mirror of
https://github.com/ParisNeo/lollms-webui.git
synced 2025-01-18 18:56:32 +00:00
Merge branch 'main' into scroll-controls
This commit is contained in:
AndzejsP
commit
75a34ad0f4
118
app.py
118
app.py
@ -22,6 +22,7 @@ import re
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import traceback
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import threading
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import sys
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from tqdm import tqdm
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from pyaipersonality import AIPersonality
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from pyGpt4All.db import DiscussionsDB, Discussion
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from flask import (
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@ -39,6 +40,7 @@ import gc
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from geventwebsocket.handler import WebSocketHandler
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from gevent.pywsgi import WSGIServer
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import requests
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from concurrent.futures import ThreadPoolExecutor, as_completed
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app = Flask("GPT4All-WebUI", static_url_path="/static", static_folder="static")
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socketio = SocketIO(app, cors_allowed_origins="*", async_mode='gevent', ping_timeout=30, ping_interval=15)
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@ -160,12 +162,7 @@ class Gpt4AllWebUI(GPT4AllAPI):
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self.add_endpoint(
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"/get_available_models", "get_available_models", self.get_available_models, methods=["GET"]
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)
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self.add_endpoint(
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"/install_model", "install_model", self.install_model, methods=["POST"]
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)
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self.add_endpoint(
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"/uninstall_model", "uninstall_model", self.uninstall_model, methods=["POST"]
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)
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self.add_endpoint(
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"/extensions", "extensions", self.extensions, methods=["GET"]
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@ -212,6 +209,85 @@ class Gpt4AllWebUI(GPT4AllAPI):
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@socketio.on('disconnect')
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def disconnect():
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print('Client disconnected')
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@socketio.on('install_model')
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def install_model(data):
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model_path = data["path"]
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progress = 0
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installation_dir = Path(f'./models/{self.config["backend"]}/')
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filename = Path(model_path).name
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installation_path = installation_dir / filename
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print("Model install requested")
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print(f"Model path : {model_path}")
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if installation_path.exists():
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print("Error: Model already exists")
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data.installing = False
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socketio.emit('install_progress',{'status': 'failed', 'error': 'model already exists'})
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socketio.emit('install_progress',{'status': 'progress', 'progress': progress})
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response = requests.get(model_path, stream=True)
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file_size = int(response.headers.get('Content-Length'))
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downloaded_size = 0
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CHUNK_SIZE = 8192
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def download_chunk(url, start_byte, end_byte, fileobj):
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headers = {'Range': f'bytes={start_byte}-{end_byte}'}
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response = requests.get(url, headers=headers, stream=True)
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downloaded_bytes = 0
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for chunk in response.iter_content(chunk_size=CHUNK_SIZE):
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if chunk:
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fileobj.seek(start_byte)
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fileobj.write(chunk)
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downloaded_bytes += len(chunk)
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start_byte += len(chunk)
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return downloaded_bytes
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def download_file(url, file_path, num_threads=4):
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response = requests.head(url)
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file_size = int(response.headers.get('Content-Length'))
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chunk_size = file_size // num_threads
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progress = 0
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with open(file_path, 'wb') as fileobj:
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with tqdm(total=file_size, unit='B', unit_scale=True, unit_divisor=1024) as pbar:
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with ThreadPoolExecutor(max_workers=num_threads) as executor:
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futures = []
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for i in range(num_threads):
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start_byte = i * chunk_size
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end_byte = start_byte + chunk_size - 1 if i < num_threads - 1 else file_size - 1
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futures.append(executor.submit(download_chunk, url, start_byte, end_byte, fileobj))
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for future in tqdm(as_completed(futures), total=num_threads):
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downloaded_bytes = future.result()
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progress += downloaded_bytes
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pbar.update(downloaded_bytes)
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socketio.emit('install_progress', {'status': 'progress', 'progress': progress})
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# Usage example
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download_file(model_path, installation_path, num_threads=4)
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socketio.emit('install_progress',{'status': 'succeeded', 'error': ''})
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@socketio.on('uninstall_model')
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def uninstall_model(data):
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model_path = data['path']
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installation_dir = Path(f'./models/{self.config["backend"]}/')
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filename = Path(model_path).name
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installation_path = installation_dir / filename
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if not installation_path.exists():
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socketio.emit('install_progress',{'status': 'failed', 'error': ''})
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installation_path.unlink()
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socketio.emit('install_progress',{'status': 'succeeded', 'error': ''})
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@socketio.on('generate_msg')
