Forgot to remove this permission

com.jeffser.Alpaca.json

@@ -11,7 +11,8 @@
         "--device=all",
         "--socket=wayland",
         "--filesystem=/sys/module/amdgpu:ro",
-        "--env=LD_LIBRARY_PATH=/app/lib:/usr/lib/x86_64-linux-gnu/GL/default/lib:/usr/lib/x86_64-linux-gnu/openh264/extra:/usr/lib/x86_64-linux-gnu/openh264/extra:/usr/lib/sdk/llvm15/lib:/usr/lib/x86_64-linux-gnu/GL/default/lib:/usr/lib/ollama:/app/plugins/AMD/lib/ollama"
+        "--env=LD_LIBRARY_PATH=/app/lib:/usr/lib/x86_64-linux-gnu/GL/default/lib:/usr/lib/x86_64-linux-gnu/openh264/extra:/usr/lib/x86_64-linux-gnu/openh264/extra:/usr/lib/sdk/llvm15/lib:/usr/lib/x86_64-linux-gnu/GL/default/lib:/usr/lib/ollama:/app/plugins/AMD/lib/ollama",
+        "--env=GSK_RENDERER=ngl"
     ],
     "add-extensions": {
     	"com.jeffser.Alpaca.Plugins": {
@@ -134,16 +135,16 @@
             "sources": [
             	{
             		"type": "archive",
-            		"url": "https://github.com/ollama/ollama/releases/download/v0.3.11/ollama-linux-amd64.tgz",
-            		"sha256": "aa4d26889a6a413f676a7f80116983731f06287534bb72adec37dd39d168d40a",
+            		"url": "https://github.com/ollama/ollama/releases/download/v0.3.12/ollama-linux-amd64.tgz",
+            		"sha256": "f0efa42f7ad77cd156bd48c40cd22109473801e5113173b0ad04f094a4ef522b",
 		    	"only-arches": [
 		    		"x86_64"
 		    	]
             	},
             	{
             		"type": "archive",
-            		"url": "https://github.com/ollama/ollama/releases/download/v0.3.11/ollama-linux-arm64.tgz",
-            		"sha256": "61e3a21bec7f706b404424b80602240281d9b651ca4e00e8edee4527a533a15b",
+            		"url": "https://github.com/ollama/ollama/releases/download/v0.3.12/ollama-linux-arm64.tgz",
+            		"sha256": "da631cbe4dd2c168dae58d6868b1ff60e881e050f2d07578f2f736e689fec04c",
 		    	"only-arches": [
 		    		"aarch64"
 		    	]
@@ -166,6 +167,18 @@
             	}
             ]
         },
+        {
+            "name": "vte",
+            "buildsystem": "meson",
+            "config-opts": ["-Dvapi=false"],
+            "sources": [
+            	{
+		   "type": "archive",
+		   "url": "https://gitlab.gnome.org/GNOME/vte/-/archive/0.78.0/vte-0.78.0.tar.gz",
+		   "sha256": "82e19d11780fed4b66400f000829ce5ca113efbbfb7975815f26ed93e4c05f2d"
+            	}
+            ]
+        },
         {
             "name" : "alpaca",
             "builddir" : true,

data/com.jeffser.Alpaca.metainfo.xml.in

@@ -78,6 +78,23 @@
   <url type="contribute">https://github.com/Jeffser/Alpaca/discussions/154</url>
   <url type="vcs-browser">https://github.com/Jeffser/Alpaca</url>
   <releases>
+    <release version="2.5.0" date="2024-10-06">
+      <url type="details">https://github.com/Jeffser/Alpaca/releases/tag/2.5.0</url>
+      <description>
+        <p>New</p>
+        <ul>
+          <li>Run bash and python scripts straight from chat</li>
+          <li>Updated Ollama to 0.3.12</li>
+          <li>New models!</li>
+        </ul>
+        <p>Fixes</p>
+        <ul>
+          <li>Fixed and made faster the launch sequence</li>
+          <li>Better detection of code blocks in messages</li>
+          <li>Fixed app not loading in certain setups with Nvidia GPUs</li>
+        </ul>
+      </description>
+    </release>
     <release version="2.0.6" date="2024-09-29">
       <url type="details">https://github.com/Jeffser/Alpaca/releases/tag/2.0.6</url>
       <description>

