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GNU GENERAL PUBLIC LICENSE
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# alpaca
A description of this project.

51
com.jeffser.Alpaca.json Normal file
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{
"id" : "com.jeffser.Alpaca",
"runtime" : "org.gnome.Platform",
"runtime-version" : "46",
"sdk" : "org.gnome.Sdk",
"command" : "alpaca",
"finish-args" : [
"--share=network",
"--share=ipc",
"--socket=fallback-x11",
"--device=dri",
"--socket=wayland"
],
"cleanup" : [
"/include",
"/lib/pkgconfig",
"/man",
"/share/doc",
"/share/gtk-doc",
"/share/man",
"/share/pkgconfig",
"*.la",
"*.a"
],
"modules" : [
{
"name": "requests",
"buildsystem": "simple",
"build-options": {
"build-args": [
"--share=network"
]
},
"build-commands": [
"pip3 install --prefix=/app --no-cache-dir requests"
]
},
{
"name" : "alpaca",
"builddir" : true,
"buildsystem" : "meson",
"sources" : [
{
"type" : "git",
"url" : "file:///home/tentri/Documents/Projects"
}
]
}
]
}

8
data/com.jeffser.Alpaca.desktop.in Normal file
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[Desktop Entry]
Name=alpaca
Exec=alpaca
Icon=com.jeffser.Alpaca
Terminal=false
Type=Application
Categories=GTK;
StartupNotify=true

5
data/com.jeffser.Alpaca.gschema.xml Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<schemalist gettext-domain="alpaca">
<schema id="com.jeffser.Alpaca" path="/com/jeffser/Alpaca/">
</schema>
</schemalist>

9
data/com.jeffser.Alpaca.metainfo.xml.in Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<component type="desktop">
<id>com.jeffser.Alpaca.desktop</id>
<metadata_license>CC0-1.0</metadata_license>
<project_license>GPL-3.0-or-later</project_license>
<description>
<p>No description</p>
</description>
</component>

130
data/icons/hicolor/scalable/apps/com.jeffser.Alpaca.svg Normal file
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data/icons/hicolor/symbolic/apps/com.jeffser.Alpaca-symbolic.svg Normal file
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data/icons/meson.build Normal file
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application_id = 'com.jeffser.Alpaca'
scalable_dir = 'hicolor' / 'scalable' / 'apps'
install_data(
scalable_dir / ('@0@.svg').format(application_id),
install_dir: get_option('datadir') / 'icons' / scalable_dir
)
symbolic_dir = 'hicolor' / 'symbolic' / 'apps'
install_data(
symbolic_dir / ('@0@-symbolic.svg').format(application_id),
install_dir: get_option('datadir') / 'icons' / symbolic_dir
)

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data/meson.build Normal file
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desktop_file = i18n.merge_file(
input: 'com.jeffser.Alpaca.desktop.in',
output: 'com.jeffser.Alpaca.desktop',
type: 'desktop',
po_dir: '../po',
install: true,
install_dir: get_option('datadir') / 'applications'
)
desktop_utils = find_program('desktop-file-validate', required: false)
if desktop_utils.found()
test('Validate desktop file', desktop_utils, args: [desktop_file])
endif
appstream_file = i18n.merge_file(
input: 'com.jeffser.Alpaca.metainfo.xml.in',
output: 'com.jeffser.Alpaca.metainfo.xml',
po_dir: '../po',
install: true,
install_dir: get_option('datadir') / 'metainfo'
)
appstreamcli = find_program('appstreamcli', required: false, disabler: true)
test('Validate appstream file', appstreamcli,
args: ['validate', '--no-net', '--explain', appstream_file])
install_data('com.jeffser.Alpaca.gschema.xml',
install_dir: get_option('datadir') / 'glib-2.0' / 'schemas'
)
compile_schemas = find_program('glib-compile-schemas', required: false, disabler: true)
test('Validate schema file',
compile_schemas,
args: ['--strict', '--dry-run', meson.current_source_dir()])
subdir('icons')

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meson.build Normal file
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project('alpaca',
version: '0.1.0',
meson_version: '>= 0.62.0',
default_options: [ 'warning_level=2', 'werror=false', ],
)
i18n = import('i18n')
gnome = import('gnome')
subdir('data')
subdir('src')
subdir('po')
gnome.post_install(
glib_compile_schemas: true,
gtk_update_icon_cache: true,
update_desktop_database: true,
)

