mirror of
https://github.com/Ladebeze66/llm_ticket3.git
synced 2025-12-13 10:46:51 +01:00
224 lines
8.2 KiB
Plaintext
224 lines
8.2 KiB
Plaintext
import os
|
|
import json
|
|
import logging
|
|
from datetime import datetime
|
|
from typing import Optional
|
|
|
|
from ..base_agent import BaseAgent
|
|
from ..utils.pipeline_logger import sauvegarder_donnees
|
|
from utils.ocr_cleaner import clean_text_with_profiles # AJOUT
|
|
from utils.ocr_utils import extraire_texte_fr
|
|
|
|
|
|
logger = logging.getLogger("AgentVisionOCR")
|
|
|
|
class AgentVisionOCR(BaseAgent):
|
|
"""
|
|
Agent LlamaVision qui extrait du texte (OCR avancé) depuis une image.
|
|
"""
|
|
|
|
def __init__(self, llm):
|
|
super().__init__("AgentVisionOCR", llm)
|
|
|
|
# Configuration des paramètres du modèle
|
|
self.params = {
|
|
"stream": False,
|
|
"seed": 0,
|
|
#"stop_sequence": [],
|
|
"temperature": 1.3,
|
|
#"reasoning_effort": 0.5,
|
|
#"logit_bias": {},
|
|
"mirostat": 0,
|
|
"mirostat_eta": 0.1,
|
|
"mirostat_tau": 5.0,
|
|
"top_k": 35,
|
|
"top_p": 0.85,
|
|
"min_p": 0.06,
|
|
"frequency_penalty": 0.15,
|
|
"presence_penalty": 0.1,
|
|
"repeat_penalty": 1.15,
|
|
"repeat_last_n": 128,
|
|
"tfs_z": 1.0,
|
|
"num_keep": 0,
|
|
"num_predict": 2048,
|
|
"num_ctx": 16384,
|
|
#"repeat_penalty": 1.1,
|
|
"num_batch": 2048,
|
|
#"mmap": True,
|
|
#"mlock": False,
|
|
#"num_thread": 4,
|
|
#"num_gpu": 1
|
|
}
|
|
|
|
# Prompt OCR optimisé
|
|
self.system_prompt = ("""You are tasked with performing an exhaustive OCR extraction on a technical or administrative web interface screenshot.
|
|
|
|
GOAL: Extract **every legible piece of text**, even partially visible, faded, or cropped. Structure your output for clarity. Do not guess, but always report what is visible.
|
|
|
|
FORMAT USING THESE CATEGORIES:
|
|
|
|
1. PAGE STRUCTURE
|
|
- Page titles
|
|
- Interface headers or section labels
|
|
- Navigation bars or visible URLs
|
|
|
|
2. IDENTIFIERS & DATA
|
|
- Operator or user names
|
|
- Sample IDs, test references
|
|
- Materials, dates, batch numbers
|
|
|
|
3. INTERFACE ELEMENTS (MANDATORY SCAN)
|
|
- Button labels (e.g., RAZ, SAVE)
|
|
- Tabs (e.g., MATERIAL, OBSERVATIONS)
|
|
- Sidebars, form field labels
|
|
|
|
4. SYSTEM MESSAGES
|
|
- Connection or server errors
|
|
- Domains, IP addresses, server notices
|
|
|
|
5. METADATA
|
|
- Standard references (e.g., "NF EN ####-#")
|
|
- Version numbers, document codes, timestamps
|
|
|
|
6. UNCLEAR / CROPPED TEXT
|
|
- Logos, partial lines (use “[...]” for truncated)
|
|
- Background/faded elements, labels not fully legible
|
|
|
|
RULES:
|
|
- Preserve punctuation, case, accents exactly.
|
|
- Include duplicates if text appears more than once.
|
|
- Never skip faint or partial text; use “[...]” if incomplete.
|
|
- Even if cropped, report as much as possible from any UI region.
|
|
|
|
This prompt is designed to generalize across all web portals, technical forms, or reports. Prioritize completeness over certainty. Do not ignore UI components or system messages.
|
|
""")
|
|
|
|
self._configurer_llm()
|
|
self.resultats = []
|
|
self.images_traitees = set()
|
|
logger.info("AgentVisionOCR initialisé avec prompt amélioré.")
