Merge branch 'main' into scroll-controls

app.py

@@ -22,6 +22,7 @@ import re
 import traceback
 import threading
 import sys
+from tqdm import tqdm
 from pyaipersonality import AIPersonality
 from pyGpt4All.db import DiscussionsDB, Discussion
 from flask import (
@@ -39,6 +40,7 @@ import gc
 from geventwebsocket.handler import WebSocketHandler
 from gevent.pywsgi import WSGIServer
 import requests
+from concurrent.futures import ThreadPoolExecutor, as_completed
 
 app = Flask("GPT4All-WebUI", static_url_path="/static", static_folder="static")
 socketio = SocketIO(app, cors_allowed_origins="*", async_mode='gevent', ping_timeout=30, ping_interval=15)
@@ -160,12 +162,7 @@ class Gpt4AllWebUI(GPT4AllAPI):
         self.add_endpoint(
             "/get_available_models", "get_available_models", self.get_available_models, methods=["GET"]
         )
-        self.add_endpoint(
+
-            "/install_model", "install_model", self.install_model, methods=["POST"]
-        )
-        self.add_endpoint(
-            "/uninstall_model", "uninstall_model", self.uninstall_model, methods=["POST"]
-        )
 
         self.add_endpoint(
             "/extensions", "extensions", self.extensions, methods=["GET"]
@@ -212,6 +209,85 @@ class Gpt4AllWebUI(GPT4AllAPI):
         @socketio.on('disconnect')
         def disconnect():
             print('Client disconnected')
+
+        @socketio.on('install_model')
+        def install_model(data):
+            model_path = data["path"]
+            progress = 0
+            installation_dir = Path(f'./models/{self.config["backend"]}/')
+            filename = Path(model_path).name
+            installation_path = installation_dir / filename
+            print("Model install requested")
+            print(f"Model path : {model_path}")
+
+            if installation_path.exists():
+                print("Error: Model already exists")
+                data.installing = False
+                socketio.emit('install_progress',{'status': 'failed', 'error': 'model already exists'})
+            
+            socketio.emit('install_progress',{'status': 'progress', 'progress': progress})
+
+            response = requests.get(model_path, stream=True)
+            file_size = int(response.headers.get('Content-Length'))
+            downloaded_size = 0
+            CHUNK_SIZE = 8192
+
+            def download_chunk(url, start_byte, end_byte, fileobj):
+                headers = {'Range': f'bytes={start_byte}-{end_byte}'}
+                response = requests.get(url, headers=headers, stream=True)
+                downloaded_bytes = 0
+
+                for chunk in response.iter_content(chunk_size=CHUNK_SIZE):
+                    if chunk:
+                        fileobj.seek(start_byte)
+                        fileobj.write(chunk)
+                        downloaded_bytes += len(chunk)
+                        start_byte += len(chunk)
+
+                return downloaded_bytes
+
+
+            def download_file(url, file_path, num_threads=4):
+                response = requests.head(url)
+                file_size = int(response.headers.get('Content-Length'))
+                chunk_size = file_size // num_threads
+                progress = 0
+
+                with open(file_path, 'wb') as fileobj:
+                    with tqdm(total=file_size, unit='B', unit_scale=True, unit_divisor=1024) as pbar:
+                        with ThreadPoolExecutor(max_workers=num_threads) as executor:
+                            futures = []
+
+                            for i in range(num_threads):
+                                start_byte = i * chunk_size
+                                end_byte = start_byte + chunk_size - 1 if i < num_threads - 1 else file_size - 1
+                                futures.append(executor.submit(download_chunk, url, start_byte, end_byte, fileobj))
+
+                            for future in tqdm(as_completed(futures), total=num_threads):
+                                downloaded_bytes = future.result()
+                                progress += downloaded_bytes
+                                pbar.update(downloaded_bytes)
+                                socketio.emit('install_progress', {'status': 'progress', 'progress': progress})
+
+            # Usage example
+            download_file(model_path, installation_path, num_threads=4)
+
+            socketio.emit('install_progress',{'status': 'succeeded', 'error': ''})
+            
+        @socketio.on('uninstall_model')
+        def uninstall_model(data):
+            model_path = data['path']
+            installation_dir = Path(f'./models/{self.config["backend"]}/')
+            filename = Path(model_path).name
+            installation_path = installation_dir / filename
+
+            if not installation_path.exists():
+                socketio.emit('install_progress',{'status': 'failed', 'error': ''})
+
+            installation_path.unlink()
+            socketio.emit('install_progress',{'status': 'succeeded', 'error': ''})
+            
+
         