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data/com.jeffser.Alpaca.desktop.in
data/com.jeffser.Alpaca.metainfo.xml.in
data/com.jeffser.Alpaca.gschema.xml
src/main.py
src/window.py
src/window.ui

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po/meson.build Normal file
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i18n.gettext('alpaca', preset: 'glib')

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python3-requests.json Normal file
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{
"name": "python3-requests",
"buildsystem": "simple",
"build-commands": [
"pip3 install --verbose --exists-action=i --no-index --find-links=\"file://${PWD}\" --prefix=${FLATPAK_DEST} \"requests\" --no-build-isolation"
],
"sources": [
{
"type": "file",
"url": "https://files.pythonhosted.org/packages/ba/06/a07f096c664aeb9f01624f858c3add0a4e913d6c96257acb4fce61e7de14/certifi-2024.2.2-py3-none-any.whl",
"sha256": "dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"
},
{
"type": "file",
"url": "https://files.pythonhosted.org/packages/63/09/c1bc53dab74b1816a00d8d030de5bf98f724c52c1635e07681d312f20be8/charset-normalizer-3.3.2.tar.gz",
"sha256": "f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"
},
{
"type": "file",
"url": "https://files.pythonhosted.org/packages/e5/3e/741d8c82801c347547f8a2a06aa57dbb1992be9e948df2ea0eda2c8b79e8/idna-3.7-py3-none-any.whl",
"sha256": "82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"
},
{
"type": "file",
"url": "https://files.pythonhosted.org/packages/70/8e/0e2d847013cb52cd35b38c009bb167a1a26b2ce6cd6965bf26b47bc0bf44/requests-2.31.0-py3-none-any.whl",
"sha256": "58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f"
},
{
"type": "file",
"url": "https://files.pythonhosted.org/packages/a2/73/a68704750a7679d0b6d3ad7aa8d4da8e14e151ae82e6fee774e6e0d05ec8/urllib3-2.2.1-py3-none-any.whl",
"sha256": "450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"
}
]
}

0
src/__init__.py Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<gresources>
<gresource prefix="/com/jeffser/Alpaca">
<file preprocess="xml-stripblanks">window.ui</file>
<file preprocess="xml-stripblanks">gtk/help-overlay.ui</file>
</gresource>
</gresources>

46
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#!@PYTHON@
# alpaca.in
#
# Copyright 2024 Unknown
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
#
# SPDX-License-Identifier: GPL-3.0-or-later
import os
import sys
import signal
import locale
import gettext
VERSION = '@VERSION@'
pkgdatadir = '@pkgdatadir@'
localedir = '@localedir@'
sys.path.insert(1, pkgdatadir)
signal.signal(signal.SIGINT, signal.SIG_DFL)
locale.bindtextdomain('alpaca', localedir)
locale.textdomain('alpaca')
gettext.install('alpaca', localedir)
if __name__ == '__main__':
import gi
from gi.repository import Gio
resource = Gio.Resource.load(os.path.join(pkgdatadir, 'alpaca.gresource'))
resource._register()
from alpaca import main
sys.exit(main.main(VERSION))

