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Grounding en cascade quand CLIP/template échouent : 1. OCR (docTR) → cherche le texte exact sur l'écran (~1s) 2. UI-TARS grounding → "click on X" → coordonnées (~3s, 94% ScreenSpot) 3. VLM reasoning → raisonnement complet + confirmation OCR (~10s) find_element_on_screen() dans input_handler.py (partagé VWB + Léa). Câblé dans find_and_click() et execute_action() comme fallback. Refonte capture écran : - mss.monitors[0] (composite) pour capturer la VM en plein écran - FullscreenSelector réécrit : overlay via getBoundingClientRect() - Bboxes et sélection alignées avec l'image (calcul JS, pas CSS) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
655 lines
24 KiB
Python
655 lines
24 KiB
Python
"""
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Module partagé de saisie texte et gestion des dialogues.
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Utilisé par les deux executors :
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- VWB executor (visual_workflow_builder/backend/api_v3/execute.py)
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- Core executor (core/execution/action_executor.py)
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Garantit le même comportement AZERTY/VM/Citrix partout.
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"""
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import logging
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import subprocess
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import shutil
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import time
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from typing import Any, Dict, List, Optional
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logger = logging.getLogger(__name__)
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try:
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import pyautogui
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PYAUTOGUI_AVAILABLE = True
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except ImportError:
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PYAUTOGUI_AVAILABLE = False
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def safe_type_text(text: str):
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"""Saisie de texte compatible VM/Citrix et claviers AZERTY/QWERTY.
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Priorité :
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1. xdotool type avec refresh layout → traverse les VM spice/QEMU
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2. Presse-papier (xclip) + Ctrl+V → fallback
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3. pyautogui.write() → dernier recours
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"""
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if not text:
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return
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# Méthode 1 : xdotool type avec refresh du layout clavier
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if shutil.which('xdotool') and shutil.which('setxkbmap'):
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try:
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subprocess.run(['setxkbmap', 'fr'], timeout=2)
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subprocess.run(
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['xdotool', 'type', '--delay', '0', '--clearmodifiers', '--', text],
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timeout=max(30, len(text) * 0.05),
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check=True
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)
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logger.debug(f"Saisie via xdotool type ({len(text)} car.)")
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return
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except Exception as e:
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logger.debug(f"xdotool type échoué: {e}")
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# Méthode 2 : Presse-papier
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xclip = shutil.which('xclip')
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if xclip and PYAUTOGUI_AVAILABLE:
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try:
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p = subprocess.Popen(
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['xclip', '-selection', 'clipboard'],
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stdin=subprocess.PIPE,
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stdout=subprocess.DEVNULL,
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stderr=subprocess.DEVNULL
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)
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p.stdin.write(text.encode('utf-8'))
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p.stdin.close()
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time.sleep(0.2)
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pyautogui.hotkey('ctrl', 'v')
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time.sleep(0.3)
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logger.debug(f"Saisie via presse-papier ({len(text)} car.)")
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return
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except Exception as e:
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logger.debug(f"xclip échoué: {e}")
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# Méthode 3 : pyautogui
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if PYAUTOGUI_AVAILABLE:
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logger.warning("Saisie via pyautogui.write() (AZERTY non garanti)")
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pyautogui.write(text, interval=0.02)
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else:
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logger.warning(f"Aucune méthode de saisie disponible pour: {text[:50]}")
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def check_screen_for_patterns() -> Optional[Dict[str, Any]]:
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"""Vérifie si l'écran contient un pattern UI connu (dialogue, popup).
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Capture l'écran, extrait le texte via OCR, et cherche un pattern
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dans la UIPatternLibrary.
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Returns:
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Dict avec le pattern trouvé, ou None.