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def generate_msg(data):
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@ -702,7 +778,7 @@ class Gpt4AllWebUI(GPT4AllAPI):
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def get_available_models(self):
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response = requests.get(f' https://gpt4all.io/models/models.json')
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response = requests.get(f'https://gpt4all.io/models/models.json')
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model_list = response.json()
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models = []
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@ -710,7 +786,8 @@ class Gpt4AllWebUI(GPT4AllAPI):
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filename = model['filename']
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filesize = model['filesize']
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path = f'https://gpt4all.io/models/{filename}'
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is_installed = Path(f'/models/{self.config["backend"]}/{filename}').is_file()
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local_path = Path(f'./models/{self.config["backend"]}/{filename}')
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is_installed = local_path.exists()
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models.append({
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'title': model['filename'],
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'icon': '/icons/default.png', # Replace with the path to the model icon
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@ -721,31 +798,6 @@ class Gpt4AllWebUI(GPT4AllAPI):
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})
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return jsonify(models)
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def install_model(self):
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model_path = request.json.get('path')
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installation_dir = Path('/models/llamacpp/')
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filename = Path(model_path).name
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installation_path = installation_dir / filename
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if installation_path.exists():
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return jsonify({'status': 'Already installed'})
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response = requests.get(model_path)
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with open(installation_path, 'wb') as f:
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f.write(response.content)
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return jsonify({'status':True})
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def uninstall_model(self):
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model_path = request.json.get('path')
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installation_dir = Path('/models/llamacpp/')
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filename = Path(model_path).name
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installation_path = installation_dir / filename
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if not installation_path.exists():
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return jsonify({'status':False})
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installation_path.unlink()
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return jsonify({'status':True})
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def get_config(self):
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return jsonify(self.config)
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@ -17,12 +17,12 @@ __github__ = "https://github.com/nomic-ai/gpt4all-ui"
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__copyright__ = "Copyright 2023, "
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__license__ = "Apache 2.0"
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backend_name = "LLAMACPP"
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backend_name = "GPT4ALL"
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class LLAMACPP(GPTBackend):
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class GPT4ALL(GPTBackend):
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file_extension='*.bin'
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def __init__(self, config:dict) -> None:
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"""Builds a LLAMACPP backend
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"""Builds a GPT4ALL backend
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Args:
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config (dict): The configuration file
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@ -30,7 +30,7 @@ class LLAMACPP(GPTBackend):
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super().__init__(config, False)
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self.model = Model(
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model_path=f"./models/llama_cpp/{self.config['model']}",
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model_path=f"./models/gpt4all/{self.config['model']}",
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prompt_context="", prompt_prefix="", prompt_suffix="",
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n_ctx=self.config['ctx_size'],
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seed=self.config['seed'],
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@ -59,7 +59,7 @@ class GPT4AllAPI():
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self.chatbot_bindings = self.create_chatbot()
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print("Chatbot created successfully")
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except Exception:
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except Exception as ex:
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self.config["backend"] = "gpt4all"
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self.backend = self.load_backend(self.BACKENDS_LIST[self.config["backend"]])
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self.config["model"] = None
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@ -1,8 +1,5 @@
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<template>
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<div
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class="flex items-center p-4 hover:bg-primary-light rounded-lg mb-2 shadow-lg"
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:class="{ 'bg-primary-light': selected }"
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>
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<div class="flex items-center p-4 hover:bg-primary-light rounded-lg mb-2 shadow-lg">
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<div class="flex-shrink-0">
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<i :class="`fas ${icon} text-xl`"></i>
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</div>
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@ -22,15 +19,31 @@
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<button
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class="px-4 py-2 rounded-md text-white font-bold transition-colors duration-300"
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:class="[isInstalled ? 'bg-red-500 hover:bg-red-600' : 'bg-green-500 hover:bg-green-600']"
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:disabled="installing || uninstalling"
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@click="toggleInstall"
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>
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{{ isInstalled ? 'Uninstall' : 'Install' }}
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<template v-if="installing">
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<div class="flex items-center space-x-2">
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<div class="h-2 w-20 bg-gray-300 rounded"></div>
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<span>Installing...</span>
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</div>
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</template>
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<template v-else-if="uninstalling">
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<div class="flex items-center space-x-2">