91
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# available_models.py
# There isn't an API to do this, sorry
available_models = {
"llama3":"Meta Llama 3: The most capable openly available LLM to date",
"phi3":"Phi-3 Mini is a 3.8B parameters, lightweight, state-of-the-art open model by Microsoft.",
"wizardlm2":"State of the art large language model from Microsoft AI with improved performance on complex chat, multilingual, reasoning and agent use cases.",
"mistral":"The 7B model released by Mistral AI, updated to version 0.2.",
"gemma":"Gemma is a family of lightweight, state-of-the-art open models built by Google DeepMind. Updated to version 1.1",
"mixtral":"A set of Mixture of Experts (MoE) model with open weights by Mistral AI in 8x7b and 8x22b parameter sizes.",
"llama2":"Llama 2 is a collection of foundation language models ranging from 7B to 70B parameters.",
"codegemma":"CodeGemma is a collection of powerful, lightweight models that can perform a variety of coding tasks like fill-in-the-middle code completion, code generation, natural language understanding, mathematical reasoning, and instruction following.",
"command-r":"Command R is a Large Language Model optimized for conversational interaction and long context tasks.",
"command-r-plus":"Command R+ is a powerful, scalable large language model purpose-built to excel at real-world enterprise use cases.",
"llava":"🌋 LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.",
"dbrx":"DBRX is an open, general-purpose LLM created by Databricks.",
"codellama":"A large language model that can use text prompts to generate and discuss code.",
"qwen":"Qwen 1.5 is a series of large language models by Alibaba Cloud spanning from 0.5B to 110B parameters",
"dolphin-mixtral":"Uncensored, 8x7b and 8x22b fine-tuned models based on the Mixtral mixture of experts models that excels at coding tasks. Created by Eric Hartford.",
"llama2-uncensored":"Uncensored Llama 2 model by George Sung and Jarrad Hope.",
"deepseek-coder":"DeepSeek Coder is a capable coding model trained on two trillion code and natural language tokens.",
"mistral-openorca":"Mistral OpenOrca is a 7 billion parameter model, fine-tuned on top of the Mistral 7B model using the OpenOrca dataset.",
"nomic-embed-text":"A high-performing open embedding model with a large token context window.",
"phi":"Phi-2: a 2.7B language model by Microsoft Research that demonstrates outstanding reasoning and language understanding capabilities.",
"dolphin-mistral":"The uncensored Dolphin model based on Mistral that excels at coding tasks. Updated to version 2.8.",
"orca-mini":"A general-purpose model ranging from 3 billion parameters to 70 billion, suitable for entry-level hardware.",
"nous-hermes2":"The powerful family of models by Nous Research that excels at scientific discussion and coding tasks.",
"zephyr":"Zephyr is a series of fine-tuned versions of the Mistral and Mixtral models that are trained to act as helpful assistants.",
"llama2-chinese":"Llama 2 based model fine tuned to improve Chinese dialogue ability.",
"wizard-vicuna-uncensored":"Wizard Vicuna Uncensored is a 7B, 13B, and 30B parameter model based on Llama 2 uncensored by Eric Hartford.",
"vicuna":"General use chat model based on Llama and Llama 2 with 2K to 16K context sizes.",
"starcoder2":"StarCoder2 is the next generation of transparently trained open code LLMs that comes in three sizes: 3B, 7B and 15B parameters.",
"openhermes":"OpenHermes 2.5 is a 7B model fine-tuned by Teknium on Mistral with fully open datasets.",
"tinyllama":"The TinyLlama project is an open endeavor to train a compact 1.1B Llama model on 3 trillion tokens.",
"openchat":"A family of open-source models trained on a wide variety of data, surpassing ChatGPT on various benchmarks. Updated to version 3.5-0106.",
"tinydolphin":"An experimental 1.1B parameter model trained on the new Dolphin 2.8 dataset by Eric Hartford and based on TinyLlama.",
"starcoder":"StarCoder is a code generation model trained on 80+ programming languages.",
"wizardcoder":"State-of-the-art code generation model",
"stable-code":"Stable Code 3B is a coding model with instruct and code completion variants on par with models such as Code Llama 7B that are 2.5x larger.",
"dolphin-llama3":"Dolphin 2.9 is a new model with 8B and 70B sizes by Eric Hartford based on Llama 3 that has a variety of instruction, conversational, and coding skills.",
"yi":"A high-performing, bilingual language model.",
"mxbai-embed-large":"State-of-the-art large embedding model from mixedbread.ai",
"neural-chat":"A fine-tuned model based on Mistral with good coverage of domain and language.",
"phind-codellama":"Code generation model based on Code Llama.",
"wizard-math":"Model focused on math and logic problems",
"starling-lm":"Starling is a large language model trained by reinforcement learning from AI feedback focused on improving chatbot helpfulness.",
"falcon":"A large language model built by the Technology Innovation Institute (TII) for use in summarization, text generation, and chat bots.",
"orca2":"Orca 2 is built by Microsoft research, and are a fine-tuned version of Meta's Llama 2 models. The model is designed to excel particularly in reasoning.",
"dolphincoder":"A 7B and 15B uncensored variant of the Dolphin model family that excels at coding, based on StarCoder2.",
"dolphin-phi":"2.7B uncensored Dolphin model by Eric Hartford, based on the Phi language model by Microsoft Research.",
"nous-hermes":"General use models based on Llama and Llama 2 from Nous Research.",
"sqlcoder":"SQLCoder is a code completion model fined-tuned on StarCoder for SQL generation tasks",
"solar":"A compact, yet powerful 10.7B large language model designed for single-turn conversation.",
"stablelm2":"Stable LM 2 is a state-of-the-art 1.6B and 12B parameter language model trained on multilingual data in English, Spanish, German, Italian, French, Portuguese, and Dutch.",
"bakllava":"BakLLaVA is a multimodal model consisting of the Mistral 7B base model augmented with the LLaVA architecture.",
"medllama2":"Fine-tuned Llama 2 model to answer medical questions based on an open source medical dataset.",
"yarn-llama2":"An extension of Llama 2 that supports a context of up to 128k tokens.",
"deepseek-llm":"An advanced language model crafted with 2 trillion bilingual tokens.",
"nous-hermes2-mixtral":"The Nous Hermes 2 model from Nous Research, now trained over Mixtral.",
"wizardlm-uncensored":"Uncensored version of Wizard LM model",
"codeqwen":"CodeQwen1.5 is a large language model pretrained on a large amount of code data.",
"all-minilm":"Embedding models on very large sentence level datasets.",
"samantha-mistral":"A companion assistant trained in philosophy, psychology, and personal relationships. Based on Mistral.",
"codeup":"Great code generation model based on Llama2.",
"stable-beluga":"Llama 2 based model fine tuned on an Orca-style dataset. Originally called Free Willy.",
"llama3-gradient":"This model extends LLama-3 8B's context length from 8k to over 1m tokens.",
"everythinglm":"Uncensored Llama2 based model with support for a 16K context window.",
"xwinlm":"Conversational model based on Llama 2 that performs competitively on various benchmarks.",
"yarn-mistral":"An extension of Mistral to support context windows of 64K or 128K.",
"meditron":"Open-source medical large language model adapted from Llama 2 to the medical domain.",
"wizardlm":"General use model based on Llama 2.",
"llama-pro":"An expansion of Llama 2 that specializes in integrating both general language understanding and domain-specific knowledge, particularly in programming and mathematics.",
"magicoder":"🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.",
"stablelm-zephyr":"A lightweight chat model allowing accurate, and responsive output without requiring high-end hardware.",
"codebooga":"A high-performing code instruct model created by merging two existing code models.",
"nexusraven":"Nexus Raven is a 13B instruction tuned model for function calling tasks.",
"mistrallite":"MistralLite is a fine-tuned model based on Mistral with enhanced capabilities of processing long contexts.",
"wizard-vicuna":"Wizard Vicuna is a 13B parameter model based on Llama 2 trained by MelodysDreamj.",
"goliath":"A language model created by combining two fine-tuned Llama 2 70B models into one.",
"open-orca-platypus2":"Merge of the Open Orca OpenChat model and the Garage-bAInd Platypus 2 model. Designed for chat and code generation.",
"notux":"A top-performing mixture of experts model, fine-tuned with high-quality data.",
"megadolphin":"MegaDolphin-2.2-120b is a transformation of Dolphin-2.2-70b created by interleaving the model with itself.",
"snowflake-arctic-embed":"A suite of text embedding models by Snowflake, optimized for performance.",
"duckdb-nsql":"7B parameter text-to-SQL model made by MotherDuck and Numbers Station.",
"moondream":"moondream is a small vision language model designed to run efficiently on edge devices.",
"notus":"A 7B chat model fine-tuned with high-quality data and based on Zephyr.",
"alfred":"A robust conversational model designed to be used for both chat and instruct use cases.",
"llava-llama3":"A LLaVA model fine-tuned from Llama 3 Instruct with better scores in several benchmarks.",
"llama3-chatqa":"A model from NVIDIA based on Llama 3 that excels at conversational question answering (QA) and retrieval-augmented generation (RAG).",
"llava-phi3":"A new small LLaVA model fine-tuned from Phi 3 Mini."
}