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"""
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try:
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from core.knowledge.ui_patterns import UIPatternLibrary
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import mss
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from PIL import Image
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lib = UIPatternLibrary()
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with mss.mss() as sct:
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monitor = sct.monitors[1]
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screenshot = sct.grab(monitor)
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screen = Image.frombytes('RGB', screenshot.size, screenshot.bgra, 'raw', 'BGRX')
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try:
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# Essayer docTR d'abord (peut être importé depuis différents chemins)
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try:
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from services.ocr_service import ocr_extract_text
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except ImportError:
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from core.extraction.field_extractor import FieldExtractor
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extractor = FieldExtractor()
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ocr_extract_text = lambda img: extractor.extract_text_from_image(img)
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ocr_text = ocr_extract_text(screen)
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except ImportError:
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logger.debug("OCR non disponible pour pattern check")
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return None
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if not ocr_text or len(ocr_text) < 5:
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return None
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pattern = lib.find_pattern(ocr_text)
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if pattern and pattern['category'] in ('dialog', 'popup'):
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logger.info(f"Pattern UI détecté: {pattern['pattern']} → {pattern['action']} '{pattern['target']}'")
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return pattern
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return None
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except Exception as e:
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logger.debug(f"Pattern check échoué: {e}")
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return None
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def handle_detected_pattern(pattern: Dict[str, Any]) -> bool:
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"""Gère automatiquement un pattern UI détecté.
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Cherche le bouton cible via OCR (position réelle sur l'écran).
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100% vision — zéro coordonnée hardcodée.
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Returns:
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True si le pattern a été géré avec succès.
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"""
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if not PYAUTOGUI_AVAILABLE:
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logger.warning("pyautogui non disponible — impossible de gérer le pattern")
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return False
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action = pattern.get('action')
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target = pattern.get('target', '')
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alternatives = pattern.get('alternatives', [])
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if action == 'click':
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candidates_labels = [target] + alternatives
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try:
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import mss
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from PIL import Image
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# Importer OCR (essayer les deux chemins)
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try:
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from services.ocr_service import ocr_extract_words
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except ImportError:
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from core.extraction.field_extractor import FieldExtractor
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extractor = FieldExtractor()
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def ocr_extract_words(img):
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return extractor.extract_words_from_image(img)
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with mss.mss() as sct:
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monitor = sct.monitors[1]
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screenshot = sct.grab(monitor)
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screen = Image.frombytes('RGB', screenshot.size, screenshot.bgra, 'raw', 'BGRX')
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words = ocr_extract_words(screen)
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# Collecter tous les matchs, prendre le plus bas (bouton = bas du dialogue)
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all_matches = []
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for candidate in candidates_labels:
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candidate_lower = candidate.lower()
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for word in words:
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word_text = word['text'].lower()
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if len(word_text) < 2 or len(candidate_lower) < 2:
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continue
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if word_text == candidate_lower:
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x1, y1, x2, y2 = word['bbox']
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all_matches.append({
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'text': word['text'],
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'x': int((x1 + x2) / 2),
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'y': int((y1 + y2) / 2),
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'match_type': 'exact',
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})
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# Recherche partielle (lettre soulignée manquante)
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if not all_matches:
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for candidate in candidates_labels:
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if len(candidate) > 3:
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partial = candidate[1:].lower()
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for word in words:
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if partial in word['text'].lower():
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x1, y1, x2, y2 = word['bbox']
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all_matches.append({
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'text': word['text'],
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'x': int((x1 + x2) / 2),
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'y': int((y1 + y2) / 2),
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'match_type': 'partial',
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})
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if all_matches:
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best = max(all_matches, key=lambda m: m['y'])
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logger.info(f"Clic sur '{best['text']}' à ({best['x']}, {best['y']})")
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pyautogui.click(best['x'], best['y'])
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time.sleep(1.0)
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return True
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logger.info(f"Bouton '{target}' introuvable par OCR — appel VLM...")
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vlm_result = vlm_reason_about_screen(
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objective=f"Cliquer sur le bouton '{target}'",
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context=f"Un dialogue '{pattern.get('pattern')}' est détecté"
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)
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if vlm_result and vlm_result.get('action') == 'click' and vlm_result.get('target'):
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vlm_target = vlm_result['target']
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for word in words:
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if vlm_target.lower() in word['text'].lower():
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x1, y1, x2, y2 = word['bbox']
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x = int((x1 + x2) / 2)
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y = int((y1 + y2) / 2)
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logger.info(f"VLM → clic sur '{word['text']}' à ({x}, {y})")
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pyautogui.click(x, y)
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time.sleep(1.0)
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return True
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return False
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except Exception as e:
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logger.warning(f"OCR bouton échoué: {e}")
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return False
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elif action == 'hotkey':
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keys = target.split('+')
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logger.info(f"Raccourci automatique: {target}")
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pyautogui.hotkey(*keys)
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time.sleep(0.5)
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return True
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return False
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def vlm_reason_about_screen(objective: str = "", context: str = "") -> Optional[Dict[str, Any]]:
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"""Demande au VLM de raisonner sur l'écran actuel et proposer une action.