meson.build

@@ -1,5 +1,5 @@
 project('Alpaca', 'c',
-          version: '2.0.6',
+          version: '2.5.0',
     meson_version: '>= 0.62.0',
   default_options: [ 'warning_level=2', 'werror=false', ],
 )

src/alpaca.gresource.xml

@@ -31,6 +31,7 @@
     <file alias="icons/scalable/status/update-symbolic.svg">icons/update-symbolic.svg</file>
     <file alias="icons/scalable/status/down-symbolic.svg">icons/down-symbolic.svg</file>
     <file alias="icons/scalable/status/chat-bubble-text-symbolic.svg">icons/chat-bubble-text-symbolic.svg</file>
+    <file alias="icons/scalable/status/execute-from-symbolic.svg">icons/execute-from-symbolic.svg</file>
     <file preprocess="xml-stripblanks">window.ui</file>
     <file preprocess="xml-stripblanks">gtk/help-overlay.ui</file>
   </gresource>

src/available_models_descriptions.py

@@ -1,4 +1,5 @@
 descriptions = {
+   'llama3.2': _("Meta's Llama 3.2 goes small with 1B and 3B models."),
    'llama3.1': _("Llama 3.1 is a new state-of-the-art model from Meta available in 8B, 70B and 405B parameter sizes."),
    'gemma2': _("Google Gemma 2 is a high-performing and efficient model available in three sizes: 2B, 9B, and 27B."),
    'qwen2.5': _("Qwen2.5 models are pretrained on Alibaba's latest large-scale dataset, encompassing up to 18 trillion tokens. The model supports up to 128K tokens and has multilingual support."),
@@ -21,87 +22,88 @@ descriptions = {
    'llama2': _("Llama 2 is a collection of foundation language models ranging from 7B to 70B parameters."),
    'codellama': _("A large language model that can use text prompts to generate and discuss code."),
    'nomic-embed-text': _("A high-performing open embedding model with a large token context window."),
+   'mxbai-embed-large': _("State-of-the-art large embedding model from mixedbread.ai"),
    '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."),
    'phi': _("Phi-2: a 2.7B language model by Microsoft Research that demonstrates outstanding reasoning and language understanding capabilities."),
-   '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."),
-   'mxbai-embed-large': _("State-of-the-art large embedding model from mixedbread.ai"),
    'starcoder2': _("StarCoder2 is the next generation of transparently trained open code LLMs that comes in three sizes: 3B, 7B and 15B parameters."),
+   'llama2-uncensored': _("Uncensored Llama 2 model by George Sung and Jarrad Hope."),
    'dolphin-mistral': _("The uncensored Dolphin model based on Mistral that excels at coding tasks. Updated to version 2.8."),
    'zephyr': _("Zephyr is a series of fine-tuned versions of the Mistral and Mixtral models that are trained to act as helpful assistants."),
+   'yi': _("Yi 1.5 is a high-performing, bilingual language model."),
    '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."),
    'orca-mini': _("A general-purpose model ranging from 3 billion parameters to 70 billion, suitable for entry-level hardware."),
-   'yi': _("Yi 1.5 is a high-performing, bilingual language model."),
    'llava-llama3': _("A LLaVA model fine-tuned from Llama 3 Instruct with better scores in several benchmarks."),
+   'qwen2.5-coder': _("The latest series of Code-Specific Qwen models, with significant improvements in code generation, code reasoning, and code fixing."),
    'mistral-openorca': _("Mistral OpenOrca is a 7 billion parameter model, fine-tuned on top of the Mistral 7B model using the OpenOrca dataset."),
    'starcoder': _("StarCoder is a code generation model trained on 80+ programming languages."),
    'tinyllama': _("The TinyLlama project is an open endeavor to train a compact 1.1B Llama model on 3 trillion tokens."),
-   'vicuna': _("General use chat model based on Llama and Llama 2 with 2K to 16K context sizes."),
    'codestral': _("Codestral is Mistral AI’s first-ever code model designed for code generation tasks."),
+   'vicuna': _("General use chat model based on Llama and Llama 2 with 2K to 16K context sizes."),
    'llama2-chinese': _("Llama 2 based model fine tuned to improve Chinese dialogue ability."),
+   'snowflake-arctic-embed': _("A suite of text embedding models by Snowflake, optimized for performance."),
    'wizard-vicuna-uncensored': _("Wizard Vicuna Uncensored is a 7B, 13B, and 30B parameter model based on Llama 2 uncensored by Eric Hartford."),
+   'granite-code': _("A family of open foundation models by IBM for Code Intelligence"),
    'codegeex4': _("A versatile model for AI software development scenarios, including code completion."),
    'nous-hermes2': _("The powerful family of models by Nous Research that excels at scientific discussion and coding tasks."),
-   'granite-code': _("A family of open foundation models by IBM for Code Intelligence"),
+   'all-minilm': _("Embedding models on very large sentence level datasets."),
    '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."),
    'aya': _("Aya 23, released by Cohere, is a new family of state-of-the-art, multilingual models that support 23 languages."),
-   'wizardlm2': _("State of the art large language model from Microsoft AI with improved performance on complex chat, multilingual, reasoning and agent use cases."),