|
|
|
|
def _configurer_llm(self):
|
|
if hasattr(self.llm, "prompt_system"):
|
|
self.llm.prompt_system = self.system_prompt
|
|
if hasattr(self.llm, "configurer"):
|
|
self.llm.configurer(**self.params)
|
|
|
|
def _extraire_ticket_id(self, image_path):
|
|
if not image_path:
|
|
return "UNKNOWN"
|
|
segments = image_path.replace('\\', '/').split('/')
|
|
for segment in segments:
|
|
if segment.startswith('T') and segment[1:].isdigit():
|
|
return segment
|
|
if segment.startswith('ticket_T') and segment[8:].isdigit():
|
|
return 'T' + segment[8:]
|
|
return "UNKNOWN"
|
|
|
|
def executer(self, image_path: str, ocr_baseline: str = "", ticket_id: Optional[str] = None) -> dict:
|
|
image_path_abs = os.path.abspath(image_path)
|
|
image_name = os.path.basename(image_path)
|
|
|
|
if image_path_abs in self.images_traitees:
|
|
logger.warning(f"[OCR-LLM] Image déjà traitée, ignorée: {image_name}")
|
|
print(f" AgentVisionOCR: Image déjà traitée, ignorée: {image_name}")
|
|
return {
|
|
"extracted_text": "DUPLICATE - Already processed",
|
|
"image_name": image_name,
|
|
"image_path": image_path_abs,
|
|
"ticket_id": ticket_id or self._extraire_ticket_id(image_path),
|
|
"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
|
|
"source_agent": self.nom,
|
|
"is_duplicate": True
|
|
}
|
|
|
|
self.images_traitees.add(image_path_abs)
|
|
logger.info(f"[OCR-LLM] Extraction OCR sur {image_name}")
|
|
print(f" AgentVisionOCR: Extraction OCR sur {image_name}")
|
|
|
|
ticket_id = ticket_id or self._extraire_ticket_id(image_path)
|
|
|
|
try:
|
|
if not os.path.exists(image_path):
|
|
raise FileNotFoundError(f"Image introuvable: {image_path}")
|
|
|
|
if not hasattr(self.llm, "interroger_avec_image"):
|
|
raise RuntimeError("Le modèle ne supporte pas l'analyse d'images.")
|
|
# Etape 1: OCR br
|
|
# Interroger le modèle
|
|
response = self.llm.interroger_avec_image(image_path, self.system_prompt)
|
|
|
|
if not response or "i cannot" in response.lower():
|
|
raise ValueError("Réponse vide ou invalide du modèle")
|
|
|
|
cleaned_text = clean_text_with_profiles(response.strip(), active_profiles=("ocr",))
|
|
|
|
model_name = getattr(self.llm, "pipeline_normalized_name",
|
|
getattr(self.llm, "modele", "llama3-vision-90b-instruct"))
|
|
model_name = model_name.replace(".", "-").replace(":", "-").replace("_", "-")
|
|
|
|
result = {
|
|
"extracted_text": cleaned_text,
|
|
"image_name": image_name,
|
|
"image_path": image_path_abs,
|
|
"ticket_id": ticket_id,
|
|
"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
|
|
"source_agent": self.nom,
|
|
"model_info": {
|
|
"model": model_name,
|
|
**self.params
|
|
}
|
|
}
|
|
|
|
self.resultats.append(result)
|
|
logger.info(f"[OCR-LLM] OCR réussi ({len(cleaned_text)} caractères) pour {image_name}")
|
|
return result
|
|
|
|
except Exception as e:
|
|
error_result = {
|
|
"extracted_text": "",
|
|
"image_name": image_name,
|
|
"image_path": image_path_abs,
|
|
"ticket_id": ticket_id or "UNKNOWN",
|
|
"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
|
|
"source_agent": self.nom,
|
|
"error": str(e),
|
|
"model_info": {
|
|
"model": getattr(self.llm, "pipeline_normalized_name", "llama3-vision-90b-instruct"),
|
|
**self.params
|
|
}
|
|
}
|
|
self.resultats.append(error_result)
|
|
logger.error(f"[OCR-LLM] Erreur OCR pour {image_name}: {e}")
|
|
return error_result
|
|
|
|
def sauvegarder_resultats(self, ticket_id: str = "T11143") -> None:
|
|
if not self.resultats:
|
|
logger.warning("[OCR-LLM] Aucun résultat à sauvegarder")
|
|
return
|
|
|
|
resultats_dedupliques = {}
|
|
for resultat in self.resultats:
|
|
image_path = resultat.get("image_path")
|
|
if not image_path:
|
|
continue
|
|
if image_path not in resultats_dedupliques or \
|
|
resultat.get("timestamp", "") > resultats_dedupliques[image_path].get("timestamp", ""):
|
|
resultats_dedupliques[image_path] = resultat
|
|
|
|
resultats_finaux = list(resultats_dedupliques.values())
|
|
|
|
try:
|
|
logger.info(f"[OCR-LLM] Sauvegarde de {len(resultats_finaux)} résultats")
|
|
sauvegarder_donnees(
|
|
ticket_id=ticket_id,
|
|
step_name="ocr_llm",
|
|
data=resultats_finaux,
|
|
base_dir=None,
|
|
is_resultat=True
|
|
)
|
|
self.resultats = []
|
|
self.images_traitees = set()
|
|
except Exception as e:
|
|
logger.error(f"[OCR-LLM] Erreur sauvegarde résultats: {e}")
|