         @socketio.on('generate_msg')
         def generate_msg(data):
@@ -702,7 +778,7 @@ class Gpt4AllWebUI(GPT4AllAPI):
     
     
     def get_available_models(self):
-        response = requests.get(f' https://gpt4all.io/models/models.json')
+        response = requests.get(f'https://gpt4all.io/models/models.json')
         model_list = response.json()
 
         models = []
@@ -710,7 +786,8 @@ class Gpt4AllWebUI(GPT4AllAPI):
             filename = model['filename']
             filesize = model['filesize']
             path = f'https://gpt4all.io/models/{filename}'
-            is_installed = Path(f'/models/{self.config["backend"]}/{filename}').is_file()
+            local_path = Path(f'./models/{self.config["backend"]}/{filename}')
+            is_installed = local_path.exists()
             models.append({
                 'title': model['filename'],
                 'icon': '/icons/default.png',  # Replace with the path to the model icon
@@ -721,31 +798,6 @@ class Gpt4AllWebUI(GPT4AllAPI):
             })
         return jsonify(models)
 
-    def install_model(self):
-        model_path = request.json.get('path')
-        installation_dir = Path('/models/llamacpp/')
-        filename = Path(model_path).name
-        installation_path = installation_dir / filename
-
-        if installation_path.exists():
-            return jsonify({'status': 'Already installed'})
-        
-        response = requests.get(model_path)
-        with open(installation_path, 'wb') as f:
-            f.write(response.content)        
-        return jsonify({'status':True})
-
-    def uninstall_model(self):
-        model_path = request.json.get('path')
-        installation_dir = Path('/models/llamacpp/')
-        filename = Path(model_path).name
-        installation_path = installation_dir / filename
-
-        if not installation_path.exists():
-            return jsonify({'status':False})
-
-        installation_path.unlink()        
-        return jsonify({'status':True})
     
     def get_config(self):
         return jsonify(self.config)

backends/gpt4all/__init__.py

@@ -17,12 +17,12 @@ __github__ = "https://github.com/nomic-ai/gpt4all-ui"
 __copyright__ = "Copyright 2023, "
 __license__ = "Apache 2.0"
 
-backend_name = "LLAMACPP"
+backend_name = "GPT4ALL"
 
-class LLAMACPP(GPTBackend):
+class GPT4ALL(GPTBackend):
     file_extension='*.bin'
     def __init__(self, config:dict) -> None:
-        """Builds a LLAMACPP backend
+        """Builds a GPT4ALL backend
 
         Args:
             config (dict): The configuration file
@@ -30,7 +30,7 @@ class LLAMACPP(GPTBackend):
         super().__init__(config, False)
         
         self.model = Model(
-                model_path=f"./models/llama_cpp/{self.config['model']}",
+                model_path=f"./models/gpt4all/{self.config['model']}",
                 prompt_context="", prompt_prefix="", prompt_suffix="",
                 n_ctx=self.config['ctx_size'], 
                 seed=self.config['seed'],

pyGpt4All/api.py

@@ -59,7 +59,7 @@ class GPT4AllAPI():
                 self.chatbot_bindings = self.create_chatbot()
                 print("Chatbot created successfully")
 
-            except Exception:
+            except Exception as ex:
                 self.config["backend"] = "gpt4all"
                 self.backend = self.load_backend(self.BACKENDS_LIST[self.config["backend"]])
                 self.config["model"] = None

web/src/components/ModelEntry.vue

@@ -1,8 +1,5 @@
 <template>
-  <div
+  <div class="flex items-center p-4 hover:bg-primary-light rounded-lg mb-2 shadow-lg">
-    class="flex items-center p-4 hover:bg-primary-light rounded-lg mb-2 shadow-lg"
-    :class="{ 'bg-primary-light': selected }"
-  >
     <div class="flex-shrink-0">
       <i :class="`fas ${icon} text-xl`"></i>
     </div>
@@ -22,15 +19,31 @@
       <button
         class="px-4 py-2 rounded-md text-white font-bold transition-colors duration-300"
         :class="[isInstalled ? 'bg-red-500 hover:bg-red-600' : 'bg-green-500 hover:bg-green-600']"
+        :disabled="installing || uninstalling"
         @click="toggleInstall"
       >
-        {{ isInstalled ? 'Uninstall' : 'Install' }}
+        <template v-if="installing">
+          <div class="flex items-center space-x-2">
+            <div class="h-2 w-20 bg-gray-300 rounded"></div>
+            <span>Installing...</span>
+          </div>
+        </template>
+        <template v-else-if="uninstalling">
+          <div class="flex items-center space-x-2">
+            <div class="h-2 w-20 bg-gray-300 rounded"></div>
+            <span>Uninstalling...</span>
+          </div>
+        </template>
+        <template v-else>
+          {{ isInstalled ? 'Uninstall' : 'Install' }}
+        </template>
       </button>
     </div>
   </div>
 </template>
 
