From 6cdf3bb5a7ff8d7b3d47e8d5fb7938f31b5cd6c4 Mon Sep 17 00:00:00 2001
From: Fernando-Emanuel Donea <fernandodonea10@gmail.com>
Date: Mon, 11 May 2026 11:41:08 +0300
Subject: [PATCH 01/10] feat: Integrare Google Gemini API

---
 ai/llm_config.py | 43 ++++++++++++++++++++++++++++++++-----------
 requirements.txt |  2 ++
 2 files changed, 34 insertions(+), 11 deletions(-)

diff --git a/ai/llm_config.py b/ai/llm_config.py
index cbaefbd..7b9f723 100644
--- a/ai/llm_config.py
+++ b/ai/llm_config.py
@@ -30,9 +30,15 @@
 
 import logging
 import os
+from dotenv import load_dotenv
 
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_ollama import ChatOllama
 
+# Încarcă variabilele de mediu din fișierul .env, dacă există
+load_dotenv()
+
 logger = logging.getLogger(__name__)
 
 # ─── Defaults (match docker-compose.yml & .env.example) ─────────────
@@ -40,6 +46,8 @@
 _DEFAULT_OLLAMA_BASE_URL = "http://localhost:11434"
 _DEFAULT_REQUEST_TIMEOUT = 120.0  # seconds — OCR docs can be big
 
+_DEFAULT_GOOGLE_MODEL = "gemini-2.0-flash"
+
 # Model registry — one entry per Modelfile
 MODEL_CLASSIFIER = "ck-model"  # ai/Modelfile
 MODEL_EXTRACTOR = "ck-extractor"  # ai/Modelfile.extractor
@@ -56,35 +64,48 @@ def get_llm(
     temperature: float | None = None,
     num_ctx: int | None = None,
     timeout: float | None = None,
-) -> ChatOllama:
-    """Return a ChatOllama instance for the requested model.
+) -> BaseChatModel:
+    """Return a chat model instance based on chosen AI_PROVIDER.
 
     Parameters
     ----------
     model : str
-        Ollama model name. Use the module constants:
-        ``MODEL_CLASSIFIER`` (default) or ``MODEL_EXTRACTOR``.
+        Ollama model name or placeholder. For Google, uses the
+        defined DEFAULT_GOOGLE_MODEL override.
     temperature : float, optional
-        Override sampling temperature (model default: 0.1).
+        Override sampling temperature (default: 0.1).
     num_ctx : int, optional
-        Override context-window size.
+        Override context-window size (Ollama only).
     timeout : float, optional
         Override HTTP request timeout (default: 120 s).
 
     Returns
     -------
-    ChatOllama
+    BaseChatModel
         A LangChain chat-model instance ready for ``.invoke()`` /
         ``.ainvoke()`` / agent binding.
     """
     provider = os.getenv("AI_PROVIDER", _DEFAULT_PROVIDER).lower()
 
     if provider == "google":
-        # Future: return ChatGoogleGenerativeAI(...)
-        raise NotImplementedError(
-            "Google AI provider is not yet implemented. "
-            "Set AI_PROVIDER=ollama or leave unset."
+        api_key = os.getenv("GOOGLE_API_KEY")
+        if not api_key:
+            raise ValueError("GOOGLE_API_KEY is required when AI_PROVIDER='google'")
+
+        google_model = os.getenv("GOOGLE_MODEL_NAME", _DEFAULT_GOOGLE_MODEL)
+        temp = temperature if temperature is not None else 0.1
+
+        logger.info(
+            "Initializing ChatGoogleGenerativeAI  model=%s",
+            google_model,
+        )
+
+        g_llm = ChatGoogleGenerativeAI(
+            model=google_model,
+            temperature=temp,
+            google_api_key=api_key,
         )
+        return g_llm
 
     if provider != "ollama":
         raise ValueError(
diff --git a/requirements.txt b/requirements.txt
index 7aee05c..77527ac 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -11,3 +11,5 @@ ruff
 fpdf
 python-docx
 Pillow
+langchain-google-genai
+python-dotenv

From c5ad492d11026e8b9f4e1c2eb966c60451544cb9 Mon Sep 17 00:00:00 2001
From: Fernando-Emanuel Donea <fernandodonea10@gmail.com>
Date: Mon, 11 May 2026 11:44:00 +0300
Subject: [PATCH 02/10] fix: Restore ai/agent_decider.py content