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Utilisé quand les réflexes (patterns) ne suffisent pas.
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Le VLM voit l'écran et décide quoi faire.
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Args:
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objective: Ce que Léa essaie de faire (ex: "cliquer sur Enregistrer")
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context: Contexte additionnel (ex: "un dialogue est apparu")
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Returns:
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Dict avec 'action', 'target', 'reasoning' ou None si le VLM ne peut pas aider.
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"""
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try:
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import mss
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import requests
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import json
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import base64
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import io
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import os
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from PIL import Image
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with mss.mss() as sct:
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monitor = sct.monitors[1]
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screenshot = sct.grab(monitor)
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screen = Image.frombytes('RGB', screenshot.size, screenshot.bgra, 'raw', 'BGRX')
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buffer = io.BytesIO()
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screen.save(buffer, format='JPEG', quality=70)
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image_b64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
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prompt = f"""Analyse cet écran et dis-moi quoi faire.
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Objectif : {objective or "Interagir avec l'interface visible"}
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Contexte : {context or "Aucun contexte supplémentaire"}
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Réponds en JSON strict :
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{{
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"action": "click" ou "type" ou "wait" ou "nothing",
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"target": "texte exact du bouton ou champ à cliquer",
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"reasoning": "explication courte de ton choix"
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}}
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Si tu vois un dialogue ou une popup, indique quel bouton cliquer.
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Si l'écran est normal sans action nécessaire, réponds action="nothing".
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Réponds UNIQUEMENT le JSON, pas d'explication."""
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from core.cognition.vram_orchestrator import get_orchestrator
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orch = get_orchestrator()
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orch.ensure_reasoning_ready()
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ollama_url = os.environ.get("OLLAMA_URL", "http://localhost:11434")
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model = os.environ.get("RPA_REASONING_MODEL", "qwen2.5vl:7b")
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response = requests.post(
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f"{ollama_url}/api/generate",
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json={
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"model": model,
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"prompt": prompt,
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"images": [image_b64],
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"stream": False,
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"options": {"temperature": 0.1, "num_predict": 200}
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},
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timeout=30
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)
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if response.status_code != 200:
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logger.warning(f"VLM reasoning failed: HTTP {response.status_code}")
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return None
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result = response.json()
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text = result.get('response', '').strip()
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import re
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match = re.search(r'\{[\s\S]*\}', text)
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if match:
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parsed = json.loads(match.group())
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logger.info(f"VLM reasoning: {parsed.get('action')} '{parsed.get('target')}' — {parsed.get('reasoning', '')[:80]}")
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return parsed
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logger.debug(f"VLM response not parseable: {text[:100]}")
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return None
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except Exception as e:
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logger.debug(f"VLM reasoning failed: {e}")
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return None
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def find_element_on_screen(
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target_text: str,
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target_description: str = "",
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anchor_image_base64: Optional[str] = None,
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) -> Optional[Dict[str, Any]]:
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"""
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Cherche un élément sur l'écran en utilisant 3 méthodes en cascade.