 <script>
+import { socket, state } from '@/services/websocket.js'
 export default {
   props: {
     title: String,
@@ -38,16 +51,33 @@ export default {
     path: String,
     description: String,
     isInstalled: Boolean,
-    onToggleInstall: Function,
+    onInstall: Function,
-    selected: Boolean // Use a boolean selected prop
+    onUninstall: Function,
+    onSelected: Function,
+    selected: Boolean
+  },
+  data() {
+    return {
+      installing: false,
+      uninstalling: false
+    };
   },
   methods: {
     toggleInstall() {
-      this.onToggleInstall(this.isInstalled, this.path);
+      if (this.isInstalled) {
+        this.uninstalling = true;
+        // Simulate uninstallation delay (replace this with your WebSocket logic)
+        this.onUninstall(this);
+      } else {
+        this.installing = true;
+        this.onInstall(this);
+
+      }
     },
     handleSelection() {
-      if (this.isInstalled && !this.selected) { // Only emit event if installed and not already selected
+      if (this.isInstalled && !this.selected) {
-        this.$emit('update:selected', true);
+        this.selected=true;
+        onSelected(this);
       }
     }
   }

web/src/views/SettingsView.vue

@@ -52,17 +52,21 @@
 
                     </select>
                 </div>
-                <div class="m-2">
+            </div>
-                    <label for="model" class="block mb-2 text-sm font-medium text-gray-900 dark:text-white">
+        </div>
-                        Model:
+        <div
-                    </label>
+            class="flex flex-col mb-2 p-3 rounded-lg bg-bg-light-tone dark:bg-bg-dark-tone hover:bg-bg-light-tone-panel hover:dark:bg-bg-dark-tone-panel duration-150 shadow-lg">
-                    <select id="model"
+            <div class="flex flex-row ">
-                        class="bg-gray-50 border border-gray-300 text-gray-900 text-sm rounded-lg focus:ring-blue-500 focus:border-blue-500 block w-full p-2.5 dark:bg-gray-700 dark:border-gray-600 dark:placeholder-gray-400 dark:text-white dark:focus:ring-blue-500 dark:focus:border-blue-500">
+                <button @click.stop="bec_collapsed = !bec_collapsed"
+                    class="text-2xl hover:text-primary duration-75  p-2 -m-2 w-full text-left active:translate-y-1">
+                    <!-- <i data-feather="chevron-right"></i> -->
 
-                        <option v-for="item in modelsArr" :selected="item === configFile.model">{{ item }}</option>
+                    <h3 class="text-lg font-semibold cursor-pointer select-none "
-
+                        @click.stop="bec_collapsed = !bec_collapsed">
-                    </select>
+                        Models zoo</h3>
-                </div>
+                </button>
+            </div>
+            <div :class="{ 'hidden': bec_collapsed }" class="flex flex-col mb-2 p-2">
                 <div v-if="models.length > 0" class="my-2">
                     <label for="model" class="block ml-2 mb-2 text-sm font-medium text-gray-900 dark:text-white">
                         Install more models:
@@ -70,12 +74,13 @@
                     <div class="overflow-y-auto max-h-96 no-scrollbar p-2">
                         <model-entry v-for="(model, index) in models" :key="index" :title="model.title" :icon="model.icon"
                             :path="model.path" :description="model.description" :is-installed="model.isInstalled"
-                            :on-toggle-install="toggleInstall" />
+                            :on-install="onInstall"
+                            :on-uninstall="onUninstall"
+                            :on-selected="onSelected" />
                     </div>
                 </div>
             </div>
         </div>
-
         <!-- PERSONALITY -->
         <div
             class="flex flex-col mb-2 p-3 rounded-lg bg-bg-light-tone dark:bg-bg-dark-tone hover:bg-bg-light-tone-panel hover:dark:bg-bg-dark-tone-panel duration-150 shadow-lg">
@@ -292,6 +297,7 @@ import { nextTick } from 'vue'
 import MessageBox from "@/components/MessageBox.vue";
 import YesNoDialog from "@/components/YesNoDialog.vue";
 import ModelEntry from '@/components/ModelEntry.vue';
+import { socket, state } from '@/services/websocket.js'
 axios.defaults.baseURL = import.meta.env.VITE_GPT4ALL_API_BASEURL
 export default {
     components: {
@@ -309,6 +315,8 @@ export default {
     data() {
 