---
 ai/agent_decider.py | 219 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 219 insertions(+)

diff --git a/ai/agent_decider.py b/ai/agent_decider.py
index e69de29..55addd8 100644
--- a/ai/agent_decider.py
+++ b/ai/agent_decider.py
@@ -0,0 +1,219 @@
+"""
+Decider Agent — ai/agent_decider.py
+
+Agent 2: Primește rezumatul (A1) și regula (A0) și decide ce face cu fișierul.
+Uses PydanticOutputParser with the class ActionDecision(status, suggested_name, suggested_folder).
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+from typing import Any, Literal
+
+from langchain_core.output_parsers import PydanticOutputParser, StrOutputParser
+from langchain_core.prompts import PromptTemplate, ChatPromptTemplate
+from pydantic import BaseModel, Field, field_validator
+
+from ai.llm_config import get_llm, MODEL_CLASSIFIER
+from ai.agent_compiler import CompiledRule
+
+logger = logging.getLogger(__name__)
+
+_MAX_RETRIES = 2
+
+# =====================================================================
+#  Pydantic schema — formatul deciziei
+# =====================================================================
+
+
+class ActionDecision(BaseModel):
+    """Decizia finală luată de Agent 2."""
+
+    status: Literal["move", "quarantine"] = Field(
+        ...,
+        description="Statusul deciziei: 'move' dacă fișierul corespunde regulii, 'quarantine' dacă nu sau dacă informațiile lipsesc.",
+    )
+    suggested_name: str = Field(
+        ...,
+        description="Numele sugerat pentru fișier (conform naming_convention din regulă). Dacă e carantină, se păstrează numele original.",
+    )
+    suggested_folder: str = Field(
+        ...,
+        description="Folderul de destinație. Dacă e 'quarantine', valoarea va fi 'Quarantine'.",
+    )
+
+    @field_validator("suggested_name")
+    @classmethod
+    def sanitize_filename(cls, v: str) -> str:
+        """Asigură-te că numele fișierului nu conține caractere invalide."""
+        # Îndepărtăm caracterele care ar putea cauza erori de filepath
+        sanitized = re.sub(r'[<>:"/\\|?*]', "_", v)
+        return sanitized
+
+
+# =====================================================================
+#  Prompt template
+# =====================================================================
+
+_SYSTEM_PROMPT_TEMPLATE = """\
+You are an expert decision-making agent for the ClutterKill system.
+Your job is to analyze a document summary and a set of organization rules, and decide if the document should be moved to the correct folder or placed in quarantine.
+
+Rule Category: {rule_category}
+Target Folder: {rule_folder}
+Naming Convention: {rule_naming}
+
+Document Summary:
+{document_summary}
+
+Original Filename: {original_filename}
+
+Instructions:
+1. If the Document Summary MATCHES the Rule Category, your status must be "move".
+2. If it DOES NOT match, or if you are unsure, your status must be "quarantine".
+3. Calculate the new filename based on the Naming Convention. If the naming convention includes {{original_filename}}, replace it with the actual original filename.
+4. If the status is "quarantine", the folder must be "Quarantine".
+5. If the status is "quarantine", the suggested_name MUST be exactly the Original Filename.
+
+IMPORTANT: You must return ONLY the raw JSON object containing the actual values. Do NOT return a JSON schema, and do NOT wrap your answer in markdown fences (like ```json).
+
+{format_instructions}
+"""
+
+_REPAIR_PROMPT = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            "You previously attempted to output a JSON decision but your JSON was invalid or did not match the schema. "
+            "Fix the JSON below so it matches the required schema exactly. "
+            "Output ONLY valid JSON, nothing else.",
+        ),
+        ("human", "Broken output:\n{broken_json}\n\nValidation error:\n{error}"),
+    ]
+)
+
+# =====================================================================
+#  DeciderAgent
+# =====================================================================
+
+
+class DeciderAgent:
+    """Agent that decides whether a file matches a rule based on its summary.
