode 100644 index 0000000000000000000000000000000000000000..457136272db0a9cab2d33bd7458e9f8a57bbbe92 --- /dev/null +++ b/data/Real-Cartoon/sample_0/input_bg.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5802404ac6c6819dec0e391fcff62ee6390d087238b1b43299ab32b5861a1236 +size 253141 diff --git a/data/sample_154/input_fg.jpg b/data/Real-Cartoon/sample_0/input_fg.jpg similarity index 100% rename from data/sample_154/input_fg.jpg rename to data/Real-Cartoon/sample_0/input_fg.jpg diff --git a/data/Real-Cartoon/sample_0/kvedit.jpg b/data/Real-Cartoon/sample_0/kvedit.jpg new file mode 100644 index 0000000000000000000000000000000000000000..f887e3a6660817e6bc98d481d1466f3ff7be8914 --- /dev/null +++ b/data/Real-Cartoon/sample_0/kvedit.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:40c3d9d2eaa877f73b29cdd4324b4358832ca24395acd7b29d4a30f15adb807b +size 95811 diff --git a/data/Real-Cartoon/sample_0/prompt.txt b/data/Real-Cartoon/sample_0/prompt.txt new file mode 100644 index 0000000000000000000000000000000000000000..20c191ad3d93aba727418002747290a6a2f91193 --- /dev/null +++ b/data/Real-Cartoon/sample_0/prompt.txt @@ -0,0 +1 @@ +a cartoon animation of a hamburger, a croissant, a piece of bread and a cup of coffee \ No newline at end of file diff --git a/data/Real-Cartoon/sample_0/tf-icon.png b/data/Real-Cartoon/sample_0/tf-icon.png new file mode 100644 index 0000000000000000000000000000000000000000..ccd035fb7a765a7fc65c6df1fb29a905550747d8 --- /dev/null +++ b/data/Real-Cartoon/sample_0/tf-icon.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ba49556a7008d708dc1e2757b3d4c993a670555b305b7eb79162cd147f1995cf +size 300497 diff --git a/data/Real-Cartoon/sample_1/alphanoise0.05_timesteps50_QTrue_KTrue_VTrue_taua0.4_taub0.8_guidance3.0.png b/data/Real-Cartoon/sample_1/alphanoise0.05_timesteps50_QTrue_KTrue_VTrue_taua0.4_taub0.8_guidance3.0.png new file mode 100644 index 0000000000000000000000000000000000000000..b7863cd854a98bdb475c99a18e1d8a1d05fcadc6 --- /dev/null +++ b/data/Real-Cartoon/sample_1/alphanoise0.05_timesteps50_QTrue_KTrue_VTrue_taua0.4_taub0.8_guidance3.0.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:70a3f02be630c9cba8cfffa2a0117e6c347f5df1f709dea39eea7262a9edcd9c +size 267628 diff --git 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430441 diff --git a/requirements.txt b/requirements.txt index 9657890c6400aaebec439763547dcf29cc3fa966..f5a343d7fc608b26c65dabea22dd554099b6e8e5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,5 @@ gradio +pandas huggingface_hub -apscheduler # Added for periodic tasks \ No newline at end of file +Pillow +apscheduler \ No newline at end of file diff --git a/restructure_folder.py b/restructure_folder.py new file mode 100644 index 0000000000000000000000000000000000000000..9bf54dc0d4d37de5548793b2440430c180dd8683 --- /dev/null +++ b/restructure_folder.py @@ -0,0 +1,78 @@ +import os +import re +import shutil + +from config import MODEL_OUTPUT_IMAGE_NAMES + +ROOT_DIR = "benchmark_images_generations" +OUTPUT_DIR = "data/" + +def main(): + for domain in os.listdir(ROOT_DIR): + domain_dir = os.path.join(ROOT_DIR, domain) + for i, sample_dir in enumerate(os.listdir(domain_dir)): + if sample_dir == ".DS_Store": + continue + sample_dir_path = os.path.join(domain_dir, sample_dir) + prompt = sample_dir[4:].strip() + + output_sample_dir = os.path.join(OUTPUT_DIR, domain, f"sample_{i}") + os.makedirs(output_sample_dir, exist_ok=True) + + # --- Look for all the images needed to complete the sample --- + + # Input background + ## look for an image (either .jpg or .png) whose name is composed as "bg{number}" + ## and copy it to the output directory, then rename it to input_bg.jpg + input_bg = None + for file in os.listdir(sample_dir_path): + if re.match(r"bg\d+\.(jpg|png)", file): + input_bg = file + break + if input_bg: + input_bg_path = os.path.join(sample_dir_path, input_bg) + shutil.copy(input_bg_path, os.path.join(output_sample_dir, "input_bg.jpg")) + else: + print(f"Warning: No input background found in {sample_dir_path}. Skipping sample {i}...") + continue + + # Input foreground + ## look for an image (either .jpg or .png) whose name is either composed as "fg_{alphanumeric}.jpg" or "fg_{alphanumeric}.png", + ## or "fg{number}_{alphanumeric}.jpg" or "fg{number}_{alphanumeric}.png", and never contains the word "mask" + ## and copy it to the output directory, then rename it to input_fg.jpg + input_fg = None + for file in os.listdir(sample_dir_path): + if re.match(r"fg(_\w+)?\.(jpg|png)", file) or re.match(r"fg\d+(_\w+)?\.