         return {
+            // Websocket
+            socket: socket,
             // Models zoo installer stuff
             models: [],
             // Accordeon stuff     
@@ -317,7 +325,6 @@ export default {
             mc_collapsed: false,
             // Settings stuff
             backendsArr: [],
-            modelsArr: [],
             persLangArr: [],
             persCatgArr: [],
             persArr: [],
@@ -339,19 +346,67 @@ export default {
                     console.log(error);
                 });
         },
+        onSelected(model_object){
+            console.log("Selected model")
+            update_setting('model', model_object.title)
+        },
         // Model installation
-        toggleInstall(isInstalled, path) {
+        onInstall(model_object) {
-            const endpoint = isInstalled ? '/uninstall_model' : '/install_model';
+            let isInstalled = model_object.isInstalled
-            axios.post(endpoint, { path })
+            let path = model_object.path
-                .then((response) => {
+            this.showProgress = true;
-                    console.log(response.data.status);
+            this.progress = 0;
+            console.log("installing...")
+
+            const progressListener = (response) => {
+                if (response.status === 'progress') {
+                this.progress = message.progress;
+                } else if (response.status === 'succeeded') {
+                // Installation completed
+                model_object.installing = false;
+                this.showProgress = false;
+                // Update the isInstalled property of the corresponding model
+                const index = this.models.findIndex((model) => model.path === path);
+                this.models[index].isInstalled = true;
+
+                this.socket.off('install_progress', progressListener);
+                } else if (response.status === 'failed') {
+                // Installation failed or encountered an error
+                model_object.installing = false;
+                this.showProgress = false;
+                this.socket.off('install_progress', progressListener);
+                console.error('Installation failed:', message.error);
+                }
+            };
+            this.socket.on('install_progress', progressListener);            
+            this.socket.emit('install_model', { path: path });
+            
+
+        },
+        onUninstall(model_object) {
+            console.log("uninstalling model...")
+            const progressListener = (response) => {
+                if (response.status === 'progress') {
+                    this.progress = message.progress;
+                } else if (response.status === 'succeeded') {
+                    // Installation completed
+                    model_object.uninstalling = false;
+
+                    this.showProgress = false;
                     // Update the isInstalled property of the corresponding model
-                    const index = this.models.findIndex((model) => model.path === path);
+                    model_object.isInstalled = false;
-                    this.$set(this.models[index], 'isInstalled', isInstalled);
+
-                })
+                    this.socket.off('install_progress', progressListener);
-                .catch((error) => {
+                } else if (response.status === 'failed') {
-                    console.error(error);
+                    // Installation failed or encountered an error
-                });
+                    model_object.uninstalling = false;
+                    this.showProgress = false;
+                    this.socket.off('install_progress', progressListener);
+                    console.error('Installation failed:', message.error);
+                }
+            };
+            this.socket.on('install_progress', progressListener);            
+            this.socket.emit('uninstall_model', { path: model_object.path });
         },
         // messagebox ok stuff
         onMessageBoxOk() {
@@ -368,7 +423,19 @@ export default {
             //this.api_get_req("list_personalities_categories").then(response => { this.persCatgArr = response })
             //this.api_get_req("list_personalities").then(response => { this.persArr = response })
             //this.api_get_req("list_languages").then(response => { this.langArr = response })
-            this.api_get_req("get_config").then(response => { this.configFile = response })
+            this.api_get_req("get_config").then(response => { 
+                this.configFile = response 
+                console.log("selecting model")
+                self.models.forEach(model => {
+                    console.log(`${model} -> ${response["model"]}`)
+                    if(model.title==response["model"]){
+                        model.selected=true;
+                    }
+                    else{
+                        model.selected=false;
+                    }
+                }); 
+            })
         },
         // Accordeon stuff
         toggleAccordion() {
@@ -467,7 +534,6 @@ export default {
         this.configFile = await this.api_get_req("get_config")
 
         this.backendsArr = await this.api_get_req("list_backends")
-        this.modelsArr = await this.api_get_req("list_models")
         this.persLangArr = await this.api_get_req("list_personalities_languages")
         this.persCatgArr = await this.api_get_req("list_personalities_categories")
         this.persArr = await this.api_get_req("list_personalities")