+
+    Usage::
+
+        agent = DeciderAgent()
+        decision = agent.decide(summary, original_filename, rule)
+        print(decision.model_dump_json())
+    """
+
+    def __init__(self, llm: Any | None = None) -> None:
+        self._llm = llm or get_llm(model=MODEL_CLASSIFIER)
+        self._parser = PydanticOutputParser(pydantic_object=ActionDecision)
+
+        self._prompt = PromptTemplate(
+            template=_SYSTEM_PROMPT_TEMPLATE,
+            input_variables=[
+                "rule_category",
+                "rule_folder",
+                "rule_naming",
+                "document_summary",
+                "original_filename",
+            ],
+            partial_variables={
+                "format_instructions": self._parser.get_format_instructions()
+            },
+        )
+
+        self._chain = self._prompt | self._llm | StrOutputParser()
+        self._repair_chain = _REPAIR_PROMPT | self._llm | StrOutputParser()
+
+    # ── public API ───────────────────────────────────────────────────
+
+    def decide(
+        self, summary: str, original_filename: str, rule: CompiledRule
+    ) -> ActionDecision:
+        """Decide the fate of a document.
+
+        Parameters
+        ----------
+        summary : str
+            Technical summary extracted from the document.
+        original_filename : str
+            The original name of the file.
+        rule : CompiledRule
+            The compiled rule containing category, folder, and naming.
+
+        Returns
+        -------
+        ActionDecision
+            Validated Pydantic model with status, suggested_name, and suggested_folder.
+        """
+        logger.info(
+            "DeciderAgent: Evaluăm documentul '%s' față de regula '%s'",
+            original_filename,
+            rule.category,
+        )
+
+        raw_output = self._chain.invoke(
+            {
+                "rule_category": rule.category,
+                "rule_folder": rule.folder_structure,
+                "rule_naming": rule.naming_convention,
+                "document_summary": summary,
+                "original_filename": original_filename,
+            }
+        )
+
+        last_error: Exception | None = None
+        current_output = raw_output
+
+        for attempt in range(_MAX_RETRIES + 1):
+            try:
+                # Funcția self._parser.parse suportă markdown fences fallback din LangChain
+                return self._parser.parse(current_output)
+            except Exception as exc:
+                last_error = exc
+                logger.warning(
+                    "Parse attempt %d/%d failed: %s",
+                    attempt + 1,
+                    _MAX_RETRIES + 1,
+                    exc,
+                )
+                if attempt < _MAX_RETRIES:
+                    current_output = self._repair_chain.invoke(
+                        {"broken_json": current_output, "error": str(exc)}
+                    )
+
+        logger.error("Eroare în timpul deciziei (după retry-uri): %s", last_error)
+        raise ValueError(
+            f"Failed to parse decision after retries: {last_error}"
+        ) from last_error
+
+
+# ─── Quick self-test ─────────────────────────────────────────────────
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+
+    agent = DeciderAgent()
+
+    test_rule = CompiledRule(
+        category="factură",
+        folder_structure="Facturi_Luna_Curenta",
+        naming_convention="factura_enel_10/20.pdf",  # intenționat cu caractere invalide pentru test
+    )
+
+    test_summary_match = "Emitent: ENEL SA, Dată: 12.05.2023, Sumă: 150 RON, Tip: Factură energie electrică."
+
+    test_filename = "doc_scanned_123.pdf"
+
+    print(f"\n{'=' * 60}")
+    print("TEST 1: Sanitizare și Retry")
+    try:
+        decision1 = agent.decide(test_summary_match, test_filename, test_rule)
+        print("Output JSON (observă cum / a fost înlocuit):")
+        print(decision1.model_dump_json(indent=2))
+    except Exception as e:
+        print(f"Error: {e}")
+    print(f"{'=' * 60}\n")