(jpg|png)", file): + if "mask" not in file: + input_fg = file + break + if input_fg: + input_fg_path = os.path.join(sample_dir_path, input_fg) + shutil.copy(input_fg_path, os.path.join(output_sample_dir, "input_fg.jpg")) + else: + print(f"Warning: No input foreground found in {sample_dir_path}. Skipping sample {i}...") + continue + + # Outputs + if any([not os.path.exists(os.path.join(sample_dir_path, image_name)) for image_name in MODEL_OUTPUT_IMAGE_NAMES.values()]): + print(f"Warning: Not all output images found in {sample_dir_path}. Skipping sample {i}...") + # Delete the output directory for this sample + shutil.rmtree(output_sample_dir) + continue + + for _, image_name in MODEL_OUTPUT_IMAGE_NAMES.items(): + image_path = os.path.join(sample_dir_path, image_name) + target_path = os.path.join(output_sample_dir, image_name) + if os.path.exists(image_path): + shutil.copy(image_path, target_path) + else: + print(f"Warning: {image_name} not found in {sample_dir_path}. Skipping...") + + # -- Create the prompt.txt file -- + ## create a file named prompt.txt in the output directory and write the prompt in it + prompt_file_path = os.path.join(output_sample_dir, "prompt.txt") + with open(prompt_file_path, "w") as prompt_file: + prompt_file.write(prompt) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/utils.py b/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1c37c5e054e748c5635fd25d6d045f3307c6c950 --- /dev/null +++ b/utils.py @@ -0,0 +1,209 @@ +\ +import os +import random +import uuid +import pandas as pd +from datetime import datetime +from huggingface_hub import HfApi, hf_hub_download, login +from PIL import Image +import shutil + +import config + +# --- Hugging Face Hub Functions --- +def login_hugging_face(): + """Logs in to Hugging Face Hub using token from config or environment variable.""" + token = config.HF_TOKEN or os.getenv("HF_HUB_TOKEN") + if token: + login(token=token) + print("Successfully logged into Hugging Face Hub.") + else: + print("HF_TOKEN not set in config and HF_HUB_TOKEN not in environment. Proceeding without login. Uploads to private repos will fail.") + +def load_preferences_from_hf_hub(repo_id, filename): + """Downloads the preferences CSV from the Hugging Face Hub dataset repo. + Returns a Pandas DataFrame or None if the file doesn't exist or on error. + """ + try: + downloaded_file_path = hf_hub_download( + repo_id=repo_id, + filename=filename, + repo_type="dataset", + local_dir=".", # Download to current directory, ensure .gitignore if needed + local_dir_use_symlinks=False + ) + # Move file to be just filename, hf_hub_download might place it in a subfolder + if os.path.dirname(downloaded_file_path) != os.path.abspath("."): + destination_path = os.path.join(".", os.path.basename(downloaded_file_path)) + shutil.move(downloaded_file_path, destination_path) + downloaded_file_path = destination_path + + if os.path.exists(downloaded_file_path): + print(f"Successfully downloaded {filename} from {repo_id}") + df = pd.read_csv(downloaded_file_path) + # Ensure local copy is named as expected by config + if downloaded_file_path != filename: + os.rename(downloaded_file_path, filename) + return df + else: # Should not happen if download was successful + print(f"Downloaded file {downloaded_file_path} does not exist locally.") + return None + except Exception as e: + print(f"Could not download {filename} from {repo_id}. Error: {e}") + print("Starting with an empty preferences table or local copy if available.") + if os.path.exists(filename): + print(f"Loading local copy of {filename}") + return pd.read_csv(filename) + return None + +def save_preferences_to_hf_hub(df, repo_id, filename, commit_message="Update preferences"): + """Saves the DataFrame to a local CSV and uploads it to the Hugging Face Hub.""" + if df is None or df.empty: + print("Preferences DataFrame is empty. Nothing to save or upload.") + return + try: + df.to_csv(filename, index=False) + print(f"Preferences saved locally to {filename}") + + api = HfApi() + api.upload_file( + path_or_fileobj=filename, + path_in_repo=filename, + repo_id=repo_id, + repo_type="dataset", + commit_message=commit_message, + ) + print(f"Successfully uploaded {filename} to {repo_id}") + except Exception as e: + print(f"Error saving or uploading {filename} to Hugging Face Hub: {e}") + print("Changes are saved locally. Will attempt upload on next scheduled push.") + +# --- Data Loading and Sampling --- +def scan_data_directory(data_folder): + """ + Scans the data directory to find domains and their samples. + Returns a dictionary: {"domain_name": ["sample_id1", "sample_id2", ...]