    'codeqwen': _("CodeQwen1.5 is a large language model pretrained on a large amount of code data."),
+   'wizardlm2': _("State of the art large language model from Microsoft AI with improved performance on complex chat, multilingual, reasoning and agent use cases."),
    'tinydolphin': _("An experimental 1.1B parameter model trained on the new Dolphin 2.8 dataset by Eric Hartford and based on TinyLlama."),
-   'all-minilm': _("Embedding models on very large sentence level datasets."),
    '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."),
    'openhermes': _("OpenHermes 2.5 is a 7B model fine-tuned by Teknium on Mistral with fully open datasets."),
+   'qwen2-math': _("Qwen2 Math is a series of specialized math language models built upon the Qwen2 LLMs, which significantly outperforms the mathematical capabilities of open-source models and even closed-source models (e.g., GPT4o)."),
    'bakllava': _("BakLLaVA is a multimodal model consisting of the Mistral 7B base model augmented with the LLaVA architecture."),
    '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."),
-   'qwen2-math': _("Qwen2 Math is a series of specialized math language models built upon the Qwen2 LLMs, which significantly outperforms the mathematical capabilities of open-source models and even closed-source models (e.g., GPT4o)."),
-   'wizard-math': _("Model focused on math and logic problems"),
    'llama3-gradient': _("This model extends LLama-3 8B's context length from 8k to over 1m tokens."),
-   'neural-chat': _("A fine-tuned model based on Mistral with good coverage of domain and language."),
    'deepseek-llm': _("An advanced language model crafted with 2 trillion bilingual tokens."),
+   'wizard-math': _("Model focused on math and logic problems"),
+   'glm4': _("A strong multi-lingual general language model with competitive performance to Llama 3."),
+   'neural-chat': _("A fine-tuned model based on Mistral with good coverage of domain and language."),
+   'reflection': _("A high-performing model trained with a new technique called Reflection-tuning that teaches a LLM to detect mistakes in its reasoning and correct course."),
+   'llama3-chatqa': _("A model from NVIDIA based on Llama 3 that excels at conversational question answering (QA) and retrieval-augmented generation (RAG)."),
+   'mistral-large': _("Mistral Large 2 is Mistral's new flagship model that is significantly more capable in code generation, mathematics, and reasoning with 128k context window and support for dozens of languages."),
+   'moondream': _("moondream2 is a small vision language model designed to run efficiently on edge devices."),
+   'xwinlm': _("Conversational model based on Llama 2 that performs competitively on various benchmarks."),
    'phind-codellama': _("Code generation model based on Code Llama."),
    'nous-hermes': _("General use models based on Llama and Llama 2 from Nous Research."),
-   'xwinlm': _("Conversational model based on Llama 2 that performs competitively on various benchmarks."),
    'sqlcoder': _("SQLCoder is a code completion model fined-tuned on StarCoder for SQL generation tasks"),
    'dolphincoder': _("A 7B and 15B uncensored variant of the Dolphin model family that excels at coding, based on StarCoder2."),
-   'llama3-chatqa': _("A model from NVIDIA based on Llama 3 that excels at conversational question answering (QA) and retrieval-augmented generation (RAG)."),
    'yarn-llama2': _("An extension of Llama 2 that supports a context of up to 128k tokens."),
-   'mistral-large': _("Mistral Large 2 is Mistral's new flagship model that is significantly more capable in code generation, mathematics, and reasoning with 128k context window and support for dozens of languages."),
-   'wizardlm': _("General use model based on Llama 2."),
    'smollm': _("🪐 A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset."),
+   'wizardlm': _("General use model based on Llama 2."),
+   'deepseek-v2': _("A strong, economical, and efficient Mixture-of-Experts language model."),
    'starling-lm': _("Starling is a large language model trained by reinforcement learning from AI feedback focused on improving chatbot helpfulness."),
-   'reflection': _("A high-performing model trained with a new technique called Reflection-tuning that teaches a LLM to detect mistakes in its reasoning and correct course."),
-   'moondream': _("moondream2 is a small vision language model designed to run efficiently on edge devices."),
-   'snowflake-arctic-embed': _("A suite of text embedding models by Snowflake, optimized for performance."),
    'samantha-mistral': _("A companion assistant trained in philosophy, psychology, and personal relationships. Based on Mistral."),
    'solar': _("A compact, yet powerful 10.7B large language model designed for single-turn conversation."),
    '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."),
-   'deepseek-v2': _("A strong, economical, and efficient Mixture-of-Experts language model."),
    'stable-beluga': _("Llama 2 based model fine tuned on an Orca-style dataset. Originally called Free Willy."),
-   'glm4': _("A strong multi-lingual general language model with competitive performance to Llama 3."),