61
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# connectionhandler.py
import json, requests
def simple_get(connection_url:str) -> dict:
try:
response = requests.get(connection_url)
if response.status_code == 200:
return {"status": "ok", "text": response.text, "status_code": response.status_code}
else:
return {"status": "error", "text": f"Failed to connect to {connection_url}. Status code: {response.status_code}", "status_code": response.status_code}
except Exception as e:
return {"status": "error", "text": f"An error occurred while trying to connect to {connection_url}", "status_code": 0}
def simple_delete(connection_url:str, data) -> dict:
try:
response = requests.delete(connection_url, json=data)
if response.status_code == 200:
return {"status": "ok", "status_code": response.status_code}
else:
return {"status": "error", "text": "Failed to delete", "status_code": response.status_code}
except Exception as e:
return {"status": "error", "text": f"An error occurred while trying to connect to {connection_url}", "status_code": 0}
def stream_post(connection_url:str, data, callback:callable) -> dict:
try:
headers = {
"Content-Type": "application/json"
}
response = requests.post(connection_url, headers=headers, data=data, stream=True)
if response.status_code == 200:
for line in response.iter_lines():
if line:
callback(json.loads(line.decode("utf-8")))
return {"status": "ok", "text": "All good", "status_code": response.status_code}
else:
return {"status": "error", "text": "Error posting data", "status_code": response.status_code}
except Exception as e:
return {"status": "error", "text": f"An error occurred while trying to connect to {connection_url}", "status_code": 0}
from time import sleep
def stream_post_fake(connection_url:str, data, callback:callable) -> dict:
data = {
"status": "pulling manifest"
}
callback(data)
for i in range(2):
for a in range(11):
sleep(.1)
data = {
"status": f"downloading digestname {i}",
"digest": f"digestname {i}",
"total": 500,
"completed": a * 50
}
callback(data)
for msg in ["verifying sha256 digest", "writting manifest", "removing any unused layers", "success"]:
sleep(.1)
data = {"status": msg}
callback(data)
return {"status": "ok", "text": "All good", "status_code": 200}

