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from __future__ import annotations |
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import logging |
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import os |
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from pathlib import Path |
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from typing import List, Optional |
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import gradio as gr |
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from dotenv import load_dotenv |
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from db import ( |
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configure_database, |
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create_score, |
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ensure_user, |
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get_global_top, |
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get_image_top, |
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get_user_recent, |
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init_db, |
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normalize_username, |
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scores_to_rows, |
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session_scope, |
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validate_username, |
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) |
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from model import ClipScorer, ImageEntry, load_image_entries |
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load_dotenv() |
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logging.basicConfig(level=logging.INFO) |
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logger = logging.getLogger("app") |
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DATABASE_URL = os.getenv("DATABASE_URL") |
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if not DATABASE_URL: |
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raise RuntimeError("DATABASE_URL ist nicht gesetzt. Bitte in den Space-Secrets hinterlegen.") |
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configure_database(DATABASE_URL) |
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init_db() |
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IMAGE_ENTRIES: List[ImageEntry] = [] |
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SCORER: Optional[ClipScorer] = None |
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EMBEDDING_ERROR: Optional[str] = None |
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try: |
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IMAGE_ENTRIES = load_image_entries(Path("images.csv")) |
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except Exception as exc: |
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EMBEDDING_ERROR = f"images.csv konnte nicht geladen werden: {exc}" |
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logger.exception("Fehler beim Laden der images.csv", exc_info=exc) |
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if EMBEDDING_ERROR is None and IMAGE_ENTRIES: |
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try: |
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SCORER = ClipScorer() |
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SCORER.load_precomputed_embeddings(IMAGE_ENTRIES) |
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except Exception as exc: |
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EMBEDDING_ERROR = ( |
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"Embeddings konnten nicht geladen werden. Bitte precompute_embeddings.py ausführen." |
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f"\nFehler: {exc}" |
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) |
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logger.exception("Fehler beim Laden der Embeddings", exc_info=exc) |
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APP_READY = EMBEDDING_ERROR is None |
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HELP_TEXT = ( |
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"Beschreibe das angezeigte Bild in 3 bis 500 Zeichen. " |
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"Die KI vergleicht deine Beschreibung mit der (theoretisch) perfekten Prompt für das angezeigte Bild. " |
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"Der Score reicht von 0 (gar nicht passend) bis 1000 (perfekte Übereinstimmung)." |
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) |
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LEADERBOARD_HEADERS = [ |
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"Platz", |
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"Benutzername", |
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"Bild-ID", |
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"Score", |
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"Ähnlichkeit", |
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"Text", |
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"Zeitstempel", |
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] |
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def fetch_global_rows() -> List[List[object]]: |
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with session_scope() as session: |
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scores = get_global_top(session) |
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return scores_to_rows(scores, include_rank=True) |
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def fetch_image_rows(image_id: str) -> List[List[object]]: |
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if not image_id: |
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return [] |
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with session_scope() as session: |
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scores = get_image_top(session, image_id) |
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return scores_to_rows(scores, include_rank=True) |
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def fetch_user_rows(username: str) -> List[List[object]]: |
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if not username: |
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return [] |
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canonical = normalize_username(username) |
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with session_scope() as session: |
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scores = get_user_recent(session, canonical) |
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return scores_to_rows(scores, include_rank=True) |
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def handle_score(username: str, text: str, image_index: int | None): |
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if not APP_READY or SCORER is None or not IMAGE_ENTRIES: |
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raise gr.Error( |
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"Embeddings sind nicht verfügbar. Bitte vor dem Start precompute_embeddings.py ausführen." |
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) |
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username_clean = (username or "").strip() |
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if not validate_username(username_clean): |
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raise gr.Error("Ungültiger Benutzername. Erlaubt sind 3-20 Zeichen aus A-Z, a-z, 0-9, _.-") |
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text_clean = (text or "").strip() |
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if len(text_clean) < 3: |
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raise gr.Error("Bitte gib mindestens 3 Zeichen Text ein.") |
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if len(text_clean) > 500: |
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raise gr.Error("Der Beschreibungstext darf höchstens 500 Zeichen enthalten.") |
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if image_index is None: |
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image_index = 0 |
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if image_index < 0 or image_index >= len(IMAGE_ENTRIES): |
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image_index = 0 |
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entry = IMAGE_ENTRIES[image_index] |
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similarity, score = SCORER.score_text_for_image(text_clean, entry.image_id) |
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with session_scope() as session: |
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user = ensure_user(session, username_clean) |
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create_score( |
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session, |
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user=user, |
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image_id=entry.image_id, |
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score_value=score, |
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similarity=similarity, |
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text=text_clean, |
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) |
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global_rows = fetch_global_rows() |
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image_rows = fetch_image_rows(entry.image_id) |
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user_rows = fetch_user_rows(username_clean) |
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gr.Info("Score gespeichert!") |
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return ( |
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gr.update(value=score), |
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gr.update(value=round(similarity, 4)), |
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gr.update(value=global_rows), |
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gr.update(value=image_rows), |
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gr.update(value=user_rows), |
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) |
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def handle_next_image(current_index: int | None): |
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if not IMAGE_ENTRIES: |
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raise gr.Error("Keine Bilder konfiguriert.") |
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if current_index is None: |
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current_index = 0 |
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new_index = (current_index + 1) % len(IMAGE_ENTRIES) |
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entry = IMAGE_ENTRIES[new_index] |
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image_rows = fetch_image_rows(entry.image_id) |
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return ( |
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new_index, |