    'dolphin-phi': _("2.7B uncensored Dolphin model by Eric Hartford, based on the Phi language model by Microsoft Research."),
    'wizardlm-uncensored': _("Uncensored version of Wizard LM model"),
-   'llava-phi3': _("A new small LLaVA model fine-tuned from Phi 3 Mini."),
    'hermes3': _("Hermes 3 is the latest version of the flagship Hermes series of LLMs by Nous Research"),
+   'yi-coder': _("Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters."),
+   'llava-phi3': _("A new small LLaVA model fine-tuned from Phi 3 Mini."),
+   'internlm2': _("InternLM2.5 is a 7B parameter model tailored for practical scenarios with outstanding reasoning capability."),
    'yarn-mistral': _("An extension of Mistral to support context windows of 64K or 128K."),
    'llama-pro': _("An expansion of Llama 2 that specializes in integrating both general language understanding and domain-specific knowledge, particularly in programming and mathematics."),
    'medllama2': _("Fine-tuned Llama 2 model to answer medical questions based on an open source medical dataset."),
-   'yi-coder': _("Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters."),
-   'internlm2': _("InternLM2.5 is a 7B parameter model tailored for practical scenarios with outstanding reasoning capability."),
    'meditron': _("Open-source medical large language model adapted from Llama 2 to the medical domain."),
    'nexusraven': _("Nexus Raven is a 13B instruction tuned model for function calling tasks."),
    'nous-hermes2-mixtral': _("The Nous Hermes 2 model from Nous Research, now trained over Mixtral."),
    'codeup': _("Great code generation model based on Llama2."),
-   'everythinglm': _("Uncensored Llama2 based model with support for a 16K context window."),
    'llama3-groq-tool-use': _("A series of models from Groq that represent a significant advancement in open-source AI capabilities for tool use/function calling."),
+   'everythinglm': _("Uncensored Llama2 based model with support for a 16K context window."),
    '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."),
+   'wizard-vicuna': _("Wizard Vicuna is a 13B parameter model based on Llama 2 trained by MelodysDreamj."),
    'mistrallite': _("MistralLite is a fine-tuned model based on Mistral with enhanced capabilities of processing long contexts."),
    'falcon2': _("Falcon2 is an 11B parameters causal decoder-only model built by TII and trained over 5T tokens."),
-   'wizard-vicuna': _("Wizard Vicuna is a 13B parameter model based on Llama 2 trained by MelodysDreamj."),
    'duckdb-nsql': _("7B parameter text-to-SQL model made by MotherDuck and Numbers Station."),
-   'qwen2.5-coder': _("The latest series of Code-Specific Qwen models, with significant improvements in code generation, code reasoning, and code fixing."),
+   'minicpm-v': _("A series of multimodal LLMs (MLLMs) designed for vision-language understanding."),
    'megadolphin': _("MegaDolphin-2.2-120b is a transformation of Dolphin-2.2-70b created by interleaving the model with itself."),
    'notux': _("A top-performing mixture of experts model, fine-tuned with high-quality data."),
    'goliath': _("A language model created by combining two fine-tuned Llama 2 70B models into one."),
@@ -110,14 +112,13 @@ descriptions = {
    'bge-m3': _("BGE-M3 is a new model from BAAI distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity."),
    'mathstral': _("MathΣtral: a 7B model designed for math reasoning and scientific discovery by Mistral AI."),
    'dbrx': _("DBRX is an open, general-purpose LLM created by Databricks."),
-   'minicpm-v': _("A series of multimodal LLMs (MLLMs) designed for vision-language understanding."),
+   'solar-pro': _("Solar Pro Preview: an advanced large language model (LLM) with 22 billion parameters designed to fit into a single GPU"),
    'nuextract': _("A 3.8B model fine-tuned on a private high-quality synthetic dataset for information extraction, based on Phi-3."),
    'alfred': _("A robust conversational model designed to be used for both chat and instruct use cases."),
    'firefunction-v2': _("An open weights function calling model based on Llama 3, competitive with GPT-4o function calling capabilities."),
-   'solar-pro': _("Solar Pro Preview: an advanced large language model (LLM) with 22 billion parameters designed to fit into a single GPU"),
-   'bge-large': _("Embedding model from BAAI mapping texts to vectors."),
    'reader-lm': _("A series of models that convert HTML content to Markdown content, which is useful for content conversion tasks."),
+   'bge-large': _("Embedding model from BAAI mapping texts to vectors."),
    'deepseek-v2.5': _("An upgraded version of DeekSeek-V2 that integrates the general and coding abilities of both DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct."),
-   'paraphrase-multilingual': _("Sentence-transformers model that can be used for tasks like clustering or semantic search."),
    'bespoke-minicheck': _("A state-of-the-art fact-checking model developed by Bespoke Labs."),
+   'paraphrase-multilingual': _("Sentence-transformers model that can be used for tasks like clustering or semantic search."),
 }