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<div class="h-2 w-20 bg-gray-300 rounded"></div>
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<span>Uninstalling...</span>
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</div>
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</template>
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<template v-else>
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{{ isInstalled ? 'Uninstall' : 'Install' }}
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</template>
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</button>
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</div>
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</div>
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</template>
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<script>
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import { socket, state } from '@/services/websocket.js'
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export default {
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props: {
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title: String,
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@ -38,16 +51,33 @@ export default {
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path: String,
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description: String,
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isInstalled: Boolean,
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onToggleInstall: Function,
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selected: Boolean // Use a boolean selected prop
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onInstall: Function,
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onUninstall: Function,
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onSelected: Function,
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selected: Boolean
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},
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data() {
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return {
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installing: false,
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uninstalling: false
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};
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},
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methods: {
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toggleInstall() {
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this.onToggleInstall(this.isInstalled, this.path);
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if (this.isInstalled) {
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this.uninstalling = true;
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// Simulate uninstallation delay (replace this with your WebSocket logic)
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this.onUninstall(this);
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} else {
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this.installing = true;
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this.onInstall(this);
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}
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},
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handleSelection() {
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if (this.isInstalled && !this.selected) { // Only emit event if installed and not already selected
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this.$emit('update:selected', true);
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if (this.isInstalled && !this.selected) {
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this.selected=true;
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onSelected(this);
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}
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}
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}
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@ -52,17 +52,21 @@
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</select>
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</div>
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<div class="m-2">
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<label for="model" class="block mb-2 text-sm font-medium text-gray-900 dark:text-white">
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Model:
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</label>
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<select id="model"
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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">
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</div>
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</div>
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<div
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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">
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<div class="flex flex-row ">
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<button @click.stop="bec_collapsed = !bec_collapsed"
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class="text-2xl hover:text-primary duration-75 p-2 -m-2 w-full text-left active:translate-y-1">
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<!-- <i data-feather="chevron-right"></i> -->
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<option v-for="item in modelsArr" :selected="item === configFile.model">{{ item }}</option>
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||||
|
||||
</select>
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||||
</div>
|
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<h3 class="text-lg font-semibold cursor-pointer select-none "
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||||
@click.stop="bec_collapsed = !bec_collapsed">
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||||
Models zoo</h3>
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</button>
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||||
</div>
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<div :class="{ 'hidden': bec_collapsed }" class="flex flex-col mb-2 p-2">
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||||
<div v-if="models.length > 0" class="my-2">
|
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<label for="model" class="block ml-2 mb-2 text-sm font-medium text-gray-900 dark:text-white">
|
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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) {
|
||||
const endpoint = isInstalled ? '/uninstall_model' : '/install_model';
|
||||
axios.post(endpoint, { path })
|
||||
.then((response) => {
|
||||
console.log(response.data.status);
|
||||
onInstall(model_object) {
|
||||
let isInstalled = model_object.isInstalled
|
||||
let path = model_object.path
|
||||
this.showProgress = true;
|
||||
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);
|
||||
this.$set(this.models[index], 'isInstalled', isInstalled);
|
||||
})
|
||||
.catch((error) => {
|
||||
console.error(error);
|
||||
});
|
||||
model_object.isInstalled = false;
|
||||
|
||||
this.socket.off('install_progress', progressListener);
|
||||
} else if (response.status === 'failed') {
|
||||
// Installation failed or encountered an error
|
||||
model_object.uninstalling = false;
|
||||
this.showProgress = false;
|
||||
this.socket.off('install_progress', progressListener);
|
||||
console.error('Installation failed:', message.error);
|
||||
}
|
||||
};
|
||||
this.socket.on('install_progress', progressListener);
|
||||
this.socket.emit('uninstall_model', { path: model_object.path });
|
||||
},
|
||||
// messagebox ok stuff
|
||||
onMessageBoxOk() {
|
||||
@ -368,7 +423,19 @@ export default {
|
||||
//this.api_get_req("list_personalities_categories").then(response => { this.persCatgArr = response })
|
||||
//this.api_get_req("list_personalities").then(response => { this.persArr = response })
|
||||
//this.api_get_req("list_languages").then(response => { this.langArr = response })
|
||||
this.api_get_req("get_config").then(response => { this.configFile = response })
|
||||
this.api_get_req("get_config").then(response => {
|
||||
this.configFile = response
|
||||
console.log("selecting model")
|
||||
self.models.forEach(model => {
|
||||
console.log(`${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")
|
||||
|
||||
88
zoos/gpt4all_models_zoo.json
Normal file
88
zoos/gpt4all_models_zoo.json
Normal file
@ -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."
|
||||
}
|
||||
]
|
||||
Loading…
Reference in New Issue
Block a user