From c40f3d74d548dabbaca6166e5672ce736a756f84 Mon Sep 17 00:00:00 2001
From: Fernando-Emanuel Donea <fernandodonea10@gmail.com>
Date: Mon, 11 May 2026 11:44:45 +0300
Subject: [PATCH 03/10] feat: Add core ScanWorker for AI pipeline orchestration
 (Task 21)

---
 core/scan_worker.py | 166 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 166 insertions(+)
 create mode 100644 core/scan_worker.py

diff --git a/core/scan_worker.py b/core/scan_worker.py
new file mode 100644
index 0000000..d1684af
--- /dev/null
+++ b/core/scan_worker.py
@@ -0,0 +1,166 @@
+import logging
+from pathlib import Path
+
+from PyQt6.QtCore import QThread, pyqtSignal
+
+from ai.agent_compiler import CompilerAgent
+from ai.agent_extractor import ExtractorAgent
+from ai.agent_decider import DeciderAgent
+from ai.tools import extract_text_from_pdf, extract_text_from_image
+from core.quarantine_db import quarantine_db
+
+logger = logging.getLogger(__name__)
+
+
+class ScanWorker(QThread):
+    """
+    Thread real de scanare care folosește pipeline-ul de agenți AI:
+    1. Agent 0 (Compiler) transformă regula naturală.
+    2. Agent 1 (Extractor) citește fișierul și scoate un rezumat tehnic.
+    3. Agent 2 (Decider) aplică regula pe rezumat pentru o decizie de rutare.
+    4. Adaugă fiecare fișier în quarantine_db cu recomandările AI.
+    """
+
+    progress_updated = pyqtSignal(int)
+    log_updated = pyqtSignal(str)
+    scan_finished = pyqtSignal(int)
+
+    def __init__(self, source_dir: str, dest_dir: str, user_rule: str):
+        super().__init__()
+        self.source_dir = Path(source_dir)
+        self.dest_dir = Path(dest_dir)
+        self.user_rule = user_rule
+
+    def run(self):
+        # 1. Inițializăm agenții
+        self.log_updated.emit("🤖 Se încarcă agenții AI...")
+        try:
+            compiler = CompilerAgent()
+            extractor = ExtractorAgent()
+            decider = DeciderAgent()
+        except Exception as e:
+            self.log_updated.emit(f"❌ Eroare la inițializarea agenților: {e}")
+            self.scan_finished.emit(0)
+            return
+
+        # 2. Compilăm regula
+        if not self.user_rule.strip():
+            self.log_updated.emit("⚠️ Regula nu a fost completată!")
+            self.scan_finished.emit(0)
+            return
+
+        self.log_updated.emit(f"🧠 Compilare regulă: '{self.user_rule}'")
+        try:
+            compiled_rule = compiler.compile(self.user_rule)
+            self.log_updated.emit(
+                f"✅ Regulă compilată:\n{compiled_rule.model_dump_json(indent=2)}"
+            )
+        except Exception as e:
+            self.log_updated.emit(f"❌ Eroare la compilarea regulii: {e}")
+            self.scan_finished.emit(0)
+            return
+
+        # 3. Preluăm fișierele din sursă
+        files = [f for f in self.source_dir.rglob("*") if f.is_file()]
+        total = len(files)
+
+        if total == 0:
+            self.log_updated.emit("⚠️ Niciun fișier găsit în folderul sursă.")
+            self.scan_finished.emit(0)
+            return
+
+        self.log_updated.emit(f"🔍 {total} fișiere găsite. Se începe scanarea cu AI...")
+
+        added_count = 0
+        skipped_count = 0
+
+        existing_paths = {r["original_path"] for r in quarantine_db.get_all()}
+
+        for i, file_path in enumerate(files):
+            str_path = str(file_path)
+
+            if str_path in existing_paths:
+                skipped_count += 1
+                self.log_updated.emit(f"⏭️ {file_path.name} — deja în carantină, skip")
+            else:
+                self.log_updated.emit(f"📄 Procesare: {file_path.name}...")
+
+                # a. Extragere text
+                text = ""
+                ext = file_path.suffix.lower()
+                try:
+                    if ext == ".pdf":
+                        text = extract_text_from_pdf(file_path)
+                    elif ext in [".png", ".jpg", ".jpeg", ".bmp", ".tiff"]:
+                        text = extract_text_from_image(file_path)
+                    elif ext in [".txt", ".csv", ".md"]:
+                        text = file_path.read_text(errors="ignore")
+                    else:
+                        text = f"Fișier de tip necunoscut ({ext}). Conținut text nedisponibil."
+                except Exception as e:
+                    logger.warning(
+                        f"Eroare extragere text pentru {file_path.name}: {e}"
+                    )
+                    text = f"Eroare extracție: {e}"
+
+                # b. Agent 1 (Extragere)
+                try:
+                    extraction_result = extractor.extract(
+                        text or "Conținut gol sau necitibil"
+                    )
+                    summary = extraction_result.get_technical_summary()
+                except Exception as e:
+                    logger.error(f"Eroare ExtractorAgent: {e}")
+                    summary = f"Eroare procesare text: {e}"
+
+                # c. Agent 2 (Decizie)
+                try:
+                    decision = decider.decide(summary, file_path.name, compiled_rule)
+
+                    if decision.status == "move":
+                        proposed_folder = str(self.dest_dir / decision.suggested_folder)
+
+                        # Mutăm și redenumim fișierul fizic imediat
+                        try:
+                            from core.file_manager import move_and_rename_file
+
+                            move_and_rename_file(
+                                str_path, proposed_folder, decision.suggested_name
+                            )
+                            added_count += 1
+                            self.log_updated.emit(
+                                f"  ↳ MOVE: Mutat și redenumit cu succes în -> {proposed_folder}/{decision.suggested_name}"
+                            )
+                        except Exception as e:
+                            logger.error(
+                                f"Eroare la mutarea fișierului {file_path.name}: {e}"
+                            )
+                            self.log_updated.emit(f"  ↳ ❌ Eroare la mutare: {e}")
+
+                    else:
+                        proposed_folder = "Quarantine"
+
+                        # Adăugăm în carantină pentru intervenție manuală
+                        quarantine_db.add(
+                            original_path=str_path,
+                            ai_proposed_name=decision.suggested_name,
+                            ai_proposed_folder=proposed_folder,
+                            reason=f"Decizie AI ({decision.status}) bazată pe: {summary[:100]}...",
+                        )
+                        added_count += 1
+                        self.log_updated.emit(
+                            f"  ↳ QUARANTINE: Trimis în carantină. Nume sugerat: {decision.suggested_name}"
+                        )
+                except Exception as e:
+                    logger.error(f"Eroare DeciderAgent: {e}")
+                    self.log_updated.emit(f"  ↳ ❌ Eroare la luarea deciziei: {e}")
+
+            # Actualizăm progresul
+            progress = int((i + 1) / total * 100)
+            self.progress_updated.emit(progress)
+
+        self.log_updated.emit(
+            f"\n✅ Scanare AI completă! {added_count} fișiere noi trimise spre review, "
+            f"{skipped_count} existente ignorate."
+        )
+        self.scan_finished.emit(added_count)