src/custom_widgets/chat_widget.py

@@ -6,7 +6,7 @@ Handles the chat widget (testing)
 import gi
 gi.require_version('Gtk', '4.0')
 gi.require_version('GtkSource', '5')
-from gi.repository import Gtk, Gio, Adw, Gdk
+from gi.repository import Gtk, Gio, Adw, Gdk, GLib
 import logging, os, datetime, shutil, random, tempfile, tarfile, json
 from ..internal import data_dir
 from .message_widget import message
@@ -154,8 +154,8 @@ class chat(Gtk.ScrolledWindow):
                         for file_name, file_type in message_data['files'].items():
                             files[os.path.join(data_dir, "chats", self.get_name(), message_id, file_name)] = file_type
                         message_element.add_attachments(files)
-                    message_element.set_text(message_data['content'])
-                    message_element.add_footer(datetime.datetime.strptime(message_data['date'] + (":00" if message_data['date'].count(":") == 1 else ""), '%Y/%m/%d %H:%M:%S'))
+                    GLib.idle_add(message_element.set_text, message_data['content'])
+                    GLib.idle_add(message_element.add_footer, datetime.datetime.strptime(message_data['date'] + (":00" if message_data['date'].count(":") == 1 else ""), '%Y/%m/%d %H:%M:%S'))
         else:
             self.show_welcome_screen(len(window.model_manager.get_model_list()) > 0)
 

src/custom_widgets/message_widget.py

@@ -10,6 +10,7 @@ from gi.repository import Gtk, GObject, Gio, Adw, GtkSource, GLib, Gdk
 import logging, os, datetime, re, shutil, threading, sys
 from ..internal import config_dir, data_dir, cache_dir, source_dir
 from .table_widget import TableWidget
+from .. import dialogs
 
 logger = logging.getLogger(__name__)
 