29
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<?xml version="1.0" encoding="UTF-8"?>
<interface>
<object class="GtkShortcutsWindow" id="help_overlay">
<property name="modal">True</property>
<child>
<object class="GtkShortcutsSection">
<property name="section-name">shortcuts</property>
<property name="max-height">10</property>
<child>
<object class="GtkShortcutsGroup">
<property name="title" translatable="yes" context="shortcut window">General</property>
<child>
<object class="GtkShortcutsShortcut">
<property name="title" translatable="yes" context="shortcut window">Show Shortcuts</property>
<property name="action-name">win.show-help-overlay</property>
</object>
</child>
<child>
<object class="GtkShortcutsShortcut">
<property name="title" translatable="yes" context="shortcut window">Quit</property>
<property name="action-name">app.quit</property>
</object>
</child>
</object>
</child>
</object>
</child>
</object>
</interface>

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# main.py
#
# Copyright 2024 Unknown
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
#
# SPDX-License-Identifier: GPL-3.0-or-later
import sys
import gi
gi.require_version('Gtk', '4.0')
gi.require_version('Adw', '1')
from gi.repository import Gtk, Gio, Adw
from .window import AlpacaWindow
class AlpacaApplication(Adw.Application):
"""The main application singleton class."""
def __init__(self):
super().__init__(application_id='com.jeffser.Alpaca',
flags=Gio.ApplicationFlags.DEFAULT_FLAGS)
self.create_action('quit', lambda *_: self.quit(), ['<primary>q'])
self.create_action('about', self.on_about_action)
self.create_action('preferences', self.on_preferences_action)
def do_activate(self):
win = self.props.active_window
if not win:
win = AlpacaWindow(application=self)
win.present()
def on_about_action(self, widget, _):
about = Adw.AboutWindow(transient_for=self.props.active_window,
application_name='Alpaca',
application_icon='com.jeffser.Alpaca',
developer_name='Jeffry Samuel Eduarte Rojas',
version='0.1.0',
developers=['Jeffser'],
copyright='© 2024 Jeffser')
about.present()
def on_preferences_action(self, widget, _):
print('app.preferences action activated')
def create_action(self, name, callback, shortcuts=None):
action = Gio.SimpleAction.new(name, None)
action.connect("activate", callback)
self.add_action(action)
if shortcuts:
self.set_accels_for_action(f"app.{name}", shortcuts)
def main(version):
app = AlpacaApplication()
return app.run(sys.argv)

37
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pkgdatadir = get_option('prefix') / get_option('datadir') / meson.project_name()
moduledir = pkgdatadir / 'alpaca'
gnome = import('gnome')
gnome.compile_resources('alpaca',
'alpaca.gresource.xml',
gresource_bundle: true,
install: true,
install_dir: pkgdatadir,
)
python = import('python')
conf = configuration_data()
conf.set('PYTHON', python.find_installation('python3').full_path())
conf.set('VERSION', meson.project_version())
conf.set('localedir', get_option('prefix') / get_option('localedir'))
conf.set('pkgdatadir', pkgdatadir)
configure_file(
input: 'alpaca.in',
output: 'alpaca',
configuration: conf,
install: true,
install_dir: get_option('bindir'),
install_mode: 'r-xr-xr-x'
)
alpaca_sources = [
'__init__.py',
'main.py',
'window.py',
'connection_handler.py',
'available_models.py',
]
install_data(alpaca_sources, install_dir: moduledir)