zoos/gpt4all_models_zoo.json

@@ -0,0 +1,88 @@
+[
+    {
+      "md5sum": "81a09a0ddf89690372fc296ff7f625af",
+      "filename": "ggml-gpt4all-j-v1.3-groovy.bin",
+      "filesize": "3785248281",
+      "isDefault": "true",
+      "bestGPTJ": "true",
+      "description": "Current best commercially licensable model based on GPT-J and trained by Nomic AI on the latest curated GPT4All dataset."
+    },
+    {
+      "md5sum": "91f886b68fbce697e9a3cd501951e455",
+      "filename": "ggml-gpt4all-l13b-snoozy.bin",
+      "filesize": "8136770688",
+      "bestLlama": "true",
+      "description": "Current best non-commercially licensable model based on Llama 13b and trained by Nomic AI on the latest curated GPT4All dataset."
+    },
+    {
+      "md5sum": "756249d3d6abe23bde3b1ae272628640",
+      "filename": "ggml-mpt-7b-chat.bin",
+      "filesize": "4854401050",
+      "isDefault": "true",
+      "bestMPT": "true",
+      "requires": "2.4.1",
+      "description": "Current best non-commercially licensable chat model based on MPT and trained by Mosaic ML."
+    },
+    {
+      "md5sum": "879344aaa9d62fdccbda0be7a09e7976",
+      "filename": "ggml-gpt4all-j-v1.2-jazzy.bin",
+      "filesize": "3785248281",
+      "description": "A commercially licensable model based on GPT-J and trained by Nomic AI on the v2 GPT4All dataset."
+    },
+    {
+      "md5sum": "61d48a82cb188cceb14ebb8082bfec37",
+      "filename": "ggml-gpt4all-j-v1.1-breezy.bin",
+      "filesize": "3785248281",
+      "description": "A commercially licensable model based on GPT-J and trained by Nomic AI on the v1 GPT4All dataset."
+    },
+    {
+      "md5sum": "5b5a3f9b858d33b29b52b89692415595",
+      "filename": "ggml-gpt4all-j.bin",
+      "filesize": "3785248281",
+      "description": "A commercially licensable model based on GPT-J and trained by Nomic AI on the v0 GPT4All dataset."
+    },
+    {
+      "md5sum": "29119f8fa11712704c6b22ac5ab792ea",
+      "filename": "ggml-vicuna-7b-1.1-q4_2.bin",
+      "filesize": "4212859520",
+      "description": "A non-commercially licensable model based on Llama 7b and trained by teams from UC Berkeley, CMU, Stanford, MBZUAI, and UC San Diego."
+    },
+    {
+      "md5sum": "95999b7b0699e2070af63bf5d34101a8",
+      "filename": "ggml-vicuna-13b-1.1-q4_2.bin",
+      "filesize": "8136770688",
+      "description": "A non-commercially licensable model based on Llama 13b and trained by teams from UC Berkeley, CMU, Stanford, MBZUAI, and UC San Diego."
+    },
+    {
+      "md5sum": "99e6d129745a3f1fb1121abed747b05a",
+      "filename": "ggml-wizardLM-7B.q4_2.bin",
+      "filesize": "4212864640",
+      "description": "A non-commercially licensable model based on Llama 7b and trained by Microsoft and Peking University."
+    },
+    {
+      "md5sum": "6cb4ee297537c9133bddab9692879de0",
+      "filename": "ggml-stable-vicuna-13B.q4_2.bin",
+      "filesize": "8136777088",
+      "description": "A non-commercially licensable model based on Llama 13b and RLHF trained by Stable AI."
+    },
+    {
+      "md5sum": "120c32a51d020066288df045ef5d52b9",
+      "filename": "ggml-mpt-7b-base.bin",
+      "filesize": "4854401028",
+      "requires": "2.4.1",
+      "description": "A commercially licensable model base pre-trained by Mosaic ML."
+    },
+    {
+      "md5sum": "d5eafd5b0bd0d615cfd5fd763f642dfe",
+      "filename": "ggml-nous-gpt4-vicuna-13b.bin",
+      "filesize": "8136777088",
+      "description": "A non-commercially licensable model based on Vicuna 13b, fine-tuned on ~180,000 instructions, trained by Nous Research."
+    },
+    {
+      "md5sum": "1cfa4958f489f0a0d1ffdf6b37322809",
+      "filename": "ggml-mpt-7b-instruct.bin",
+      "filesize": "4854401028",
+      "requires": "2.4.1",
+      "description": "A commericially licensable instruct model based on MPT and trained by Mosaic ML."
+    }
+  ]