@@ -103,10 +104,14 @@ class code_block(Gtk.Box):
         self.source_view.update_property([4], [_("{}Code Block").format('{} '.format(self.language.get_name()) if self.language else "")])
 
         title_box = Gtk.Box(margin_start=12, margin_top=3, margin_bottom=3, margin_end=3)
-        title_box.append(Gtk.Label(label=self.language.get_name() if self.language else _("Code Block"), hexpand=True, xalign=0))
+        title_box.append(Gtk.Label(label=self.language.get_name() if self.language else (language_name.title() if language_name else _("Code Block")), hexpand=True, xalign=0))
         copy_button = Gtk.Button(icon_name="edit-copy-symbolic", css_classes=["flat", "circular"], tooltip_text=_("Copy Message"))
         copy_button.connect("clicked", lambda *_: self.on_copy())
         title_box.append(copy_button)
+        if language_name and language_name.lower() in ['bash', 'python3']:
+            run_button = Gtk.Button(icon_name="execute-from-symbolic", css_classes=["flat", "circular"], tooltip_text=_("Run Script"))
+            run_button.connect("clicked", lambda *_: self.run_script(language_name))
+            title_box.append(run_button)
         self.append(title_box)
         self.append(Gtk.Separator())
         self.append(self.source_view)
@@ -121,6 +126,12 @@ class code_block(Gtk.Box):
         clipboard.set(text)
         window.show_toast(_("Code copied to the clipboard"), window.main_overlay)
 
+    def run_script(self, language_name):
+        logger.debug("Running script")
+        start = self.buffer.get_start_iter()
+        end = self.buffer.get_end_iter()
+        dialogs.run_script(window, self.buffer.get_text(start, end, False), language_name)
+
 class attachment(Gtk.Button):
     __gtype_name__ = 'AlpacaAttachment'
 
@@ -473,8 +484,7 @@ class message(Gtk.Overlay):
         self.content_children = []
         if text:
             self.content_children = []
-            code_block_pattern = re.compile(r'```(\w+)\n(.*?)\n```', re.DOTALL)
-            no_lang_code_block_pattern = re.compile(r'`\n(.*?)\n`', re.DOTALL)
+            code_block_pattern = re.compile(r'[```|`](\w*)\n(.*?)\n\s*[```|`]', re.DOTALL)
             table_pattern = re.compile(r'((\r?\n){2}|^)([^\r\n]*\|[^\r\n]*(\r?\n)?)+(?=(\r?\n){2}|$)', re.MULTILINE)
             bold_pattern = re.compile(r'\*\*(.*?)\*\*') #"**text**"
             code_pattern = re.compile(r'`([^`\n]*?)`') #"`text`"
@@ -493,15 +503,6 @@ class message(Gtk.Overlay):
                 code_text = match.group(2)
                 parts.append({"type": "code", "text": code_text, "language": 'python3' if language == 'python' else language})
                 pos = end
-            # Code blocks (No language)
-            for match in no_lang_code_block_pattern.finditer(self.text):
-                start, end = match.span()
-                if pos < start:
-                    normal_text = self.text[pos:start]
-                    parts.append({"type": "normal", "text": normal_text.strip()})
-                code_text = match.group(1)
-                parts.append({"type": "code", "text": code_text, "language": None})
-                pos = end
             # Tables
             for match in table_pattern.finditer(self.text):
                 start, end = match.span()

src/custom_widgets/terminal_widget.py

@@ -0,0 +1,49 @@
+#chat_widget.py
+"""
+Handles the terminal widget
+"""
+
+import gi
+gi.require_version('Gtk', '4.0')
+gi.require_version('Vte', '3.91')
+from gi.repository import Gtk, Vte, GLib, Pango, GLib, Gdk
+
+class terminal(Vte.Terminal):
+    __gtype_name__ = 'AlpacaTerminal'
+
+    def __init__(self, script:list):
+        super().__init__(css_classes=["terminal"])
+        self.set_font(Pango.FontDescription.from_string("Monospace 12"))
+        self.set_clear_background(False)
+        pty = Vte.Pty.new_sync(Vte.PtyFlags.DEFAULT, None)
+
+        self.set_pty(pty)
+
+        env = {
+            'TERM': "xterm-256color",
+            'SUDO_ASKPASS': "sh -c 'pkexec echo'"
+        }
+
+        pty.spawn_async(
+            GLib.get_current_dir(),
+            script,
+            [],
+            GLib.SpawnFlags.DEFAULT,
+            None,
+            None,
+            -1,
+            None,
+            None
+        )
+
+        key_controller = Gtk.EventControllerKey()
+        key_controller.connect("key-pressed", self.on_key_press)
+        self.add_controller(key_controller)
+
+    def on_key_press(self, controller, keyval, keycode, state):
+        ctrl = state & Gdk.ModifierType.CONTROL_MASK
+        shift = state & Gdk.ModifierType.SHIFT_MASK
+        if ctrl and keyval == Gdk.KEY_c:
+            self.copy_clipboard()
+            return True
+        return False