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# window.py
#
# Copyright 2024 Unknown
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
#
# SPDX-License-Identifier: GPL-3.0-or-later
import gi
gi.require_version("Soup", "3.0")
from gi.repository import Adw, Gtk, GLib
import json, requests, threading
from datetime import datetime
from .connection_handler import simple_get, simple_delete, stream_post, stream_post_fake
from .available_models import available_models
@Gtk.Template(resource_path='/com/jeffser/Alpaca/window.ui')
class AlpacaWindow(Adw.ApplicationWindow):
__gtype_name__ = 'AlpacaWindow'
#Variables
ollama_url = None
local_models = []
messages_history = []
#Elements
bot_message : Gtk.TextBuffer = None
overlay = Gtk.Template.Child()
chat_container = Gtk.Template.Child()
message_entry = Gtk.Template.Child()
send_button = Gtk.Template.Child()
model_drop_down = Gtk.Template.Child()
model_string_list = Gtk.Template.Child()
manage_models_button = Gtk.Template.Child()
manage_models_dialog = Gtk.Template.Child()
model_list_box = Gtk.Template.Child()
pull_model_dialog = Gtk.Template.Child()
pull_model_status_page = Gtk.Template.Child()
pull_model_progress_bar = Gtk.Template.Child()
def show_toast(self, msg:str):
toast = Adw.Toast(
title=msg,
timeout=2
)
self.overlay.add_toast(toast)
def show_message(self, msg:str, bot:bool):
message_text = Gtk.TextView(
editable=False,
focusable=False,
wrap_mode= Gtk.WrapMode.WORD,
margin_top=12,
margin_bottom=12,
margin_start=12,
margin_end=12,
)
message_buffer = message_text.get_buffer()
message_buffer.insert(message_buffer.get_end_iter(), msg)
message_box = Adw.Bin(
child=message_text,
css_classes=["card" if bot else None]
)
message_text.set_valign(Gtk.Align.CENTER)
self.chat_container.append(message_box)
if bot: self.bot_message = message_buffer
def update_list_local_models(self):
self.local_models = []
response = simple_get(self.ollama_url + "/api/tags")
if response['status'] == 'ok':
for model in json.loads(response['text'])['models']:
self.model_string_list.append(model["name"])
self.local_models.append(model["name"])
self.model_drop_down.set_selected(0)
return
#IF IT CONTINUES THEN THERE WAS EN ERROR
self.show_toast(response['text'])
self.show_connection_dialog()
def dialog_response(self, dialog, task):
self.ollama_url = dialog.get_extra_child().get_text()
if dialog.choose_finish(task) == "login":
response = simple_get(self.ollama_url)
if response['status'] == 'ok':
if "Ollama is running" in response['text']:
self.message_entry.grab_focus_without_selecting()
self.update_list_local_models()
return
else:
response = {"status": "error", "text": f"Unexpected response from {self.ollama_url} : {response['text']}"}
#IF IT CONTINUES THEN THERE WAS EN ERROR
self.show_toast(response['text'])
self.show_connection_dialog()
else:
self.destroy()
def show_connection_dialog(self):
dialog = Adw.AlertDialog(
heading="Login",
body="Please enter the Ollama instance URL",
close_response="cancel"
)
dialog.add_response("cancel", "Cancel")
dialog.add_response("login", "Login")
dialog.set_response_appearance("login", Adw.ResponseAppearance.SUGGESTED)
entry = Gtk.Entry(text="http://localhost:11434") #FOR TESTING PURPOSES
dialog.set_extra_child(entry)
dialog.choose(parent = self, cancellable = None, callback = self.dialog_response)
def update_bot_message(self, data):
if data['done']:
try:
api_datetime = data['created_at']
api_datetime = api_datetime[:-4] + api_datetime[-1]
formated_datetime = datetime.strptime(api_datetime, "%Y-%m-%dT%H:%M:%S.%fZ").strftime("%Y/%m/%d %H:%M")
text = f"\n\n<small>{data['model']}\t|\t{formated_datetime}</small>"
GLib.idle_add(self.bot_message.insert_markup, self.bot_message.get_end_iter(), text, len(text))
except Exception as e: print(e)
self.bot_message = None
else:
if self.bot_message is None:
GLib.idle_add(self.show_message, data['message']['content'], True)
self.messages_history.append({
"role": "assistant",
"content"