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gr.update(value=entry.image_url), |
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gr.update(value=f"**Bild-ID:** {entry.image_id}"), |
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gr.update(value=entry.image_id), |
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gr.update(value=image_rows), |
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) |
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def handle_image_dropdown(image_id: str): |
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rows = fetch_image_rows(image_id) |
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return gr.update(value=rows) |
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def handle_username_change(username: str): |
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if not username: |
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return gr.update(value=[]) |
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username_clean = username.strip() |
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if not validate_username(username_clean): |
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gr.Warning("Benutzername ungültig. Zeige keine Ergebnisse.") |
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return gr.update(value=[]) |
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rows = fetch_user_rows(username_clean) |
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return gr.update(value=rows) |
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def build_interface() -> gr.Blocks: |
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status_message = "" |
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if EMBEDDING_ERROR: |
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status_message = f"⚠️ {EMBEDDING_ERROR}" |
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elif not IMAGE_ENTRIES: |
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status_message = "⚠️ Keine Bilder konfiguriert." |
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else: |
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status_message = "✅ Bereit zum Scoren!" |
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initial_index = 0 if IMAGE_ENTRIES else None |
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initial_entry = IMAGE_ENTRIES[0] if IMAGE_ENTRIES else None |
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global_rows = fetch_global_rows() if APP_READY else [] |
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image_rows = fetch_image_rows(initial_entry.image_id) if initial_entry else [] |
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image_choices = [entry.image_id for entry in IMAGE_ENTRIES] |
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with gr.Blocks(title="KI Prompt Challenge", theme=gr.themes.Soft()) as demo: |
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gr.Markdown("# KI Prompt Challenge") |
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gr.Markdown(HELP_TEXT) |
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gr.Markdown(status_message) |
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image_state = gr.State(initial_index) |
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with gr.Row(): |
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with gr.Column(scale=3): |
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image_component = gr.Image( |
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value=initial_entry.image_url if initial_entry else None, |
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label="Bild", |
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show_download_button=False, |
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) |
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image_info = gr.Markdown( |
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f"**Bild-ID:** {initial_entry.image_id}" if initial_entry else "Kein Bild geladen." |
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) |
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next_button = gr.Button( |
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"Nächstes Bild", |
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variant="secondary", |
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interactive=bool(IMAGE_ENTRIES), |
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) |
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with gr.Column(scale=2): |
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username_input = gr.Textbox( |
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label="Benutzername", |
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placeholder="3-20 Zeichen (A-Z, a-z, 0-9, _.-)", |
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) |
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text_input = gr.Textbox( |
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label="Beschreibungstext", |
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placeholder="Was siehst du auf dem Bild?", |
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lines=5, |
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) |
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score_button = gr.Button( |
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"Scoren", |
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variant="primary", |
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interactive=APP_READY and bool(IMAGE_ENTRIES), |
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) |
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score_output = gr.Number(label="Score", value=0, precision=0) |
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similarity_output = gr.Number(label="Ähnlichkeit", value=0.0, precision=4) |
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gr.Markdown("### Leaderboard") |
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with gr.Tabs(): |
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with gr.Tab("Top 50"): |
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global_df = gr.Dataframe( |
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headers=LEADERBOARD_HEADERS, |
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value=global_rows, |
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datatype=[ |
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"number", |
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"str", |
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"str", |
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"number", |
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"number", |
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"str", |
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"str", |
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], |
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interactive=False, |
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wrap=True, |
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) |
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with gr.Tab("Dieses Bild Top 50"): |
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image_dropdown = gr.Dropdown( |
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choices=image_choices, |
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value=initial_entry.image_id if initial_entry else None, |
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label="Bild auswählen", |
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interactive=bool(image_choices), |
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) |
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image_df = gr.Dataframe( |
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headers=LEADERBOARD_HEADERS, |
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value=image_rows, |
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datatype=[ |
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"number", |
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"str", |
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"str", |
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"number", |
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"number", |
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"str", |
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"str", |
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], |
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interactive=False, |
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wrap=True, |
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) |
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with gr.Tab("Meine letzten 50"): |
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user_df = gr.Dataframe( |
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headers=LEADERBOARD_HEADERS, |
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value=[], |
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datatype=[ |
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"number", |
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"str", |
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"str", |
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"number", |
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"number", |
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"str", |
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"str", |
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], |
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interactive=False, |
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wrap=True, |
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) |
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next_button.click( |
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handle_next_image, |
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inputs=[image_state], |
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outputs=[image_state, image_component, image_info, image_dropdown, image_df], |
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) |
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score_button.click( |
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handle_score, |
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inputs=[username_input, text_input, image_state], |
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outputs=[score_output, similarity_output, global_df, image_df, user_df], |
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) |
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image_dropdown.change(handle_image_dropdown, inputs=[image_dropdown], outputs=[image_df]) |
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username_input.change(handle_username_change, inputs=[username_input], outputs=[user_df]) |
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return demo |
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demo = build_interface() |
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if __name__ == "__main__": |
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demo.launch() |
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