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Niveau 1 — OCR (rapide, ~1s) : docTR pour trouver le texte exact
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Niveau 2 — UI-TARS grounding (~3s) : modèle GUI spécialisé
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Niveau 3 — VLM reasoning (~10s) : raisonnement + OCR de confirmation
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Args:
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target_text: Texte de l'élément à trouver (ex: "Demo", "Enregistrer")
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target_description: Description plus longue (ex: "le dossier Demo sur le bureau")
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anchor_image_base64: Image de référence de l'ancre (pour CLIP matching, réservé futur)
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Returns:
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{'x': int, 'y': int, 'method': str, 'confidence': float} ou None
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"""
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if not target_text and not target_description:
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logger.debug("find_element_on_screen: ni target_text ni target_description fournis")
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return None
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search_label = target_description or target_text
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logger.info(f"[Grounding] Recherche élément: '{search_label}' (cascade 3 niveaux)")
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# ─── Niveau 1 — OCR (rapide, ~1s) ───
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result = _grounding_ocr(target_text)
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if result:
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return result
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# ─── Niveau 2 — UI-TARS grounding (~3s) ───
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result = _grounding_ui_tars(target_text, target_description)
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if result:
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return result
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# ─── Niveau 3 — VLM reasoning (~10s) ───
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result = _grounding_vlm(target_text, target_description)
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if result:
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return result
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logger.warning(f"[Grounding] ÉCHEC total pour '{search_label}' — aucune méthode n'a trouvé l'élément")
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return None
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def _capture_screen():
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"""Capture l'écran principal et retourne (PIL.Image, width, height)."""
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try:
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import mss
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from PIL import Image as PILImage
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with mss.mss() as sct:
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monitor = sct.monitors[1]
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screenshot = sct.grab(monitor)
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screen = PILImage.frombytes('RGB', screenshot.size, screenshot.bgra, 'raw', 'BGRX')
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return screen, monitor['width'], monitor['height']
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except Exception as e:
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logger.debug(f"Capture écran échouée: {e}")
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return None, 0, 0
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def _grounding_ocr(target_text: str) -> Optional[Dict[str, Any]]:
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"""Niveau 1 — Cherche le texte par OCR (docTR). ~1s."""
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if not target_text:
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return None
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try:
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screen, screen_w, screen_h = _capture_screen()
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if screen is None:
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return None
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# Importer OCR (essayer les deux chemins)
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try:
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from services.ocr_service import ocr_extract_words
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except ImportError:
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from core.extraction.field_extractor import FieldExtractor
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extractor = FieldExtractor()
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def ocr_extract_words(img):
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return extractor.extract_words_from_image(img)
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words = ocr_extract_words(screen)
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if not words:
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logger.debug("[Grounding/OCR] Aucun mot détecté")
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return None
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target_lower = target_text.lower()
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# Matching exact insensible à la casse
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for word in words:
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if word['text'].lower() == target_lower:
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x1, y1, x2, y2 = word['bbox']
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x = int((x1 + x2) / 2)
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y = int((y1 + y2) / 2)
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logger.info(f"[Grounding/OCR] Trouvé '{word['text']}' à ({x}, {y}) — match exact")
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return {'x': x, 'y': y, 'method': 'ocr', 'confidence': 0.95}
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# Matching partiel (mot coupé : "nregistrer" pour "Enregistrer")
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for word in words:
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word_lower = word['text'].lower()
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if len(word_lower) < 3 or len(target_lower) < 3:
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continue
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# Le mot OCR contient le target (ou l'inverse)
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if target_lower in word_lower or word_lower in target_lower:
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x1, y1, x2, y2 = word['bbox']
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x = int((x1 + x2) / 2)
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y = int((y1 + y2) / 2)
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logger.info(f"[Grounding/OCR] Trouvé '{word['text']}' à ({x}, {y}) — match partiel")
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return {'x': x, 'y': y, 'method': 'ocr', 'confidence': 0.80}
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# Matching partiel lettre initiale manquante (soulignée ou coupée)
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if len(target_lower) > 3:
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partial = target_lower[1:]
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for word in words:
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if partial in word['text'].lower():
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x1, y1, x2, y2 = word['bbox']
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x = int((x1 + x2) / 2)
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y = int((y1 + y2) / 2)
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logger.info(f"[Grounding/OCR] Trouvé '{word['text']}' à ({x}, {y}) — match partiel (lettre initiale manquante)")
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return {'x': x, 'y': y, 'method': 'ocr', 'confidence': 0.70}
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logger.debug(f"[Grounding/OCR] '{target_text}' non trouvé parmi {len(words)} mots")
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return None
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except Exception as e:
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logger.debug(f"[Grounding/OCR] Erreur: {e}")
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return None
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def _grounding_ui_tars(target_text: str, target_description: str = "") -> Optional[Dict[str, Any]]:
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"""Niveau 2 — UI-TARS grounding visuel (~3s)."""
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try:
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import requests
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import base64
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import io
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import re
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import os
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screen, screen_w, screen_h = _capture_screen()
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if screen is None:
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return None
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# Encoder le screenshot en base64
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buffer = io.BytesIO()
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screen.save(buffer, format='JPEG', quality=70)
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image_b64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
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# Construire le prompt pour UI-TARS
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click_target = target_description or target_text
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prompt = f"click on {click_target}"
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ollama_url = os.environ.get("OLLAMA_URL", "http://localhost:11434")
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model = "0000/ui-tars-1.5-7b-q8_0:7b"
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logger.info(f"[Grounding/UI-TARS] Envoi à {model}: '{prompt}'")
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response = requests.post(
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f"{ollama_url}/api/generate",
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json={
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"model": model,
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"prompt": prompt,
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"images": [image_b64],
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"stream": False,
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"options": {"temperature": 0.1, "num_predict": 50}
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},
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timeout=30
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)
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if response.status_code != 200:
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logger.warning(f"[Grounding/UI-TARS] HTTP {response.status_code}")
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return None
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result = response.json()
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text = result.get('response', '').strip()
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logger.debug(f"[Grounding/UI-TARS] Réponse brute: {text[:200]}")
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# Parser les coordonnées de UI-TARS
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coords = _parse_ui_tars_coordinates(text, screen_w, screen_h)
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if coords:
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x, y = coords
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# Valider que les coordonnées sont dans l'écran
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if 0 <= x <= screen_w and 0 <= y <= screen_h:
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logger.info(f"[Grounding/UI-TARS] Grounding → ({x}, {y})")
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return {'x': x, 'y': y, 'method': 'ui_tars', 'confidence': 0.85}
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else:
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logger.warning(f"[Grounding/UI-TARS] Coordonnées hors écran: ({x}, {y}) pour {screen_w}x{screen_h}")
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return None
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logger.debug(f"[Grounding/UI-TARS] Pas de coordonnées parsées dans: {text[:100]}")
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return None
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except Exception as e:
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logger.debug(f"[Grounding/UI-TARS] Erreur: {e}")
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return None
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def _parse_ui_tars_coordinates(text: str, screen_w: int, screen_h: int) -> Optional[tuple]:
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"""Parse les coordonnées retournées par UI-TARS.
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UI-TARS peut retourner :
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- Coordonnées normalisées (0-1000) : "click at (500, 300)"
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- Coordonnées en pixels : "click at (960, 540)"
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- Format (x, y) ou [x, y] ou x,y
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- Format "Action: click\nCoordinate: (500, 300)" ou "[500, 300]"
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Returns:
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(x_pixel, y_pixel) ou None
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"""
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import re
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|
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# Chercher des patterns de coordonnées
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patterns = [
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r'Coordinate:\s*\[?\(?\s*(\d+(?:\.\d+)?)\s*,\s*(\d+(?:\.\d+)?)\s*\)?\]?',
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r'click\s+(?:at\s+)?\[?\(?\s*(\d+(?:\.\d+)?)\s*,\s*(\d+(?:\.\d+)?)\s*\)?\]?',
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r'\(\s*(\d+(?:\.\d+)?)\s*,\s*(\d+(?:\.\d+)?)\s*\)',
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r'\[\s*(\d+(?:\.\d+)?)\s*,\s*(\d+(?:\.\d+)?)\s*\]',
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]
|
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|
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for pattern in patterns:
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match = re.search(pattern, text, re.IGNORECASE)
|
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if match:
|
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raw_x = float(match.group(1))
|
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raw_y = float(match.group(2))
|
|
|
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# UI-TARS utilise souvent des coordonnées normalisées 0-1000
|
|
if raw_x <= 1000 and raw_y <= 1000 and (raw_x > 1 or raw_y > 1):
|
|
# Probablement normalisées sur 1000
|
|
x = int(raw_x * screen_w / 1000)
|
|
y = int(raw_y * screen_h / 1000)
|
|
elif raw_x <= 1.0 and raw_y <= 1.0:
|
|
# Normalisées 0-1
|
|
x = int(raw_x * screen_w)
|
|
y = int(raw_y * screen_h)
|
|
else:
|
|
# Pixels directs
|
|
x = int(raw_x)
|
|
y = int(raw_y)
|
|
|
|
return (x, y)
|
|
|
|
return None
|
|
|
|
|
|
def _grounding_vlm(target_text: str, target_description: str = "") -> Optional[Dict[str, Any]]:
|
|
"""Niveau 3 — VLM reasoning + confirmation OCR (~10s)."""