From 2ef475e6854262ee5f4f2c35b4ef0533d8cdef14 Mon Sep 17 00:00:00 2001
From: Fernando-Emanuel Donea <fernandodonea10@gmail.com>
Date: Mon, 11 May 2026 11:45:31 +0300
Subject: [PATCH 04/10] feat: Integrate AI ScanWorker into ScanTab UI (Task 21)

---
 ui/tabs/scan_tab.py | 136 +++++++-------------------------------------
 1 file changed, 21 insertions(+), 115 deletions(-)

diff --git a/ui/tabs/scan_tab.py b/ui/tabs/scan_tab.py
index 9b5e775..8b8d553 100644
--- a/ui/tabs/scan_tab.py
+++ b/ui/tabs/scan_tab.py
@@ -1,5 +1,3 @@
-from pathlib import Path
-
 from PyQt6.QtWidgets import (
     QWidget,
     QVBoxLayout,
@@ -11,117 +9,9 @@
     QTextEdit,
     QFileDialog,
 )
-from PyQt6.QtCore import QThread, pyqtSignal
-
-from core.quarantine_db import quarantine_db
-
-# Categorii de fișiere bazate pe extensie (placeholder până la integrarea AI)
-EXTENSION_CATEGORIES = {
-    ".pdf": "Documente/PDF",
-    ".doc": "Documente/Word",
-    ".docx": "Documente/Word",
-    ".txt": "Documente/Text",
-    ".rtf": "Documente/Text",
-    ".xlsx": "Documente/Excel",
-    ".xls": "Documente/Excel",
-    ".csv": "Documente/CSV",
-    ".pptx": "Documente/PowerPoint",
-    ".ppt": "Documente/PowerPoint",
-    ".jpg": "Imagini",
-    ".jpeg": "Imagini",
-    ".png": "Imagini",
-    ".gif": "Imagini",
-    ".bmp": "Imagini",
-    ".svg": "Imagini",
-    ".zip": "Arhive",
-    ".rar": "Arhive",
-    ".7z": "Arhive",
-    ".tar": "Arhive",
-    ".gz": "Arhive",
-    ".py": "Cod",
-    ".js": "Cod",
-    ".html": "Cod",
-    ".css": "Cod",
-    ".java": "Cod",
-    ".mp3": "Audio",
-    ".wav": "Audio",
-    ".mp4": "Video",
-    ".avi": "Video",
-    ".mkv": "Video",
-}
-
-
-class ScanThread(QThread):
-    """
-    Thread real de scanare care:
-    1. Parcurge recursiv folderul sursă
-    2. Categorizează fișierele după extensie (până la integrarea AI)
-    3. Adaugă fiecare fișier în quarantine_db pentru review-ul utilizatorului
-    """
-
-    progress_updated = pyqtSignal(int)
-    log_updated = pyqtSignal(str)
-    scan_finished = pyqtSignal(int)  # emite numărul total de fișiere adăugate
-
-    def __init__(self, source_dir: str, dest_dir: str):
-        super().__init__()
-        self.source_dir = Path(source_dir)
-        self.dest_dir = Path(dest_dir)
 
-    def run(self):
-        # Colectăm toate fișierele din source (recursiv)
-        files = [f for f in self.source_dir.rglob("*") if f.is_file()]
-        total = len(files)
 
-        if total == 0:
-            self.log_updated.emit("⚠️ Niciun fișier găsit în folderul sursă.")
-            self.scan_finished.emit(0)
-            return
-
-        self.log_updated.emit(f"🔍 {total} fișiere găsite. Se începe scanarea...")
-
-        added_count = 0
-        skipped_count = 0
-
-        # Preluăm fișierele deja existente în carantină (pentru a evita duplicatele)
-        existing_paths = {r["original_path"] for r in quarantine_db.get_all()}
-
-        for i, file_path in enumerate(files):
-            str_path = str(file_path)
-
-            # Verificăm dacă fișierul e deja în carantină
-            if str_path in existing_paths:
-                skipped_count += 1
-                self.log_updated.emit(
-                    f"⏭️ {file_path.name} — deja în carantină, skip"
-                )
-            else:
-                # Categorizare pe bază de extensie
-                ext = file_path.suffix.lower()
-                category = EXTENSION_CATEGORIES.get(ext, "Altele")
-                proposed_folder = str(self.dest_dir / category)
-
-                quarantine_db.add(
-                    original_path=str_path,
-                    ai_proposed_name=file_path.name,
-                    ai_proposed_folder=proposed_folder,
-                    reason=f"Categorizat automat după extensie: {ext or 'fără extensie'}",
-                )
-                added_count += 1
-
-                self.log_updated.emit(
-                    f"📄 {file_path.name} → 📂 {category}"
-                )
-
-            # Actualizăm progress bar-ul
-            progress = int((i + 1) / total * 100)
-            self.progress_updated.emit(progress)
-
-        self.log_updated.emit(
-            f"\n✅ Scanare completă! {added_count} fișiere adăugate, "
-            f"{skipped_count} existente (skip)."
-        )
-        self.scan_finished.emit(added_count)
+from core.scan_worker import ScanWorker
 