} + """ + all_samples_by_domain = {} + if not os.path.isdir(data_folder): + print(f"Error: Data folder '{data_folder}' not found.") + return all_samples_by_domain + + for domain_name in os.listdir(data_folder): + domain_path = os.path.join(data_folder, domain_name) + if os.path.isdir(domain_path): + all_samples_by_domain[domain_name] = [] + for sample_id in os.listdir(domain_path): + sample_path = os.path.join(domain_path, sample_id) + # Basic check: ensure it's a directory and contains expected files (e.g., prompt) + prompt_file = os.path.join(sample_path, config.PROMPT_FILE_NAME) + bg_image = os.path.join(sample_path, config.BACKGROUND_IMAGE_NAME) + if os.path.isdir(sample_path) and os.path.exists(prompt_file) and os.path.exists(bg_image): + all_samples_by_domain[domain_name].append(sample_id) + if not all_samples_by_domain[domain_name]: + print(f"Warning: No valid samples found in domain '{domain_name}'.") + if not all_samples_by_domain: + print(f"Warning: No domains found or no valid samples in any domain in '{data_folder}'.") + return all_samples_by_domain + +def prepare_session_samples(all_samples_by_domain, samples_per_domain): + """ + Prepares a list of (domain, sample_id) tuples for a user session. + Randomly selects 'samples_per_domain' from each domain. + The returned list is shuffled. + """ + session_queue = [] + for domain, samples in all_samples_by_domain.items(): + if samples: # only if there are samples in the domain + chosen_samples = random.sample(samples, min(len(samples), samples_per_domain)) + for sample_id in chosen_samples: + session_queue.append((domain, sample_id)) + random.shuffle(session_queue) + return session_queue + +# --- Session and Data Handling --- +def generate_session_id(): + """Generates a unique session ID.""" + return uuid.uuid4().hex[:config.SESSION_ID_LENGTH] + +def load_sample_data(domain, sample_id): + """ + Loads data for a specific sample: prompt, input images, and output image paths. + Returns a dictionary or None if data is incomplete. + """ + sample_path = os.path.join(config.DATA_FOLDER, domain, sample_id) + prompt_path = os.path.join(sample_path, config.PROMPT_FILE_NAME) + bg_image_path = os.path.join(sample_path, config.BACKGROUND_IMAGE_NAME) + fg_image_path = os.path.join(sample_path, config.FOREGROUND_IMAGE_NAME) + + if not all(os.path.exists(p) for p in [prompt_path, bg_image_path, fg_image_path]): + print(f"Error: Missing core files for sample {domain}/{sample_id}") + return None + + try: + with open(prompt_path, 'r', encoding='utf-8') as f: + prompt_text = f.read().strip() + except Exception as e: + print(f"Error reading prompt for {domain}/{sample_id}: {e}") + return None + + output_images = {} # {model_key: path_to_image} + for model_key, img_name in config.MODEL_OUTPUT_IMAGE_NAMES.items(): + img_path = os.path.join(sample_path, img_name) + if os.path.exists(img_path): + output_images[model_key] = img_path + else: + print(f"Warning: Missing output image {img_name} for model {model_key} in sample {domain}/{sample_id}") + # Decide if a sample is invalid if an output is missing, or if it can proceed + # For now, we'll allow it to proceed and it just won't show that option. + # A better approach might be to ensure all 4 are present during data prep. + + if len(output_images) < len(config.MODEL_OUTPUT_IMAGE_NAMES): + print(f"Warning: Sample {domain}/{sample_id} is missing one or more model outputs. It will have fewer than 4 options.") + if not output_images: # No outputs at all + return None + + + return { + "prompt": prompt_text, + "background_img_path": bg_image_path, + "foreground_img_path": fg_image_path, + "output_image_paths": output_images # dict {model_key: path} + } + +def record_preference(df, session_id, domain, sample_id, prompt, bg_path, fg_path, displayed_models_info, preferred_model_key): + """ + Appends a new preference record to the DataFrame. + displayed_models_info: list of (model_key, image_path) in the order they were displayed. + preferred_model_key: The key of the model the user selected (e.g., "model_a"). + """ + timestamp = datetime.now().isoformat() + + # Create a dictionary for the new row + new_row = { + "session_id": session_id, + "timestamp": timestamp, + "domain": domain, + "sample_id": sample_id, + "prompt": prompt, + "input_background": os.path.basename(bg_path), # Storing just filename for brevity + "input_foreground": os.path.basename(fg_path), # Storing just filename for brevity + "preferred_model_key": preferred_model_key, + "preferred_model_filename": config.MODEL_OUTPUT_IMAGE_NAMES.get(preferred_model_key, "N/A") + } + + # Add displayed order; ensure all columns exist even if fewer than 4 models were shown + for i in range(4): # Assuming max 4 display slots + col_name = f"displayed_order_model_{i+1}" + if i < len(displayed_models_info): + new_row[col_name] = displayed_models_info[i][0] # Store model_key + else: + new_row[col_name] = None # Or some placeholder like "EMPTY_SLOT" + + new_df_row = pd.DataFrame([new_row], columns=config.CSV_HEADERS) + + if df is None: + df = new_df_row + else: + df = pd.concat([df, new_df_row], ignore_index=True) + return df