src/dialogs.py

@@ -3,11 +3,11 @@
 Handles UI dialogs
 """
 import os
-import logging, requests, threading, shutil
+import logging, requests, threading, shutil, subprocess, re
 from pytube import YouTube
 from html2text import html2text
 from gi.repository import Adw, Gtk
-from .internal import cache_dir
+from .internal import cache_dir, data_dir
 
 logger = logging.getLogger(__name__)
 # CLEAR CHAT | WORKS
@@ -416,3 +416,59 @@ def attach_website(self, url):
         cancellable = None,
         callback = lambda dialog, task, url=url: attach_website_response(self, dialog, task, url)
     )
+
+# Run Script
+
+def run_script_response(self, dialog, task, script, language_name):
+    if dialog.choose_finish(task) == "accept":
+        logger.info('Running: \n{}'.format(script))
+        if language_name == 'python3':
+            if not os.path.isdir(os.path.join(data_dir, 'pyenv')):
+                os.mkdir(os.path.join(data_dir, 'pyenv'))
+            with open(os.path.join(data_dir, 'pyenv', 'main.py'), 'w') as f:
+                f.write(script)
+            script = [
+                'echo "🐍 {}\n"'.format(_('Setting up Python environment...')),
+                'python3 -m venv "{}"'.format(os.path.join(data_dir, 'pyenv')),
+                '{} {}'.format(os.path.join(data_dir, 'pyenv', 'bin', 'python3').replace(' ', '\\ '), os.path.join(data_dir, 'pyenv', 'main.py').replace(' ', '\\ '))
+            ]
+            if os.path.isfile(os.path.join(data_dir, 'pyenv', 'requirements.txt')):
+                script.insert(1, '{} install -r {} | grep -v "already satisfied"; clear'.format(os.path.join(data_dir, 'pyenv', 'bin', 'pip3'), os.path.join(data_dir, 'pyenv', 'requirements.txt')))
+            else:
+                with open(os.path.join(data_dir, 'pyenv', 'requirements.txt'), 'w') as f:
+                    f.write('')
+            script = ';\n'.join(script)
+
+        script += '; echo "\n🦙 {}"'.format(_('Script exited'))
+        if language_name == 'bash':
+            script = re.sub(r'(?m)^\s*sudo', 'pkexec', script)
+        if shutil.which('flatpak-spawn') and language_name == 'bash':
+            sandbox = True
+            try:
+                process = subprocess.run(['flatpak-spawn', '--host', 'bash', '-c', 'echo "test"'], check=True)
+                sandbox = False
+            except Exception as e:
+                pass
+            if sandbox:
+                script = 'echo "🦙 {}\n";'.format(_('The script is contained inside Flatpak')) + script
+                self.run_terminal(['bash', '-c', script])
+            else:
+                self.run_terminal(['flatpak-spawn', '--host', 'bash', '-c', script])
+        else:
+            self.run_terminal(['bash', '-c', script])
+
+def run_script(self, script:str, language_name:str):
+    dialog = Adw.AlertDialog(
+        heading=_("Run Script"),
+        body=_("Make sure you understand what this script does before running it, Alpaca is not responsible for any damages to your device or data"),
+        close_response="cancel"
+    )
+    dialog.add_response("cancel", _("Cancel"))
+    dialog.add_response("accept", _("Accept"))
+    dialog.set_response_appearance("accept", Adw.ResponseAppearance.SUGGESTED)
+    dialog.set_default_response("accept")
+    dialog.choose(
+        parent = self,
+        cancellable = None,
+        callback = lambda dialog, task, script=script, language_name=language_name: run_script_response(self, dialog, task, script, language_name)
+    )

