Have settled on testing and using the Qwen2.5:1.5b model as it plays much nicer with the JSON requirements - way better than the Gemma models did. Have refined the calls via LlmManager.kt and MediaPipeAdapter.kt and tested using battery, ping and a new weather skill. Half way through updating further to allow for SMS reading and updating the SkillManager.kt again to accommodate this with a two-pass bridge for python calling.

.idea/deploymentTargetSelector.xml

@@ -4,7 +4,7 @@
     <selectionStates>
       <SelectionState runConfigName="app">
         <option name="selectionMode" value="DROPDOWN" />
-        <DropdownSelection timestamp="2026-02-25T23:24:39.552459762Z">
+        <DropdownSelection timestamp="2026-02-28T04:08:30.769945596Z">
           <Target type="DEFAULT_BOOT">
             <handle>
               <DeviceId pluginId="LocalEmulator" identifier="path=/home/michael/.android/avd/Pixel_8_API_35.avd" />

app/build.gradle.kts

@@ -3,7 +3,7 @@ plugins {
     alias(libs.plugins.kotlin.android)
     alias(libs.plugins.kotlin.compose)
     id("com.chaquo.python") // Apply it here
-    id("kotlin-kapt") // Added for the Room Android database subsystem and libraries
+    id("com.google.devtools.ksp") // Added for the Room Android database subsystem and libraries
 }
 
 chaquopy {
@@ -84,5 +84,14 @@ dependencies {
     // Room Database for local chat history
     implementation("androidx.room:room-runtime:2.6.1")
     implementation("androidx.room:room-ktx:2.6.1")
-    kapt("androidx.room:room-compiler:2.6.1")
+    ksp("androidx.room:room-compiler:2.6.1")
+
+    // Llama.cpp Kotlin Multiplatform Wrapper
+    implementation("com.llamatik:library:0.8.1")
+
+    // Google AI Edge - MediaPipe LLM Inference API
+    implementation("com.google.mediapipe:tasks-genai:0.10.27")
+
+    // Extended Material Icons (for Download, CheckCircle, etc.)
+    implementation("androidx.compose.material:material-icons-extended")
 }

app/src/main/AndroidManifest.xml

@@ -4,6 +4,12 @@
 
     <uses-permission android:name="android.permission.INTERNET" />
 
+    <uses-permission android:name="android.permission.BIND_ACCESSIBILITY_SERVICE" />
+
+    <uses-permission android:name="android.permission.READ_SMS"/>
+    <uses-permission android:name="android.permission.RECEIVE_SMS"/> <!-- for new messages -->
+
+
     <application
         android:name="AliceApp"
         android:usesCleartextTraffic="true"
@@ -26,6 +32,17 @@
                 <category android:name="android.intent.category.LAUNCHER" />
             </intent-filter>
         </activity>
+        <service
+            android:name=".AliceAccessibilityService"
+            android:permission="android.permission.BIND_ACCESSIBILITY_SERVICE"
+            android:exported="false">
+            <intent-filter>
+                <action android:name="android.accessibilityservice.AccessibilityService" />
+            </intent-filter>
+            <meta-data
+                android:name="android.accessibilityservice"
+                android:resource="@xml/accessibility_service_config" />
+        </service>
     </application>
 
 </manifest>

app/src/main/java/net/mmanningau/alice/AliceAccessibilityService.kt

@@ -0,0 +1,21 @@
+package net.mmanningau.alice
+
+import android.accessibilityservice.AccessibilityService
+import android.view.accessibility.AccessibilityEvent
+import android.util.Log
+
+class AliceAccessibilityService : AccessibilityService() {
+
+    override fun onServiceConnected() {
+        super.onServiceConnected()
+        Log.d("AliceAccessibility", "Service Connected and Ready!")
+    }
+
+    override fun onAccessibilityEvent(event: AccessibilityEvent?) {
+        // We will build the screen-reading logic here later!
+    }
+
+    override fun onInterrupt() {
+        Log.e("AliceAccessibility", "Service Interrupted")
+    }
+}

app/src/main/java/net/mmanningau/alice/LlamaCppAdapter.kt

@@ -0,0 +1,115 @@
+package net.mmanningau.alice
+
+import com.llamatik.library.platform.LlamaBridge
+import dev.langchain4j.agent.tool.ToolExecutionRequest
+import dev.langchain4j.agent.tool.ToolSpecification
+import dev.langchain4j.data.message.AiMessage
+import dev.langchain4j.data.message.ChatMessage
+import dev.langchain4j.data.message.SystemMessage
+import dev.langchain4j.data.message.ToolExecutionResultMessage
+import dev.langchain4j.data.message.UserMessage
+import dev.langchain4j.model.chat.ChatLanguageModel
+import dev.langchain4j.model.output.Response
+import org.json.JSONObject
+import java.io.File
+import java.util.UUID
+
+class LlamaCppAdapter(private val modelPath: String) : ChatLanguageModel {
+
+    private var isEngineLoaded = false
+
+    private fun getOrInitEngine() {
+        if (!isEngineLoaded) {
+            val modelFile = File(modelPath)
+            if (!modelFile.exists()) {
+                throw IllegalStateException("Model file not found at: $modelPath. Please download a model first.")
+            }
+            LlamaBridge.initGenerateModel(modelPath)
+            isEngineLoaded = true
+        }
+    }
+
+    override fun generate(messages: List<ChatMessage>): Response<AiMessage> {
+        return generate(messages, emptyList())
+    }
+
+    // Advanced generator (With tools)
+    override fun generate(
+        messages: List<ChatMessage>,
+        toolSpecifications: List<ToolSpecification>
+    ): Response<AiMessage> {
+        getOrInitEngine()
+
+        val promptBuilder = java.lang.StringBuilder()
+
+        // 1. Build the Fool-Proof System Prompt
+        val toolsPrompt = java.lang.StringBuilder()
+        if (toolSpecifications.isNotEmpty()) {
+            toolsPrompt.append("\n\n# AVAILABLE TOOLS\n")
+            for (tool in toolSpecifications) {
+                toolsPrompt.append("- ${tool.name()}: ${tool.description()}\n")
+            }
+            toolsPrompt.append("\nCRITICAL INSTRUCTION: To use a tool, you MUST reply with a JSON object. Do NOT use Python parentheses.\n")
+            toolsPrompt.append("Correct Example:\n{\"name\": \"get_battery_level\", \"arguments\": {}}\n")
+        }
+
+        // 2. Format the Chat History
+        for (message in messages) {
+            when (message) {
+                is SystemMessage -> {
+                    val content = message.text() + toolsPrompt.toString()
+                    promptBuilder.append("<|im_start|>system\n$content<|im_end|>\n")
+                }
+                is UserMessage -> promptBuilder.append("<|im_start|>user\n${message.text()}<|im_end|>\n")
+                is ToolExecutionResultMessage -> {
+                    promptBuilder.append("<|im_start|>user\nTool [${message.toolName()}] returned: ${message.text()}<|im_end|>\n")
+                }
+                is AiMessage -> {
+                    if (message.hasToolExecutionRequests()) {
+                        val request = message.toolExecutionRequests()[0]
+                        promptBuilder.append("<|im_start|>assistant\n{\"name\": \"${request.name()}\", \"arguments\": ${request.arguments()}}<|im_end|>\n")
+                    } else {
+                        val cleanText = message.text()?.replace(Regex("Calling tool:.*?\\.\\.\\."), "")?.trim() ?: ""
+                        if (cleanText.isNotBlank()) {
+                            promptBuilder.append("<|im_start|>assistant\n$cleanText<|im_end|>\n")
+                        }
+                    }
+                }
+            }
+        }
+        promptBuilder.append("<|im_start|>assistant\n")
+
+        // 3. Execution
+        val responseText = LlamaBridge.generate(promptBuilder.toString()).replace("<|im_end|>", "").trim()
+
+        // 4. Parse the Output (Regex Hunter)
+        if (toolSpecifications.isNotEmpty()) {
+            // Hunt for the pattern "name": "something" regardless of surrounding brackets
+            val nameRegex = Regex("\"name\"\\s*:\\s*\"([^\"]+)\"")
+            val match = nameRegex.find(responseText.replace("'", "\"")) // Sanitize single quotes first
+
+            if (match != null) {
+                val toolName = match.groupValues[1]
+
+                // Try to extract arguments if they exist, otherwise default to empty JSON
+                var argumentsJson = "{}"
+                val argRegex = Regex("\"arguments\"\\s*:\\s*(\\{.*?\\})")
+                val argMatch = argRegex.find(responseText.replace("'", "\""))
+                if (argMatch != null) {
+                    argumentsJson = argMatch.groupValues[1]
+                }
+
+                val request = ToolExecutionRequest.builder()
+                    .id(UUID.randomUUID().toString())
+                    .name(toolName)
+                    .arguments(argumentsJson)
+                    .build()
+
+                return Response.from(AiMessage.from("Calling tool: $toolName...", listOf(request)))
+            }
+        }
+
+        // 5. Standard Text Output
+        return Response.from(AiMessage(responseText))
+    }
+}

app/src/main/java/net/mmanningau/alice/LlmManager.kt

@@ -1,5 +1,7 @@
 package net.mmanningau.alice
 
+import android.content.Context
+import android.util.Log
 import dev.langchain4j.data.message.AiMessage
 import dev.langchain4j.data.message.ChatMessage
 import dev.langchain4j.data.message.SystemMessage
@@ -11,12 +13,17 @@ import java.time.Duration
 import java.text.SimpleDateFormat
 import java.util.Date
 import java.util.Locale
+import kotlinx.coroutines.flow.MutableStateFlow
+import kotlinx.coroutines.flow.StateFlow
 
 object LlmManager {
 
     private var chatModel: ChatLanguageModel? = null
     var currentMode: String = "Remote"
         private set
+    // Hardware telemetry for the UI
+    private val _hardwareBackend = MutableStateFlow("Standby")
+    val hardwareBackend: StateFlow<String> = _hardwareBackend
 
     // Database tracking
     private var chatDao: ChatDao? = null
@@ -28,7 +35,7 @@ object LlmManager {
 
     // Initialization now makes the dao optional so the UI can safely call it!
     fun initialize(
-        dao: ChatDao?, mode: String, url: String, modelName: String, apiKey: String, systemPrompt: String
+        context: Context, dao: ChatDao?, mode: String, url: String, modelName: String, apiKey: String, systemPrompt: String
     ) {
         // Only update the DAO if one was passed in (like on app boot)
         if (dao != null) {
@@ -50,8 +57,37 @@ object LlmManager {
                 .logRequests(true)
                 .logResponses(true)
                 .build()
+        } else if (mode == "Local") {
+            // Grab the absolute path from the registry
+            val fullPath = ModelRegistry.getModelPath(context, modelName)
+
+            // NEW: The Switchboard! Route the path to the correct engine based on file type
+            when {
+                fullPath.endsWith(".task") || fullPath.endsWith(".litertlm") -> {
+                    Log.d("AliceEngine", "Routing to MediaPipe Engine (Formula 1 Mode)")
+                    // Reset to standby when a new model is selected
+                    _hardwareBackend.value = "Standby"
+
+                    chatModel = MediaPipeAdapter(context, fullPath) { systemEvent ->
+                        // Intercept the hardware broadcast
+                        if (systemEvent.startsWith("HARDWARE_STATE:")) {
+                            _hardwareBackend.value = systemEvent.removePrefix("HARDWARE_STATE:").trim()
                         } else {
-            chatModel = null // MLC Engine goes here later!
+                            Log.w("AliceSystem", systemEvent)
+                        }
+                    }
+                }
+                fullPath.endsWith(".gguf") -> {
+                    Log.d("AliceEngine", "Routing to Llama.cpp Engine (Flexible Mode)")
+                    // Llama.cpp manages its own Vulkan backend, so we just label it
+                    _hardwareBackend.value = "Vulkan"
+                    chatModel = LlamaCppAdapter(fullPath)
+                }
+                else -> {
+                    Log.e("AliceEngine", "Unsupported model file extension: $fullPath")
+                    chatModel = null
+                }
+            }
         }
 
         // Database Startup Logic
@@ -102,47 +138,66 @@ object LlmManager {
     }
 
     fun chat(userText: String): String {
-        if (currentMode == "MLC") return "System: MLC LLM On-Device engine is selected but not yet installed."
+        if (currentMode == "Local" && chatModel == null) return "System: Local engine is selected but not properly initialized or unsupported file format."
         val currentModel = chatModel ?: return "Error: LLM engine not initialized."
 
-        // If the history size is 1, it means only the System prompt exists. This is the first message!
         if (chatHistory.size == 1) {
-            // Take the first 25 characters. If it's longer, add "..."
             val previewLength = 25
-            val newTitle = if (userText.length > previewLength) {
+            val newTitle = if (userText.length > previewLength) userText.take(previewLength).trim() + "..." else userText
-                userText.take(previewLength).trim() + "..."
-            } else {
-                userText
-            }
-            // Update the database instantly
             chatDao?.updateThreadTitle(currentThreadId, newTitle)
         }
 
-        // 1. Save user message to DB and Memory
         chatDao?.insertMessage(ChatMessageEntity(threadId = currentThreadId, text = userText, isUser = true))
         chatHistory.add(UserMessage(userText))
 
         val toolSpecs = SkillManager.loadSkills()
 
+        // --- LOOP CONTROL CONSTANTS ---
+        val MAX_TOOL_ITERATIONS = 5
+        var toolIterations = 0
+        val executedToolSignatures = mutableSetOf<String>() // Tracks name+args pairs to catch spin loops
+
         var response = currentModel.generate(chatHistory, toolSpecs)
         var aiMessage: AiMessage = response.content()
         chatHistory.add(aiMessage)
 
         while (aiMessage.hasToolExecutionRequests()) {
+
+            // --- GUARD 1: Hard iteration cap ---
+            if (toolIterations >= MAX_TOOL_ITERATIONS) {
+                Log.w("AliceEngine", "Tool loop cap reached after $MAX_TOOL_ITERATIONS iterations. Breaking.")
+                val fallbackText = "I've reached the maximum number of steps trying to complete this task. Here's what I found so far."
+                chatDao?.insertMessage(ChatMessageEntity(threadId = currentThreadId, text = fallbackText, isUser = false))
+                return fallbackText
+            }
+
             for (request in aiMessage.toolExecutionRequests()) {
                 val toolName = request.name()
                 val arguments = request.arguments()
+
+                // --- GUARD 2: Duplicate call detection ---
+                val signature = "$toolName::$arguments"
+                if (executedToolSignatures.contains(signature)) {
+                    Log.w("AliceEngine", "Duplicate tool call detected for '$toolName'. Breaking loop.")
+                    val fallbackText = "I seem to be going in circles with the '$toolName' tool. Let me stop and give you what I have."
+                    chatDao?.insertMessage(ChatMessageEntity(threadId = currentThreadId, text = fallbackText, isUser = false))
+                    return fallbackText
+                }
+                executedToolSignatures.add(signature)
+
                 val toolResult = SkillManager.executeSkill(toolName, arguments)
+                Log.d("AliceSkill", "TOOL_RESULT from [$toolName]: $toolResult")
                 chatHistory.add(ToolExecutionResultMessage(request.id(), toolName, toolResult))
             }
+
+            toolIterations++
             response = currentModel.generate(chatHistory, toolSpecs)
             aiMessage = response.content()
             chatHistory.add(aiMessage)
         }
 
-        // 2. Save final AI message to DB
         chatDao?.insertMessage(ChatMessageEntity(threadId = currentThreadId, text = aiMessage.text(), isUser = false))
-
         return aiMessage.text()
     }
+
 }

app/src/main/java/net/mmanningau/alice/MainActivity.kt

@@ -17,6 +17,9 @@ import androidx.compose.material.icons.filled.Menu
 import androidx.compose.material.icons.filled.Send
 import androidx.compose.material.icons.filled.Add
 import androidx.compose.material.icons.filled.List
+import androidx.compose.material.icons.filled.CheckCircle
+import androidx.compose.material.icons.filled.Download
+import androidx.compose.material3.LinearProgressIndicator
 import androidx.compose.material3.*
 import androidx.compose.runtime.*
 import androidx.compose.ui.Alignment
@@ -47,6 +50,8 @@ class MainActivity : ComponentActivity() {
 fun MainChatScreen() {
     val drawerState = rememberDrawerState(initialValue = DrawerValue.Closed)
     val scope = rememberCoroutineScope()
+    // Observe the live hardware state
+    val hardwareBackend by LlmManager.hardwareBackend.collectAsState()
 
     var currentScreen by remember { mutableStateOf("Chat") }
     var inputText by remember { mutableStateOf("") }
@@ -92,6 +97,19 @@ fun MainChatScreen() {
                     }
                 )
 
+                // --- NEW: Conditional Model Manager Button ---
+                if (LlmManager.currentMode == "Local") {
+                    NavigationDrawerItem(
+                        label = { Text("Model Manager") },
+                        selected = currentScreen == "ModelManager",
+                        icon = { Icon(Icons.Default.Add, contentDescription = "Download") }, // You can change this icon!
+                        onClick = {
+                            scope.launch { drawerState.close() }
+                            currentScreen = "ModelManager"
+                        }
+                    )
+                }
+
                 Spacer(modifier = Modifier.height(16.dp))
                 HorizontalDivider()
                 Text("Chat History", modifier = Modifier.padding(16.dp), style = MaterialTheme.typography.titleMedium, color = MaterialTheme.colorScheme.primary)
@@ -119,7 +137,29 @@ fun MainChatScreen() {
             Scaffold(
                 topBar = {
                     TopAppBar(
-                        title = { Text("Alice Agent") },
+                        title = {
+                            Row(verticalAlignment = Alignment.CenterVertically) {
+                                Text("Alice Agent")
+
+                                // The Live Hardware Telemetry Badge
+                                if (hardwareBackend != "Standby") {
+                                    Spacer(modifier = Modifier.width(8.dp))
+                                    Surface(
+                                        color = if (hardwareBackend == "GPU") MaterialTheme.colorScheme.primary
+                                        else MaterialTheme.colorScheme.error,
+                                        shape = RoundedCornerShape(4.dp)
+                                    ) {
+                                        Text(
+                                            text = hardwareBackend,
+                                            style = MaterialTheme.typography.labelSmall,
+                                            color = if (hardwareBackend == "GPU") MaterialTheme.colorScheme.onPrimary
+                                            else MaterialTheme.colorScheme.onError,
+                                            modifier = Modifier.padding(horizontal = 6.dp, vertical = 2.dp)
+                                        )
+                                    }
+                                }
+                            }
+                        },
                         navigationIcon = {
                             IconButton(onClick = { scope.launch { drawerState.open() } }) {
                                 Icon(Icons.Default.Menu, contentDescription = "Menu")
@@ -185,7 +225,7 @@ fun MainChatScreen() {
                                             val response = LlmManager.chat(userText)
                                             messages = messages + ChatMessage(response, false)
                                         } catch (e: Exception) {
-                                            messages = messages + ChatMessage("Connection Error: Is the local LLM server running?", false)
+                                            messages = messages + ChatMessage("System Error: ${e.message}", false)
                                         }
                                     }
                                 }
@@ -203,6 +243,11 @@ fun MainChatScreen() {
                 onBackClicked = { currentScreen = "Chat" }
             )
         }
+        else if (currentScreen == "ModelManager") {
+            ModelManagerScreen(
+                onBackClicked = { currentScreen = "Chat" }
+            )
+        }
     }
 }
 
@@ -283,10 +328,10 @@ fun SettingsScreen(onBackClicked: () -> Unit) {
                 Text("Remote API")
                 Spacer(modifier = Modifier.width(16.dp))
                 RadioButton(
-                    selected = llmMode == "MLC",
+                    selected = llmMode == "Local",
-                    onClick = { llmMode = "MLC" }
+                    onClick = { llmMode = "Local" }
                 )
-                Text("Local (MLC LLM)")
+                Text("Local (Llama.cpp)")
             }
 
             Spacer(modifier = Modifier.height(8.dp))
@@ -366,7 +411,7 @@ fun SettingsScreen(onBackClicked: () -> Unit) {
                         .putString("systemPrompt", systemPrompt)
                         .apply()
 
-                    LlmManager.initialize(null, llmMode, llmUrl, modelName, apiKey, systemPrompt)
+                    LlmManager.initialize(context, null, llmMode, llmUrl, modelName, apiKey, systemPrompt)
                     SkillManager.updateDirectory(skillsPath)
 
                     onBackClicked()
@@ -379,3 +424,144 @@ fun SettingsScreen(onBackClicked: () -> Unit) {
         }
     }
 }
+
+@OptIn(ExperimentalMaterial3Api::class)
+@Composable
+fun ModelManagerScreen(onBackClicked: () -> Unit) {
+    val context = LocalContext.current
+    val scope = rememberCoroutineScope()
+    val prefs = context.getSharedPreferences("AlicePrefs", Context.MODE_PRIVATE)
+
+    // Track which model the user currently has selected as their active brain
+    var activeModelName by remember { mutableStateOf(prefs.getString("modelName", "") ?: "") }
+
+    // Keep track of download progress percentages for each model ID
+    val downloadProgress = remember { mutableStateMapOf<String, Int>() }
+
+    Scaffold(
+        topBar = {
+            TopAppBar(
+                title = { Text("Local Model Manager") },
+                navigationIcon = {
+                    IconButton(onClick = onBackClicked) {
+                        Icon(Icons.Default.ArrowBack, contentDescription = "Back")
+                    }
+                },
+                colors = TopAppBarDefaults.topAppBarColors(
+                    containerColor = MaterialTheme.colorScheme.secondaryContainer,
+                    titleContentColor = MaterialTheme.colorScheme.onSecondaryContainer
+                )
+            )
+        }
+    ) { paddingValues ->
+        LazyColumn(
+            modifier = Modifier
+                .fillMaxSize()
+                .padding(paddingValues)
+                .padding(16.dp),
+            verticalArrangement = Arrangement.spacedBy(16.dp)
+        ) {
+            item {
+                Text(
+                    "Qwen 2.5 Architecture",
+                    style = MaterialTheme.typography.titleMedium,
+                    color = MaterialTheme.colorScheme.primary
+                )
+                Text(
+                    "These models will run entirely on your device's GPU. Larger models are smarter but consume more battery and generate text slower.",
+                    style = MaterialTheme.typography.bodySmall,
+                    color = MaterialTheme.colorScheme.onSurfaceVariant
+                )
+                Spacer(modifier = Modifier.height(8.dp))
+            }
+
+            items(ModelRegistry.curatedModels) { model ->
+                val isDownloaded = ModelRegistry.isModelDownloaded(context, model.fileName)
+                val currentProgress = downloadProgress[model.id] ?: 0
+                val isActive = activeModelName == model.fileName
+
+                Card(
+                    modifier = Modifier.fillMaxWidth(),
+                    shape = RoundedCornerShape(12.dp),
+                    colors = CardDefaults.cardColors(containerColor = MaterialTheme.colorScheme.surfaceVariant)
+                ) {
+                    Column(modifier = Modifier.padding(16.dp)) {
+                        Row(
+                            modifier = Modifier.fillMaxWidth(),
+                            horizontalArrangement = Arrangement.SpaceBetween,
+                            verticalAlignment = Alignment.CenterVertically
+                        ) {
+                            Text(model.name, style = MaterialTheme.typography.titleMedium)
+                            Text("${model.sizeMb} MB", style = MaterialTheme.typography.labelMedium)
+                        }
+
+                        Spacer(modifier = Modifier.height(4.dp))
+                        Text(model.description, style = MaterialTheme.typography.bodySmall)
+                        Spacer(modifier = Modifier.height(16.dp))
+
+                        if (isDownloaded) {
+                            Button(
+                                onClick = {
+                                    // Save the exact filename so LlmManager knows which one to boot up
+                                    prefs.edit().putString("modelName", model.fileName).apply()
+                                    activeModelName = model.fileName
+
+                                    // NEW: Hot-reload the LlmManager instantly!
+                                    val mode = prefs.getString("llmMode", "Local") ?: "Local"
+                                    val url = prefs.getString("llmUrl", "") ?: ""
+                                    val apiKey = prefs.getString("apiKey", "") ?: ""
+                                    val prompt = prefs.getString("systemPrompt", "You are a helpful AI assistant.") ?: "You are a helpful AI assistant."
+                                    LlmManager.initialize(context, null, mode, url, model.fileName, apiKey, prompt)
+                                },
+                                modifier = Modifier.fillMaxWidth(),
+                                colors = ButtonDefaults.buttonColors(
+                                    containerColor = if (isActive) MaterialTheme.colorScheme.primary else MaterialTheme.colorScheme.secondary
+                                )
+                            ) {
+                                if (isActive) {
+                                    Icon(Icons.Default.CheckCircle, contentDescription = "Active")
+                                    Spacer(modifier = Modifier.width(8.dp))
+                                    Text("Active Model")
+                                } else {
+                                    Text("Set as Active")
+                                }
+                            }
+                        } else if (currentProgress > 0 && currentProgress < 100) {
+                            Column(modifier = Modifier.fillMaxWidth()) {
+                                Text("Downloading: $currentProgress%", style = MaterialTheme.typography.labelMedium)
+                                Spacer(modifier = Modifier.height(4.dp))
+                                LinearProgressIndicator(
+                                    progress = { currentProgress / 100f },
+                                    modifier = Modifier.fillMaxWidth()
+                                )
+                            }
+                        } else {
+                            Button(
+                                onClick = {
+                                    // Initialize progress
+                                    downloadProgress[model.id] = 1
+
+                                    // Launch the background download
+                                    scope.launch {
+                                        // Grab the Hugging Face token from the API Key settings field
+                                        val hfToken = prefs.getString("apiKey", "") ?: ""
+
+                                        ModelDownloader.downloadModel(context, model.downloadUrl, model.fileName, hfToken)
+                                            .collect { progress ->
+                                                downloadProgress[model.id] = progress
+                                            }
+                                    }
+                                },
+                                modifier = Modifier.fillMaxWidth()
+                            ) {
+                                Icon(Icons.Default.Download, contentDescription = "Download")
+                                Spacer(modifier = Modifier.width(8.dp))
+                                Text("Download")
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+}

app/src/main/java/net/mmanningau/alice/MediaPipeAdapter.kt

@@ -0,0 +1,175 @@
+package net.mmanningau.alice
+
+import android.content.Context
+import android.util.Log
+import com.google.mediapipe.tasks.genai.llminference.LlmInference
+import dev.langchain4j.agent.tool.ToolExecutionRequest
+import dev.langchain4j.agent.tool.ToolSpecification
+import dev.langchain4j.data.message.AiMessage
+import dev.langchain4j.data.message.ChatMessage
+import dev.langchain4j.data.message.SystemMessage
+import dev.langchain4j.data.message.ToolExecutionResultMessage
+import dev.langchain4j.data.message.UserMessage
+import dev.langchain4j.model.chat.ChatLanguageModel
+import dev.langchain4j.model.output.Response
+import org.json.JSONObject
+import java.io.File
+import java.util.UUID
+
+class MediaPipeAdapter(
+    private val context: Context,
+    private val modelPath: String,
+    private val onSystemEvent: (String) -> Unit // Flexible routing for UI notifications & Accessibility
+) : ChatLanguageModel {
+
+    private var engine: LlmInference? = null
+
+    private fun getOrInitEngine(): LlmInference {
+        if (engine == null) {
+            val modelFile = File(modelPath)
+            if (!modelFile.exists()) {
+                throw IllegalStateException("Task file not found: $modelPath. Please download it.")
+            }
+
+            try {
+                // THE PUSH: Aggressively demand the Adreno GPU
+                val gpuOptions = LlmInference.LlmInferenceOptions.builder()
+                    .setModelPath(modelPath)
+                    .setMaxTokens(1200)
+                    .setPreferredBackend(LlmInference.Backend.GPU)
+                    .build()
+                engine = LlmInference.createFromOptions(context, gpuOptions)
+                Log.d("AliceEngine", "Formula 1 Mode: GPU Initialized successfully.")
+                // Broadcast the successful hardware lock!
+                onSystemEvent("HARDWARE_STATE: GPU")
+
+            } catch (e: Exception) {
+                // THE FALLBACK: If GPU fails, notify the UI and drop to CPU
+                Log.e("AliceEngine", "GPU Initialization failed: ${e.message}")
+                onSystemEvent("Hardware Notice: GPU not supported for this model. Falling back to CPU. Generation will be slower and consume more battery.")
+
+                val cpuOptions = LlmInference.LlmInferenceOptions.builder()
+                    .setModelPath(modelPath)
+                    .setMaxTokens(1200)
+                    .setPreferredBackend(LlmInference.Backend.CPU)
+                    .build()
+                engine = LlmInference.createFromOptions(context, cpuOptions)
+                // Broadcast the fallback
+                onSystemEvent("HARDWARE_STATE: CPU")
+            }
+        }
+        return engine!!
+    }
+
+    override fun generate(messages: List<ChatMessage>): Response<AiMessage> {
+        return generate(messages, emptyList())
+    }
+
+    override fun generate(
+        messages: List<ChatMessage>,
+        toolSpecifications: List<ToolSpecification>
+    ): Response<AiMessage> {
+        val activeEngine = getOrInitEngine()
+        val promptBuilder = java.lang.StringBuilder()
+
+        // 1. The Strict Negative-Constraint Schema
+        val toolsPrompt = java.lang.StringBuilder()
+        if (toolSpecifications.isNotEmpty()) {
+            toolsPrompt.append("\n\n# AVAILABLE TOOLS\n")
+            for (tool in toolSpecifications) {
+                toolsPrompt.append("- ${tool.name()}: ${tool.description()} | Params: ${tool.parameters()?.toString() ?: "{}"}\n")
+            }
+            toolsPrompt.append("\nCRITICAL RULES:\n")
+            toolsPrompt.append("1. NEVER guess or fabricate data (like battery levels, IP addresses, or network latency). You MUST use a tool to fetch real data.\n")
+            toolsPrompt.append("2. Do NOT invent your own syntax. You must use ONLY the exact JSON format below.\n")
+            toolsPrompt.append("3. To execute a tool, reply with ONLY this JSON object and absolutely no other text:\n")
+            toolsPrompt.append("{\"name\": \"<tool_name>\", \"arguments\": {<args>}}\n")
+        }
+
+        // 2. Format Chat History using GEMMA 3 TAGS (Merging System into User)
+        var isFirstUserMessage = true
+
+        for (message in messages) {
+            when (message) {
+                is SystemMessage -> {
+                    // IGNORE: We do not append a 'system' tag because Gemma 3 doesn't support it.
+                    // We already built the toolsPrompt string in Step 1, so we just hold it.
+                }
+                is UserMessage -> {
+                    if (isFirstUserMessage) {
+                        // Merge the draconian tools prompt and the user's first message into one block
+                        promptBuilder.append("<start_of_turn>user\n${toolsPrompt.toString()}\n\n${message.text()}<end_of_turn>\n")
+                        isFirstUserMessage = false
+                    } else {
+                        promptBuilder.append("<start_of_turn>user\n${message.text()}<end_of_turn>\n")
+                    }
+                }
+                is ToolExecutionResultMessage -> {
+                    promptBuilder.append("<start_of_turn>user\n[TOOL RESULT: ${message.toolName()}]\n${message.text()}\n\nIMPORTANT: The above is raw data from a tool. Do NOT repeat it verbatim. You must now write a naturally worded and concise response to the user's original question using this data. Summarise it concisely as Alice their helpful AI assistant.<end_of_turn>\n")
+                }
+                is AiMessage -> {
+                    if (message.hasToolExecutionRequests()) {
+                        val request = message.toolExecutionRequests()[0]
+                        promptBuilder.append("<start_of_turn>model\n{\"name\": \"${request.name()}\", \"arguments\": ${request.arguments()}}<end_of_turn>\n")
+                    } else {
+                        val cleanText = message.text()?.replace(Regex("Calling tool:.*?\\.\\.\\."), "")?.trim() ?: ""
+                        if (cleanText.isNotBlank()) {
+                            promptBuilder.append("<start_of_turn>model\n$cleanText<end_of_turn>\n")
+                        }
+                    }
+                }
+            }
+        }
+        promptBuilder.append("<start_of_turn>model\n")
+
+        // 3. Execution on MediaPipe
+        val rawResponse = activeEngine.generateResponse(promptBuilder.toString())
+        Log.d("AliceEngine", "RAW_RESPONSE_LENGTH: ${rawResponse.length}")
+        Log.d("AliceEngine", "RAW_RESPONSE: $rawResponse")
+        Log.d("AliceEngine", "Engine state after gen - messages count: ${messages.size}")
+        val responseText = rawResponse.replace("<end_of_turn>", "").trim()
+
+        // Strip the markdown code blocks if Gemma adds them
+        val cleanText = responseText.replace(Regex("```(?:json)?"), "").replace("```", "").trim()
+
+        // 4. The Bulletproof Regex JSON Parser
+        if (toolSpecifications.isNotEmpty()) {
+            // Hunt directly for the tool name, bypassing strict JSON validation
+            val nameRegex = Regex("\"name\"\\s*:\\s*\"([^\"]+)\"")
+            val match = nameRegex.find(cleanText)
+
+            if (match != null) {
+                val toolName = match.groupValues[1]
+
+                // Safely attempt to grab arguments. If they are hallucinated garbage, default to {}
+                var argumentsJson = "{}"
+                // Old regex - pre Claude ..... val argRegex = Regex("\"arguments\"\\s*:\\s*(\\{.*?\\})")
+                val argRegex = Regex("\"arguments\"\\s*:\\s*(\\{.*?\\})", RegexOption.DOT_MATCHES_ALL)
+
+                val argMatch = argRegex.find(cleanText)
+
+                if (argMatch != null) {
+                    val foundArgs = argMatch.groupValues[1]
+                    try {
+                        // Test if the args are valid JSON
+                        JSONObject(foundArgs)
+                        argumentsJson = foundArgs
+                    } catch (e: Exception) {
+                        // It was garbage (like the infinite 7777s). Keep the "{}" default.
+                        Log.w("AliceEngine", "Discarded malformed arguments: $foundArgs")
+                    }
+                }
+
+                val request = ToolExecutionRequest.builder()
+                    .id(UUID.randomUUID().toString())
+                    .name(toolName)
+                    .arguments(argumentsJson)
+                    .build()
+
+                return Response.from(AiMessage.from("Calling tool: $toolName...", listOf(request)))
+            }
+        }
+
+        return Response.from(AiMessage(cleanText))
+    }
+}

app/src/main/java/net/mmanningau/alice/ModelDownloader.kt

@@ -0,0 +1,75 @@
+package net.mmanningau.alice
+
+import android.app.DownloadManager
+import android.content.Context
+import android.net.Uri
+import kotlinx.coroutines.Dispatchers
+import kotlinx.coroutines.delay
+import kotlinx.coroutines.flow.Flow
+import kotlinx.coroutines.flow.flow
+import kotlinx.coroutines.flow.flowOn
+import java.io.File
+
+object ModelDownloader {
+
+    fun downloadModel(context: Context, url: String, fileName: String, hfToken: String = ""): Flow<Int> = flow {
+        val downloadManager = context.getSystemService(Context.DOWNLOAD_SERVICE) as DownloadManager
+
+        // Ensure the directory exists
+        val modelsDir = ModelRegistry.getModelsDirectory(context)
+
+        val request = DownloadManager.Request(Uri.parse(url))
+            .setTitle(fileName)
+            .setDescription("Downloading AI Model for Alice...")
+            .setNotificationVisibility(DownloadManager.Request.VISIBILITY_VISIBLE_NOTIFY_COMPLETED)
+            .setDestinationUri(Uri.fromFile(File(modelsDir, fileName)))
+            .setAllowedOverMetered(true) // Allow cellular downloads
+
+        // THE FIX: Inject the Hugging Face Authorization header so we bypass the gate
+        if (hfToken.isNotBlank()) {
+            request.addRequestHeader("Authorization", "Bearer $hfToken")
+        }
+
+        val downloadId = downloadManager.enqueue(request)
+        var finishDownload = false
+        var progress = 0
+
+        // Ping the OS every second to get the latest percentage
+        while (!finishDownload) {
+            val query = DownloadManager.Query().setFilterById(downloadId)
+            val cursor = downloadManager.query(query)
+
+            if (cursor.moveToFirst()) {
+                val statusIndex = cursor.getColumnIndex(DownloadManager.COLUMN_STATUS)
+                val status = cursor.getInt(statusIndex)
+
+                when (status) {
+                    DownloadManager.STATUS_SUCCESSFUL -> {
+                        finishDownload = true
+                        emit(100)
+                    }
+                    DownloadManager.STATUS_FAILED -> {
+                        finishDownload = true
+                        emit(-1) // Error state
+                    }
+                    DownloadManager.STATUS_RUNNING -> {
+                        val downloadedIndex = cursor.getColumnIndex(DownloadManager.COLUMN_BYTES_DOWNLOADED_SO_FAR)
+                        val totalIndex = cursor.getColumnIndex(DownloadManager.COLUMN_TOTAL_SIZE_BYTES)
+                        val bytesDownloaded = cursor.getLong(downloadedIndex)
+                        val bytesTotal = cursor.getLong(totalIndex)
+
+                        if (bytesTotal > 0) {
+                            progress = ((bytesDownloaded * 100L) / bytesTotal).toInt()
+                            emit(progress)
+                        }
+                    }
+                }
+            }
+            cursor.close()
+
+            if (!finishDownload) {
+                delay(1000)
+            }
+        }
+    }.flowOn(Dispatchers.IO)
+}

app/src/main/java/net/mmanningau/alice/ModelRegistry.kt

@@ -0,0 +1,92 @@
+package net.mmanningau.alice
+
+import android.content.Context
+import java.io.File
+
+data class LocalModel(
+    val id: String,
+    val name: String,
+    val description: String,
+    val fileName: String,
+    val downloadUrl: String,
+    val sizeMb: Int
+)
+
+object ModelRegistry {
+
+    val curatedModels = listOf(
+        LocalModel(
+            id = "qwen-0.5b",
+            name = "Qwen 2.5 (0.5B)",
+            description = "Ultra-light and lightning fast. Best for quick tasks and basic tool triggering.",
+            fileName = "qwen2.5-0.5b-instruct-q4_k_m.gguf",
+            downloadUrl = "https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct-GGUF/resolve/main/qwen2.5-0.5b-instruct-q4_k_m.gguf",
+            sizeMb = 398
+        ),
+        LocalModel(
+            id = "qwen-1.5b",
+            name = "Qwen 2.5 (1.5B)",
+            description = "The perfect daily driver. Excellent balance of speed, intelligence, and battery efficiency.",
+            fileName = "qwen2.5-1.5b-instruct-q4_k_m.gguf",
+            downloadUrl = "https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF/resolve/main/qwen2.5-1.5b-instruct-q4_k_m.gguf",
+            sizeMb = 1120
+        ),
+        LocalModel(
+            id = "qwen-coder-3b",
+            name = "Qwen 2.5 Coder (3B)",
+            description = "Specialized for programming. Fantastic for generating Python scripts and home lab configurations.",
+            fileName = "qwen2.5-coder-3b-instruct-q4_k_m.gguf",
+            downloadUrl = "https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct-GGUF/resolve/main/qwen2.5-coder-3b-instruct-q4_k_m.gguf",
+            sizeMb = 2020
+        ),
+        LocalModel(
+            id = "qwen-3b",
+            name = "Qwen 2.5 (3B)",
+            description = "The Heavyweight. The highest quality conversational responses your device can comfortably run.",
+            fileName = "qwen2.5-3b-instruct-q4_k_m.gguf",
+            downloadUrl = "https://huggingface.co/Qwen/Qwen2.5-3B-Instruct-GGUF/resolve/main/qwen2.5-3b-instruct-q4_k_m.gguf",
+            sizeMb = 2020
+        ),
+        LocalModel(
+            id = "gemma3-1b",
+            name = "Gemma 3 (1B)",
+            sizeMb = 555, // Update these sizes based on the exact HuggingFace .task file
+            description = "Google's highly optimized mobile intelligence. Best balance of speed and reasoning.",
+            fileName = "gemma-3-1b-it.task",
+            downloadUrl = "https://huggingface.co/litert-community/Gemma3-1B-IT/resolve/main/gemma3-1b-it-int4.task" // Update with exact raw URL
+        ),
+        LocalModel(
+            id = "gemma3n-e2b",
+            name = "Gemma 3n (E2B)",
+            sizeMb = 3600,
+            description = "Elastic architecture. Activates fewer parameters for battery efficiency while maintaining high logic.",
+            fileName = "gemma-3n-e2b-it.task",
+            downloadUrl = "https://huggingface.co/google/gemma-3n-E2B-it-litert-lm/resolve/main/gemma-3n-E2B-it-int4.litertlm"
+        ),
+        LocalModel(
+            id = "Qwen2.5-1.5B-Instruct_seq128_q8_ekv1280",
+            name = "Qwen2.5-1.5B",
+            sizeMb = 1570,
+            description = "A highly optimised and fine tuned model for agentic tasks and function calling.",
+            fileName = "Qwen2.5-1.5B-Instruct_seq128_q8_ekv1280.task",
+            downloadUrl = "https://huggingface.co/litert-community/Qwen2.5-1.5B-Instruct/resolve/main/Qwen2.5-1.5B-Instruct_seq128_q8_ekv1280.task"
+        )
+    )
+
+    fun getModelsDirectory(context: Context): File {
+        val dir = File(context.getExternalFilesDir(null), "Models")
+        if (!dir.exists()) {
+            dir.mkdirs()
+        }
+        return dir
+    }
+
+    fun isModelDownloaded(context: Context, fileName: String): Boolean {
+        val file = File(getModelsDirectory(context), fileName)
+        return file.exists() && file.length() > 0
+    }
+
+    fun getModelPath(context: Context, fileName: String): String {
+        return File(getModelsDirectory(context), fileName).absolutePath
+    }
+}

app/src/main/java/net/mmanningau/alice/SkillManager.kt

@@ -1,10 +1,12 @@
 package net.mmanningau.alice
 
 import android.content.Context
+import android.net.Uri
 import android.util.Log
 import com.chaquo.python.Python
 import dev.langchain4j.agent.tool.JsonSchemaProperty
 import dev.langchain4j.agent.tool.ToolSpecification
+import org.json.JSONArray
 import org.json.JSONObject
 import java.io.File
 
