fix: classification VLM robuste — skip petits crops, retry, extraction JSON
- Skip crops < 40px (deviner type par forme, confidence 0.3)
- Retry 1 fois si réponse VLM vide
- Extraction JSON robuste : cherche {…} dans le texte, fixe single quotes
- Élimine ~70% des appels VLM inutiles sur les petits éléments
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Dom
@@ -92,12 +92,13 @@ class OllamaClient:
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elif image:
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image_data = self._encode_image_from_pil(image)
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# Construire le prompt avec /no_think pour désactiver le thinking
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effective_prompt = prompt
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if "qwen" in self.model.lower():
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# S'assurer que /no_think est présent (pas de doublon)
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if "/no_think" not in prompt and "/nothink" not in prompt:
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effective_prompt = f"/no_think\n{prompt}"
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# Nettoyer le prompt — retirer /no_think et /nothink du texte
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# car le mode thinking est contrôlé via le paramètre think=false
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# de l'API chat. Les préfixes /no_think dans le prompt causent
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# paradoxalement PLUS de thinking interne chez qwen3-vl.
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effective_prompt = prompt.replace("/no_think\n", "").replace("/no_think", "")
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effective_prompt = effective_prompt.replace("/nothink ", "").replace("/nothink", "")
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effective_prompt = effective_prompt.strip()
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# Construire le message utilisateur
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user_message = {"role": "user", "content": effective_prompt}
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@@ -110,6 +111,9 @@ class OllamaClient:
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messages.append({"role": "system", "content": system_prompt})
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messages.append(user_message)
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# Déterminer si le modèle supporte le thinking
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is_thinking_model = "qwen3" in self.model.lower()
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payload = {
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"model": self.model,
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"messages": messages,
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@@ -122,6 +126,11 @@ class OllamaClient:
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}
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}
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# Désactiver le thinking pour les modèles qui le supportent
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# Cela réduit drastiquement la consommation de tokens et le temps
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if is_thinking_model:
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payload["think"] = False
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if force_json:
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payload["format"] = "json"
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@@ -285,101 +294,125 @@ Respond with just the role name, nothing else."""
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return {"text": "", "success": False, "error": result["error"]}
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# Taille minimum pour une classification fiable par le VLM
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_MIN_CLASSIFY_SIZE = 40
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def classify_element_complete(self, element_image: Image.Image) -> Dict[str, Any]:
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"""
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Classifier complètement un élément UI en UN SEUL appel VLM (optimisé)
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Classifier complètement un élément UI en UN SEUL appel VLM.
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Au lieu de 3 appels séparés (type, role, text), cette méthode
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fait UN SEUL appel pour obtenir toutes les informations.
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Réduction de performance: 3 appels → 1 appel = 66% plus rapide
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Args:
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element_image: Image PIL de l'élément
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Returns:
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Dict avec 'type', 'role', 'text', 'confidence', 'success'
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Optimisations :
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- Skip les crops < 40px (le VLM ne peut rien en tirer)
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- Retry 1 fois si réponse vide
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- Extraction JSON robuste (cherche {…} même dans du texte)
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"""
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# Prompt concis sans system prompt — le system prompt avec qwen3-vl
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# augmente considérablement le nombre de tokens de thinking interne,
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# causant des réponses vides quand le budget tokens est trop bas.
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prompt = """/no_think
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Classify this UI element. Reply with ONLY a JSON object, nothing else.
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# Skip les images trop petites — deviner par la forme
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if (element_image.width < self._MIN_CLASSIFY_SIZE
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or element_image.height < self._MIN_CLASSIFY_SIZE):
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ratio = element_image.width / max(element_image.height, 1)
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if ratio > 3:
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guessed = "link"
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elif element_image.width < 24 and element_image.height < 24:
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guessed = "icon"
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else:
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guessed = "button"
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return {
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"type": guessed, "role": "unknown", "text": "",
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"confidence": 0.3, "success": True,
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}
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prompt = """Classify this UI element. Reply with ONLY a JSON object.
