update models for rescuebox , fix chromadb location, fix uninstall (#44)

* move facematch chromadb to appdata
automate ollama install for text_summary

* update uninstall to remove pre reqs

Author: nb950

RescueBox-Desktop/build/installer.nsh

@@ -24,13 +24,20 @@ Function .onInstSuccess
     Strcpy "$INSTDIR_DAT" "$INSTDIR\resources\assets\rb_server"
     ExpandEnvStrings $0 %COMSPEC%
     ExecWait '"$0" /C "msiexec /i $INSTDIR_DAT\winfsp-2.0.23075.msi   INSTALLLEVEL=1000 /passive"'
+    Var /GLOBAL O_LOG
+    Strcpy "$O_LOG" "$AppData\RescueBox-Desktop\logs\o.log"
+    ExecWait '"$0" /C "start /min $INSTDIR_DAT\OllamaSetup.exe /verysilent /SP /SUPPRESSMSGBOXES /log=$O_LOG"'
+
 FunctionEnd
 
 
 Section "Uninstall"
   Var /GLOBAL INSTDIR_LOG
   Strcpy "$INSTDIR_LOG" "$AppData\RescueBox-Desktop\logs"
-
+  MessageBox MB_OK "Uninstall Prereqs"
+  ExpandEnvStrings $0 %COMSPEC%
+  ExecWait '"$0" /C "winget uninstall --nowarn --disable-interactivity WinFsp.WinFsp"'
+  ExecWait '"$0" /C "winget uninstall --nowarn --disable-interactivity ollama.ollama"'
   FindWindow $0 "RescueBox-Desktop"
   SendMessage $0 ${WM_CLOSE} 0 0
   ExecWait '"$0" /k "del/f /q $INSTDIR_LOG\*.log"'

src/face-detection-recognition/face_detection_recognition/database_functions.py

@@ -5,6 +5,8 @@
 import json
 from dotenv import load_dotenv
 import os
+import platform
+from pathlib import Path
 
 from face_detection_recognition.utils.resource_path import get_config_path
 
@@ -38,17 +40,15 @@ def __init__(self):
             settings = Settings(
                 anonymized_telemetry=False,
             )
+            db_path = Path.home() / ".rescueBox-desktop" / "facematch"
+            if platform.system() == "Windows":
+                appdata = os.environ.get("APPDATA")
+                db_path = Path(appdata) / "RescueBox-Desktop" / "facematch"
+            if not db_path.exists():
+                db_path.mkdir(parents=True, exist_ok=True)
             self.client = chromadb.PersistentClient(
                 settings=settings,
-                path=os.path.abspath(
-                    os.path.join(
-                        os.getcwd(),
-                        "src",
-                        "face-detection-recognition",
-                        "resources",
-                        "data",
-                    )
-                ),
+                path=str(db_path),
             )
 
     def create_full_collection_name(self, base_name, detector, model, isEnsemble):

src/text-summary/output/story_1.txt

@@ -6,6 +6,7 @@
     summarize,
 )
 from text_summary.summary_prompt import PROMPT
+from ollama._types import ListResponse
 
 
 def test_extract_response_after_think():
@@ -18,10 +19,15 @@ def test_extract_response_after_think():
     assert extract_response_after_think(text_without_tag) == text_without_tag.strip()
 
 
-@patch("text_summary.model.ollama.pull")
-def test_ensure_model_exists(mock_pull):
+@patch("ollama.list")
+@patch("ollama.pull")
+# @patch.object(ollama.Client, '__init__', return_value=None)
+def test_ensure_model_exists(mock_pull, mock_list):
     # Test case where model is supported and pull is successful
+
     mock_pull.return_value = MagicMock(status="success")
+
+    mock_list.return_value = {"models": [ListResponse.Model(model="gemma3:1b")]}
     ensure_model_exists("gemma3:1b")  # Should not raise any exception
 
     # Test case where model is not supported
@@ -30,8 +36,8 @@ def test_ensure_model_exists(mock_pull):
 
     # Test case where pull fails
     mock_pull.return_value = MagicMock(status="failure")
-    with pytest.raises(ValueError, match="Failed to pull model 'gemma3:1b':"):
-        ensure_model_exists("gemma3:1b")
+    with pytest.raises(ValueError, match="Failed to pull model 'llama3.2:3b'"):
+        ensure_model_exists("llama3.2:3b")
 
 
 @patch("text_summary.model.ollama.generate")

src/text-summary/output/story_2.txt

@@ -25,11 +25,14 @@ def ensure_model_exists(model: str) -> None:
             f"Model '{model}' is not supported. Supported models are: {SUPPORTED_MODELS}"
         )
     try:
-        response = ollama.pull(model)
-    except ollama.ResponseError as e:
-        raise ValueError(e.error)
-    if response.status != "success":
-        raise ValueError(f"Failed to pull model '{model}': {response}")
+        resp = ollama.list()
+        models = [m.model for m in resp["models"]]
+        if model not in models:
+            response = ollama.pull(model)
+            if response.status != "success":
+                raise ValueError(f"Failed to pull model '{model}': {response}")
+    except ValueError as e:
+        raise ValueError(e)
 
 
 def summarize(model: str, text: str) -> str: