import 'dart:convert'; import 'dart:io'; import 'dart:typed_data'; import 'package:flutter/widgets.dart'; import 'package:app/components/conversation_thread/conversation.dart'; import 'package:camera/camera.dart'; import 'package:http/http.dart' as http; import 'package:app/components/conversation_thread/chat.dart'; import 'package:app/components/util/speech_to_text.dart'; import 'model.dart'; // Uses Singleton design pattern to ensure that any server references across the app are using the same instance. class Backend { // SERVER VARIABLES static final Backend singleBackend = Backend._internal(); Backend._internal(); // TODO: One-time process to establish server connection is required for easy setup/maintenance. static const url = "http://10.0.2.2:11434/api/"; // For emulated device in testing //static final url = "http://192.168.1.68:11434/api"; // For local access to home server static String endpoint = ""; static final Map headers = {'Content-Type': 'application/json'}; static final bool UI_TESTING = true; // AI VARIABLES static final SpeechToText stt = SpeechToText(); static late bool _speechEnabled; // PRIVACY-NECESSARY VARIABLES // TODO: Reorder based on most recent usage. static List conversations = []; static List loadedChats = []; static late Conversation loadedConversation; static late List cameras; static late CameraController controller; // RUNTIME & STATUS VARIABLES static late int conversationID; static late int maxConversationID; static late Model model; static String prompt = ""; static String response = ""; static bool initiated = false; // TODO: Verify all different components of STT, TTS, LLM, etc have been initialized. // TODO: Handshake w/ server to validate identity factory Backend() { return singleBackend; } void init() async { // Prevent multiple initiations if(!initiated) { initiated = true; // Speech-To-Text engine activation. _speechEnabled = stt.isSpeechEnabled(); model = Model("llama3"); // Primarily used llama3, testing gemma2 maxConversationID = conversations.length; cameras = await availableCameras(); controller = CameraController(cameras[0], ResolutionPreset.max); controller.initialize(); // TODO: Add error catching on camera } // TODO: Dynamically get available models and models that can be pulled. // TODO: Dynamically get the user's default model // TODO: Dynamically obtain conversations. // TODO: Obtain all user permissions at once. } // PRIMARY INTERFACES @Deprecated("Use STT interface directly and use respondWhenReady().") static Future sttGetResponseWhenReady() async { // TODO: In case on-device STT is unavailable, use server-side STT service. if (!_speechEnabled) { return "Unable to process Speech-To-Text on-device."; } prompt = stt.getTextWhenReady() as String; conversations[conversationID].add(Chat(prompt, true, 0)); if(!UI_TESTING) { httpSendRequest(); } conversations[conversationID].add(Chat(response, false, 0)); return response; } static void respondWhenReady(String p) async { prompt = p; conversations[conversationID].add(Chat(prompt, true, 0)); if(!UI_TESTING) { httpSendRequest(); } } static ImageProvider convertFromBase64(img) { Uint8List imageBytes = base64Decode(img); return Image.memory(imageBytes).image; } // Converts image to base64 encoded string for data transfer. static String convertToBase64(f) { List imageBytes = f.readAsBytesSync(); String img = base64Encode(imageBytes); // TODO: Add special handling for images included in prompts. conversations[conversationID].add(Chat.image(img, true, 1)); return img; } // CONVERSATION MANAGEMENT @Deprecated("") static List getConversationsList() { return conversations; } static Conversation getLoadedConversation() { return loadedConversation; } // Load conversation should be called prior to any other server requests. static void loadConversation(id) { conversationID = id; loadedConversation = conversations[id]; loadedChats = loadedConversation.chats; } // TODO: Better conversation ID management is needed. Preferably randomized IDs to not indicate the number of conversations. static int createConversation() { int id = conversations.length; conversations.add(Conversation(id, "Conversation $id", "Another AI conversation")); return id; } static void deleteConversation(id) { loadedChats = conversations[conversationID].chats; // TODO: Temporarily saving the chats for "undo" popup. conversations.removeAt(id); } @Deprecated('Just directly get loadedChats list. Unless multiple chats can be opened simultaneously?') static List getChatsByConversationID(id) { return conversations[id].chats; } // SERVER UTILS static List> convertMessages() { // TODO: Integrate with getConversationByID() List> messageList = []; for (Chat c in loadedChats) { messageList.add({ "role": c.role ? "user" : "assistant", "content": c.content, }); } return messageList; } // Formats the prompt in JSON. static String constructPrompt() { // TODO: Allow for additional flags, ie. continuous conversation, images, etc. // TODO: Allow for toggleable states between stream // TODO: Allow for different models to be dynamically selected. // TODO: Further adaption of API calls to allow for images to/from the AI model // Example API call for image recognition // curl http://localhost:11434/api/generate -d '{ // "model": "llava", // "prompt":"What is in this picture?", // "stream": false, // "images": 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// }' Map data; if(loadedConversation.conversationContext) { loadedChats = [...loadedChats, Chat(prompt, true, 0)]; endpoint = "chat"; List> messageList = convertMessages(); // Last prompt was text if(loadedChats.last.chatType == 0) { data = { "model": model.name, "messages": messageList, "stream": loadedConversation.stream, }; // Last prompt was image } else { //if (loadedChats.last.chatType == 1) { // TODO: Do some wizardry to pipe the output of llava as additional context for the user's actual prompt or something. data = { "model": "llava", "prompt": "What is in this picture?", "stream": loadedConversation.stream, "images" : [loadedChats.last.content], }; } } else { endpoint = "generate"; data = { "model": model.name, "prompt": prompt, "stream": loadedConversation.stream, }; } // Encode the JSON payload return json.encode(data); } // Send the HTTP request to the server. static void httpSendRequest() async { // TODO: Add AES256 encryption to the prompt/messages thread. // TODO: Add SHA256 checksum to be sent and verified to prevent/notify in case of data loss. String json = constructPrompt(); // Send the HTTP POST request final serverResponse = await http.post( Uri.parse('$url$endpoint'), headers: headers, body: json, ); if (serverResponse.statusCode == 200) { Map re = jsonDecode(serverResponse.body); if(conversations[conversationID].conversationContext) { response = re["message"]["content"]; // TODO: Modify in case of different response types. loadedChats = [...loadedChats, Chat(response, false, 0)]; } else { response = re["response"]; } } conversations[conversationID].add(Chat(response, false, 0)); prompt = ""; response = ""; } }