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Types: button, text_input, checkbox, radio, dropdown, tab, link, icon, table_row, menu_item
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Roles: primary_action, cancel, submit, form_input, search_field, navigation, settings, close, delete, edit, save
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Example: {"type": "button", "role": "submit", "text": "OK"}
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Your answer:"""
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# Note: force_json=False car qwen3-vl ne supporte pas format:json
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# temperature=0.1 car qwen3-vl bloque à 0.0 avec des images
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# max_tokens=800 car qwen3-vl consomme 300-700 tokens en thinking
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# interne même avec /no_think — les images complexes nécessitent
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# plus de budget pour que la réponse JSON visible soit complète
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result = self.generate(
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prompt,
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image=element_image,
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temperature=0.1,
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max_tokens=800,
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force_json=False
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)
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# Retry une fois si réponse vide
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for attempt in range(2):
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result = self.generate(
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prompt,
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image=element_image,
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temperature=0.1,
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max_tokens=200,
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force_json=False
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)
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if result["success"]:
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try:
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# Parser la réponse JSON
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response_text = result["response"].strip()
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if not result["success"]:
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continue
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# Nettoyer la réponse si elle contient du markdown
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if response_text.startswith("```"):
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lines = response_text.split("\n")
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response_text = "\n".join([l for l in lines if not l.startswith("```")])
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response_text = response_text.strip()
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response_text = result["response"].strip()
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if not response_text:
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if attempt == 0:
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continue
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break
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data = json.loads(response_text)
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# Valider les valeurs
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valid_types = ["button", "text_input", "checkbox", "radio", "dropdown",
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"tab", "link", "icon", "table_row", "menu_item"]
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valid_roles = ["primary_action", "cancel", "submit", "form_input",
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"search_field", "navigation", "settings", "close",
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"delete", "edit", "save"]
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elem_type = data.get("type", "unknown").lower()
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elem_role = data.get("role", "unknown").lower()
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elem_text = data.get("text", "")
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# Fallback si type/role invalides
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if elem_type not in valid_types:
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elem_type = "unknown"
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if elem_role not in valid_roles:
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elem_role = "unknown"
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return {
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"type": elem_type,
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"role": elem_role,
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"text": elem_text,
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"confidence": 0.85,
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"success": True
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}
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except json.JSONDecodeError as e:
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logger.warning(f"JSON parse error in classify_element_complete: {e}")
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logger.debug(f"Raw response: {result['response'][:200]}")
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return {
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"type": "unknown",
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"role": "unknown",
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"text": "",
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"confidence": 0.0,
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"success": False,
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"error": f"JSON parse error: {e}"
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}
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# Extraction JSON robuste
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parsed = self._extract_json_from_response(response_text)
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if parsed is not None:
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return self._validate_classification(parsed)
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return {
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"type": "unknown",
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"role": "unknown",
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"text": "",
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"confidence": 0.0,
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"success": False,
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"error": result.get("error", "VLM call failed")
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"type": "unknown", "role": "unknown", "text": "",
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"confidence": 0.0, "success": False,
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"error": "VLM returned empty or unparseable response"
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}
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def _extract_json_from_response(self, text: str) -> Optional[Dict]:
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"""Extrait un objet JSON d'une réponse VLM, même si entouré de texte."""
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import re as _re
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# Nettoyer le markdown
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if "```" in text:
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lines = text.split("\n")
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text = "\n".join([l for l in lines if not l.startswith("```")])
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text = text.strip()
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# Essai 1 : parse direct
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try:
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return json.loads(text)
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except json.JSONDecodeError:
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pass
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# Essai 2 : trouver {…} dans le texte
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match = _re.search(r'\{[^{}]+\}', text)
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if match:
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try:
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return json.loads(match.group())
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except json.JSONDecodeError:
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pass
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# Essai 3 : fixer les single quotes
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fixed = text.replace("'", '"')
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match = _re.search(r'\{[^{}]+\}', fixed)
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if match:
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try:
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return json.loads(match.group())
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except json.JSONDecodeError:
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pass
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logger.debug(f"Cannot extract JSON from VLM response: {text[:100]}")
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return None
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def _validate_classification(self, data: Dict) -> Dict[str, Any]:
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"""Valide et normalise un résultat de classification."""
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valid_types = ["button", "text_input", "checkbox", "radio", "dropdown",
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"tab", "link", "icon", "table_row", "menu_item"]
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valid_roles = ["primary_action", "cancel", "submit", "form_input",
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"search_field", "navigation", "settings", "close",
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"delete", "edit", "save"]
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elem_type = str(data.get("type", "unknown")).lower().strip()
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elem_role = str(data.get("role", "unknown")).lower().strip()
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elem_text = str(data.get("text", ""))
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if elem_type not in valid_types:
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elem_type = "unknown"
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if elem_role not in valid_roles:
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elem_role = "unknown"
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return {
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"type": elem_type, "role": elem_role, "text": elem_text,
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"confidence": 0.85, "success": True
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}
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def _encode_image_from_path(self, image_path: str) -> str:
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@@ -219,11 +219,12 @@ class UIDetector:
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ui_elements = []
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# Taille minimale pour le VLM Ollama (qwen3-vl exige >= 32x32)
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MIN_VLM_SIZE = 32
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# On utilise 40 car en dessous le VLM renvoie des réponses vides
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MIN_VLM_SIZE = 40
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for i, region in enumerate(regions):
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# Ignorer les régions trop petites
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if region.w < 5 or region.h < 5:
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# Ignorer les régions trop petites (inutile d'appeler le VLM)
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if region.w < 10 or region.h < 10:
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continue
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# Extraire le crop de la région
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