 
 class ScanTab(QWidget):
@@ -154,6 +44,16 @@ def init_ui(self):
         dest_layout.addWidget(self.dest_input)
         dest_layout.addWidget(self.dest_browse_btn)
 
+        # Rule layout
+        rule_layout = QHBoxLayout()
+        self.rule_label = QLabel("AI Organizing Rule:")
+        self.rule_input = QLineEdit()
+        self.rule_input.setPlaceholderText(
+            "e.g., Pune facturile in folderul FacturiNou"
+        )
+        rule_layout.addWidget(self.rule_label)
+        rule_layout.addWidget(self.rule_input)
+
         # Start button
         self.start_btn = QPushButton("Start Scan")
         self.start_btn.setObjectName(
@@ -172,6 +72,7 @@ def init_ui(self):
         # Add all to main layout
         layout.addLayout(source_layout)
         layout.addLayout(dest_layout)
+        layout.addLayout(rule_layout)
         layout.addWidget(self.start_btn)
         layout.addWidget(self.progress_bar)
         layout.addWidget(QLabel("Logs:"))
@@ -192,9 +93,13 @@ def browse_dest(self):
             self.dest_input.setText(directory)
 
     def start_scan(self):
-        if not self.source_input.text() or not self.dest_input.text():
+        if (
+            not self.source_input.text()
+            or not self.dest_input.text()
+            or not self.rule_input.text()
+        ):
             self.log_area.append(
-                "⚠️ Selectează atât folderul sursă cât și cel destinație."
+                "⚠️ Selectează folderele și introdu o regulă de organizare AI."
             )
             return
 
@@ -202,10 +107,11 @@ def start_scan(self):
         self.progress_bar.setValue(0)
         self.log_area.clear()
 
-        # Pornim thread-ul real de scanare
-        self.scan_thread = ScanThread(
+        # Pornim worker-ul AI de scanare
+        self.scan_thread = ScanWorker(
             source_dir=self.source_input.text(),
             dest_dir=self.dest_input.text(),
+            user_rule=self.rule_input.text(),
         )
         self.scan_thread.progress_updated.connect(self.update_progress)
         self.scan_thread.log_updated.connect(self.append_log)

From b9dd6dfe39273d09d313a6e0712624946caa1704 Mon Sep 17 00:00:00 2001
From: Fernando-Emanuel Donea <fernandodonea10@gmail.com>
Date: Mon, 11 May 2026 11:47:06 +0300
Subject: [PATCH 05/10] chore: untrack __pycache__ files

---
 ui/__pycache__/__init__.cpython-314.pyc          | Bin 141 -> 0 bytes
 ui/__pycache__/app_window.cpython-314.pyc        | Bin 1990 -> 0 bytes
 ui/tabs/__pycache__/__init__.cpython-314.pyc     | Bin 146 -> 0 bytes
 .../__pycache__/quarantine_tab.cpython-314.pyc   | Bin 7821 -> 0 bytes
 ui/tabs/__pycache__/rules_tab.cpython-314.pyc    | Bin 9531 -> 0 bytes
 ui/tabs/__pycache__/scan_tab.cpython-314.pyc     | Bin 7593 -> 0 bytes
 6 files changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 ui/__pycache__/__init__.cpython-314.pyc
 delete mode 100644 ui/__pycache__/app_window.cpython-314.pyc
 delete mode 100644 ui/tabs/__pycache__/__init__.cpython-314.pyc
 delete mode 100644 ui/tabs/__pycache__/quarantine_tab.cpython-314.pyc
 delete mode 100644 ui/tabs/__pycache__/rules_tab.cpython-314.pyc
 delete mode 100644 ui/tabs/__pycache__/scan_tab.cpython-314.pyc

diff --git a/ui/__pycache__/__init__.cpython-314.pyc b/ui/__pycache__/__init__.cpython-314.pyc
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diff --git a/ui/tabs/__pycache__/scan_tab.cpython-314.pyc b/ui/tabs/__pycache__/scan_tab.cpython-314.pyc
deleted file mode 100644
index 7c5e1c647d068883157fe2482892bdd8d26efb94..0000000000000000000000000000000000000000
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From e8f733409b40058cbcb6f40ff970ae874e05fa1f Mon Sep 17 00:00:00 2001
From: Fernando-Emanuel Donea <fernandodonea10@gmail.com>
Date: Mon, 11 May 2026 12:24:56 +0300
Subject: [PATCH 06/10] docs: update .env.example with GOOGLE_MODEL_NAME

---
 .env.example | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.env.example b/.env.example
index 558cced..d34f305 100644
--- a/.env.example
+++ b/.env.example
@@ -5,5 +5,8 @@ AI_PROVIDER=ollama
 # Daca AI_PROVIDER este 'google', introdu aici cheia ta secreta de API
 GOOGLE_API_KEY=your_google_api_key_here
 
+# Modelul implicit daca folosesti google
+GOOGLE_MODEL_NAME=gemini-2.5-flash
+
 # URL-ul de baza pentru containerul local de ollama
 OLLAMA_BASE_URL=http://ollama:11434

From f6e57c3086e89daa2709c64336dd4f9acafa4a58 Mon Sep 17 00:00:00 2001
From: Fernando-Emanuel Donea <fernandodonea10@gmail.com>
Date: Mon, 11 May 2026 12:40:04 +0300
Subject: [PATCH 07/10] update .env.example

---
 .env.example | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.env.example b/.env.example
index d34f305..636dd69 100644
--- a/.env.example
+++ b/.env.example
@@ -9,4 +9,4 @@ GOOGLE_API_KEY=your_google_api_key_here
 GOOGLE_MODEL_NAME=gemini-2.5-flash
 
 # URL-ul de baza pentru containerul local de ollama
-OLLAMA_BASE_URL=http://ollama:11434
+OLLAMA_BASE_URL=http://localhost:11434

From cc0206bd20b0ea6aaee1c07ff5ca11d4aa566a66 Mon Sep 17 00:00:00 2001
From: Fernando-Emanuel Donea <fernandodonea10@gmail.com>
Date: Mon, 11 May 2026 13:16:35 +0300
Subject: [PATCH 08/10] docs: update setup instructions in README

---
 README.md | 38 +++++++++++++++++++++++++++++++-------
 1 file changed, 31 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index ba7e6fc..2184e79 100644
--- a/README.md
+++ b/README.md
@@ -139,24 +139,48 @@ To ensure consistent code quality and formatting, this project is configured to
 
 Once installed, `ruff` will automatically format your code on `git commit`. If changes are made by the formatter, the commit will abort—simply `git add` the updated files and run `git commit` again.
 
-## 🐳 Docker & Local AI Setup
+## 🐳 Environment & Local AI Setup
 
 The application leverages Docker to seamlessly run local AI models without complicating the host system.
 
-### Setup Instructions:
-1. Start the Docker containers:
+### Step-by-Step Setup Instructions:
+
+1. **Configure Environment Variables**:
    ```bash
-   docker-compose up -d
+   cp .env.example .env
    ```
-2. Create the custom AI model (Gemma 2 based) configured for precision:
+
+2. **Start the Docker container** (for Ollama):
+   ```bash
+   docker-compose up -d ollama
+   ```
+
+3. **Pull base model and Create Custom AI Models**:
+   ClutterKill uses two distinct models for processing (Classifier and Extractor):
    ```bash
+   # Pull the base model (Wait for the download to finish)
+   docker exec -it clutterkill_ollama ollama pull gemma2:2b
+   # Note: If you get a 'manifest does not exist' error on older machines, use 'gemma:2b' instead and update the Modelfiles.
+
+   # Create Agent 0 & 2 (Classifier)
    docker exec -it clutterkill_ollama ollama create ck-model -f /app/ai/Modelfile
+   
+   # Create Agent 1 (Extractor)
+   docker exec -it clutterkill_ollama ollama create ck-extractor -f /app/ai/Modelfile.extractor
    ```
-3. Verify the model is running:
+
+4. **Verify the models are running**:
    ```bash
    curl http://localhost:11434/api/tags
    ```
 
-*Note: The `docker-compose.yml` configuration also provides environment variables (`AI_PROVIDER`, `GOOGLE_API_KEY`) to easily switch between local `ollama` processing and cloud-based alternatives like `google`.*
+5. **Run the Application**:
+   Activate your virtual environment and run the graphical interface:
+   ```bash
+   source .venv/bin/activate
+   python main.py
+   ```
+
+*Note: The project configuration also provides environment variables (`AI_PROVIDER`, `GOOGLE_API_KEY`) to easily switch between local `ollama` processing and cloud-based alternatives like `google`.*
 
 For more detailed DevOps and QA instructions, please refer to [README_ingineri.md](README_ingineri.md).