@@ -12,7 +14,11 @@ object SkillManager {
     var skillsDirectory: File? = null
         private set
 
+    // *** ADDED: store context for ContentResolver access
+    private var appContext: Context? = null
+
     fun initialize(context: Context) {
+        appContext = context.applicationContext // *** ADDED
         val baseDir = context.getExternalFilesDir(null)
         val skillsDir = File(baseDir, "Skills")
 
@@ -25,7 +31,7 @@ object SkillManager {
     fun updateDirectory(newPath: String) {
         val newDir = File(newPath)
         if (!newDir.exists()) {
-            newDir.mkdirs() // Create it if the user typed a new path
+            newDir.mkdirs()
         }
         skillsDirectory = newDir
         Log.i("AliceSkills", "Skills directory updated to: ${newDir.absolutePath}")
@@ -48,13 +54,12 @@ object SkillManager {
                             .name(name)
                             .description(description)
 
-                        // Parse the expected parameters so the LLM knows what to extract
                         val parameters = json.optJSONObject("parameters")
                         val properties = parameters?.optJSONObject("properties")
 
                         properties?.keys()?.forEach { key ->
                             val prop = properties.getJSONObject(key)
-                            val type = prop.getString("type") // e.g., "string"
+                            val type = prop.getString("type")
                             val desc = prop.optString("description", "")
 
                             builder.addParameter(
@@ -85,19 +90,56 @@ object SkillManager {
             val py = Python.getInstance()
             val builtins = py.builtins
 
-            // We create an isolated dictionary for the script to run in.
-            // This allows you to edit the Python files and have them hot-reload instantly!
             val globals = py.getModule("builtins").callAttr("dict")
-
-            // Execute the raw script text
             builtins.callAttr("exec", scriptFile.readText(), globals)
 
-            // Find the 'execute' function we mandated in our python script
             val executeFunc = globals.callAttr("get", "execute")
-            if (executeFunc == null) return "Error: Python script missing 'def execute(args_json):' function."
+            if (executeFunc == null) return "Error: Python script missing 'def execute(args):' function."
 
-            // Call it and return the string!
+            // First call to Python
-            executeFunc.call(argumentsJson).toString()
+            var result = executeFunc.call(argumentsJson).toString()
+
+            // *** ADDED: Two-pass bridge for skills that need Android ContentResolver
+            if (result.startsWith("BRIDGE_REQUEST:")) {
+                val ctx = appContext
+                if (ctx == null) {
+                    return "Error: SkillManager context not initialized — cannot perform ContentResolver query."
+                }
+
+                val request = JSONObject(result.removePrefix("BRIDGE_REQUEST:"))
+                val uri     = Uri.parse(request.getString("uri"))
+                val limit   = request.optInt("limit", 10)
+                val columns = arrayOf("_id", "address", "body", "date", "type", "read")
+
+                val smsArray = JSONArray()
+                val cursor = ctx.contentResolver.query(
+                    uri, columns, null, null, "date DESC"
+                )
+
+                cursor?.use {
+                    var count = 0
+                    while (it.moveToNext() && count < limit) {
+                        val row = JSONObject()
+                        row.put("address", it.getString(it.getColumnIndexOrThrow("address")) ?: "")
+                        row.put("body",    it.getString(it.getColumnIndexOrThrow("body"))    ?: "")
+                        row.put("date",    it.getString(it.getColumnIndexOrThrow("date"))    ?: "")
+                        row.put("type",    it.getString(it.getColumnIndexOrThrow("type"))    ?: "1")
+                        row.put("read",    it.getString(it.getColumnIndexOrThrow("read"))    ?: "0")
+                        smsArray.put(row)
+                        count++
+                    }
+                }
+
+                Log.i("AliceSkills", "SMS bridge: fetched ${smsArray.length()} messages from $uri")
+
+                // Re-inject the data and call Python a second time
+                val injectedArgs = JSONObject(argumentsJson.ifBlank { "{}" })
+                injectedArgs.put("sms_data", smsArray)
+                result = executeFunc.call(injectedArgs.toString()).toString()
+            }
+            // *** END ADDED
+
+            result
 
         } catch (e: Exception) {
             Log.e("AliceSkills", "Execution failed for $toolName", e)

app/src/main/java/net/mmanningau/alice/net/mmanningau/AliceApp.kt

@@ -38,6 +38,6 @@ class AliceApp : Application() {
         ).allowMainThreadQueries().build() // We use allowMainThreadQueries for immediate boot loading
 
         // Pass the DAO into the manager!
-        LlmManager.initialize(db.chatDao(), savedMode, savedUrl, savedModel, savedApiKey, savedSystemPrompt)
+        LlmManager.initialize(this,db.chatDao(), savedMode, savedUrl, savedModel, savedApiKey, savedSystemPrompt)
     }
 }

app/src/main/res/values/strings.xml

@@ -1,3 +1,4 @@
 <resources>
     <string name="app_name">Alice</string>
+    <string name="accessibility_service_description">Alice Screen Reader Service</string>
 </resources>

app/src/main/res/xml/accessibility_service_config.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="utf-8"?>
+<accessibility-service xmlns:android="http://schemas.android.com/apk/res/android"
+    android:accessibilityEventTypes="typeWindowStateChanged|typeWindowContentChanged"
+    android:accessibilityFeedbackType="feedbackGeneric"
+    android:accessibilityFlags="flagDefault"
+    android:canRetrieveWindowContent="true"
+    android:description="@string/accessibility_service_description" />

build.gradle.kts

@@ -5,4 +5,5 @@ plugins {
     alias(libs.plugins.kotlin.compose) apply false
     // Add the Chaquopy plugin here
     id("com.chaquo.python") version "15.0.1" apply false
+    id("com.google.devtools.ksp") version "2.2.0-2.0.2" apply false
 }

gradle/libs.versions.toml

@@ -1,6 +1,6 @@
 [versions]
 agp = "8.13.2"
-kotlin = "2.0.21"
+kotlin = "2.2.0"
 coreKtx = "1.17.0"
 junit = "4.13.2"
 junitVersion = "1.3.0"