|
|
try:
|
|
search_label = target_description or target_text
|
|
|
|
vlm_result = vlm_reason_about_screen(
|
|
objective=f"Cliquer sur {search_label}",
|
|
context=f"Je cherche l'élément '{target_text}' sur l'écran pour cliquer dessus"
|
|
)
|
|
|
|
if not vlm_result:
|
|
logger.debug("[Grounding/VLM] VLM n'a pas retourné de résultat")
|
|
return None
|
|
|
|
if vlm_result.get('action') != 'click' or not vlm_result.get('target'):
|
|
logger.debug(f"[Grounding/VLM] VLM action={vlm_result.get('action')}, pas un clic")
|
|
return None
|
|
|
|
vlm_target = vlm_result['target']
|
|
logger.info(f"[Grounding/VLM] VLM suggère de cliquer sur: '{vlm_target}'")
|
|
|
|
# Confirmation par OCR : chercher le target VLM sur l'écran
|
|
screen, screen_w, screen_h = _capture_screen()
|
|
if screen is None:
|
|
return None
|
|
|
|
try:
|
|
try:
|
|
from services.ocr_service import ocr_extract_words
|
|
except ImportError:
|
|
from core.extraction.field_extractor import FieldExtractor
|
|
extractor = FieldExtractor()
|
|
def ocr_extract_words(img):
|
|
return extractor.extract_words_from_image(img)
|
|
|
|
words = ocr_extract_words(screen)
|
|
|
|
vlm_target_lower = vlm_target.lower()
|
|
for word in words:
|
|
if vlm_target_lower in word['text'].lower() or word['text'].lower() in vlm_target_lower:
|
|
x1, y1, x2, y2 = word['bbox']
|
|
x = int((x1 + x2) / 2)
|
|
y = int((y1 + y2) / 2)
|
|
logger.info(f"[Grounding/VLM] Confirmé par OCR: '{word['text']}' à ({x}, {y})")
|
|
return {'x': x, 'y': y, 'method': 'vlm', 'confidence': 0.75}
|
|
|
|
logger.debug(f"[Grounding/VLM] Target VLM '{vlm_target}' non trouvé par OCR")
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.debug(f"[Grounding/VLM] OCR de confirmation échoué: {e}")
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.debug(f"[Grounding/VLM] Erreur: {e}")
|
|
return None
|
|
|
|
|
|
def post_execution_cleanup(execution_mode: str = 'debug'):
|
|
"""Vérifie l'écran après exécution et gère les dialogues restants.
|
|
|
|
Appelé après la dernière étape d'un workflow pour laisser l'écran propre.
|
|
"""
|
|
if execution_mode not in ('intelligent', 'debug'):
|
|
return
|
|
|
|
logger.info("Vérification écran final...")
|
|
time.sleep(1.0)
|
|
for _ in range(3):
|
|
detected = check_screen_for_patterns()
|
|
if detected:
|
|
logger.info(f"Dialogue résiduel détecté: {detected.get('pattern')}")
|
|
handle_detected_pattern(detected)
|
|
time.sleep(1.0)
|
|
else:
|
|
vlm_result = vlm_reason_about_screen(
|
|
objective="Vérifier que l'écran est propre après l'exécution",
|
|
context="Le workflow vient de se terminer"
|
|
)
|
|
if vlm_result and vlm_result.get('action') in ('click', 'type'):
|
|
logger.info(f"VLM post-workflow: {vlm_result.get('action')} '{vlm_result.get('target')}'")
|
|
break
|