From e241c31cf691d5956f024f137556615eb0470be7 Mon Sep 17 00:00:00 2001
From: Fernando-Emanuel Donea <fernandodonea10@gmail.com>
Date: Mon, 11 May 2026 13:21:43 +0300
Subject: [PATCH 09/10] fix: enforce smart file renaming by Agent 2

---
 ai/agent_decider.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/ai/agent_decider.py b/ai/agent_decider.py
index 55addd8..4187bc8 100644
--- a/ai/agent_decider.py
+++ b/ai/agent_decider.py
@@ -72,9 +72,10 @@ def sanitize_filename(cls, v: str) -> str:
 Instructions:
 1. If the Document Summary MATCHES the Rule Category, your status must be "move".
 2. If it DOES NOT match, or if you are unsure, your status must be "quarantine".
-3. Calculate the new filename based on the Naming Convention. If the naming convention includes {{original_filename}}, replace it with the actual original filename.
-4. If the status is "quarantine", the folder must be "Quarantine".
-5. If the status is "quarantine", the suggested_name MUST be exactly the Original Filename.
+3. Calculate the new filename based on the Naming Convention. If the naming convention is '{original_filename}', missing, or unclear, YOU MUST create a smart, descriptive, and short filename based on the Document Summary (e.g. 'Factura_ENEL_12_05_2023.pdf' or 'Curs_Analiza_MDS.pdf'). DO NOT keep chaotic original filenames like 'scan_123.pdf'.
+4. CRITICAL: The new filename MUST keep the exact same file extension as the Original Filename.
+5. If the status is "quarantine", the folder must be "Quarantine".
+6. If the status is "quarantine", the suggested_name MUST be exactly the Original Filename.
 
 IMPORTANT: You must return ONLY the raw JSON object containing the actual values. Do NOT return a JSON schema, and do NOT wrap your answer in markdown fences (like ```json).
 

From 0e9ef9a45803a5aa3d59b11b1f2713f02a5577e8 Mon Sep 17 00:00:00 2001
From: Fernando-Emanuel Donea <fernandodonea10@gmail.com>
Date: Mon, 11 May 2026 13:54:05 +0300
Subject: [PATCH 10/10] fix: agent decider treats naming convention as semantic
 template

---
 ai/agent_decider.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/ai/agent_decider.py b/ai/agent_decider.py
index 4187bc8..25aeff3 100644
--- a/ai/agent_decider.py
+++ b/ai/agent_decider.py
@@ -72,10 +72,16 @@ def sanitize_filename(cls, v: str) -> str:
 Instructions:
 1. If the Document Summary MATCHES the Rule Category, your status must be "move".
 2. If it DOES NOT match, or if you are unsure, your status must be "quarantine".
-3. Calculate the new filename based on the Naming Convention. If the naming convention is '{original_filename}', missing, or unclear, YOU MUST create a smart, descriptive, and short filename based on the Document Summary (e.g. 'Factura_ENEL_12_05_2023.pdf' or 'Curs_Analiza_MDS.pdf'). DO NOT keep chaotic original filenames like 'scan_123.pdf'.
-4. CRITICAL: The new filename MUST keep the exact same file extension as the Original Filename.
-5. If the status is "quarantine", the folder must be "Quarantine".
-6. If the status is "quarantine", the suggested_name MUST be exactly the Original Filename.
+3. Build the new filename using the Naming Convention as a TEMPLATE:
+   - The Naming Convention may contain camelCase or descriptive placeholder words like "abreviereaMateriei", "NumarulCursului", "Data", "Emitent", "Suma", etc.
+   - YOU MUST extract the actual values from the Document Summary and substitute them into each placeholder.
+   - Example: If Naming Convention is "abreviereaMateriei_Curs_NumarulCursului_Data" and the document is about "Algoritmi Avansati, Cursul 4, 01.01.2026", the result must be "AlgoritmiAvansati_Curs_4_01012026".
+   - If a placeholder value cannot be determined from the Document Summary, use a sensible short abbreviation (e.g. "Unknown").
+   - If the Naming Convention is literally "{{original_filename}}", keep the original filename unchanged.
+4. CRITICAL: The new filename MUST keep the exact same file extension as the Original Filename (e.g. .pdf, .docx).
+5. CRITICAL: Do NOT include spaces in the filename. Use underscores (_) instead.
+6. If the status is "quarantine", the folder must be "Quarantine".
+7. If the status is "quarantine", the suggested_name MUST be exactly the Original Filename.
 
 IMPORTANT: You must return ONLY the raw JSON object containing the actual values. Do NOT return a JSON schema, and do NOT wrap your answer in markdown fences (like ```json).