src/icons/execute-from-symbolic.svg

@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" height="16px" viewBox="0 0 16 16" width="16px"><path d="m 4.992188 2.996094 v 10 h 1 c 0.175781 0 0.347656 -0.039063 0.5 -0.125 l 7 -4 c 0.308593 -0.171875 0.46875 -0.523438 0.46875 -0.875 c 0 -0.351563 -0.160157 -0.703125 -0.46875 -0.875 l -7 -4 c -0.152344 -0.085938 -0.324219 -0.125 -0.5 -0.125 z m 0 0" fill="#222222"/></svg>

src/meson.build

@@ -50,7 +50,8 @@ custom_widgets = [
   'custom_widgets/table_widget.py',
   'custom_widgets/message_widget.py',
   'custom_widgets/chat_widget.py',
-  'custom_widgets/model_widget.py'
+  'custom_widgets/model_widget.py',
+  'custom_widgets/terminal_widget.py'
 ]
 
 install_data(alpaca_sources, install_dir: moduledir)

src/style.css

@@ -36,4 +36,7 @@ stacksidebar {
 }
 .code_block {
   font-family: monospace;
-}
+}
+.terminal {
+  padding: 10px;
+}

src/window.py

@@ -32,7 +32,7 @@ gi.require_version('GdkPixbuf', '2.0')
 from gi.repository import Adw, Gtk, Gdk, GLib, GtkSource, Gio, GdkPixbuf
 
 from . import dialogs, connection_handler
-from .custom_widgets import message_widget, chat_widget, model_widget
+from .custom_widgets import message_widget, chat_widget, model_widget, terminal_widget
 from .internal import config_dir, data_dir, cache_dir, source_dir
 
 logger = logging.getLogger(__name__)
@@ -118,6 +118,9 @@ class AlpacaWindow(Adw.ApplicationWindow):
 
     style_manager = Adw.StyleManager()
 
+    terminal_scroller = Gtk.Template.Child()
+    terminal_dialog = Gtk.Template.Child()
+
     @Gtk.Template.Callback()
     def stop_message(self, button=None):
         self.chat_list_box.get_current_chat().stop_message()
@@ -351,14 +354,15 @@ class AlpacaWindow(Adw.ApplicationWindow):
             current_chat = self.chat_list_box.get_current_chat()
         if current_chat:
             for key, message in current_chat.messages.items():
-                message.set_visible(re.search(search_term, message.text, re.IGNORECASE))
-                for block in message.content_children:
-                    if isinstance(block, message_widget.text_block):
-                        if search_term:
-                            highlighted_text = re.sub(f"({re.escape(search_term)})", r"<span background='yellow' bgalpha='30%'>\1</span>", block.get_text(),flags=re.IGNORECASE)
-                            block.set_markup(highlighted_text)
-                        else:
-                            block.set_markup(block.get_text())
+                if message and message.text:
+                    message.set_visible(re.search(search_term, message.text, re.IGNORECASE))
+                    for block in message.content_children:
+                        if isinstance(block, message_widget.text_block):
+                            if search_term:
+                                highlighted_text = re.sub(f"({re.escape(search_term)})", r"<span background='yellow' bgalpha='30%'>\1</span>", block.get_text(),flags=re.IGNORECASE)
+                                block.set_markup(highlighted_text)
+                            else:
+                                block.set_markup(block.get_text())
 
     @Gtk.Template.Callback()
     def on_clipboard_paste(self, textview):
@@ -367,6 +371,10 @@ class AlpacaWindow(Adw.ApplicationWindow):
         clipboard.read_text_async(None, self.cb_text_received)
         clipboard.read_texture_async(None, self.cb_image_received)
 
+    def run_terminal(self, script:list):
+        self.terminal_scroller.set_child(terminal_widget.terminal(script))
+        self.terminal_dialog.present(self)
+
     def convert_model_name(self, name:str, mode:int) -> str: # mode=0 name:tag -> Name (tag)   |   mode=1 Name (tag) -> name:tag
         try:
             if mode == 0:
@@ -813,9 +821,20 @@ Generate a title following these rules:
         self.banner.set_revealed(monitor.get_power_saver_enabled() and self.powersaver_warning_switch.get_active())
 
     def prepare_alpaca(self, local_port:int, remote_url:str, remote:bool, tweaks:dict, overrides:dict, bearer_token:str, idle_timer_delay:int, save:bool):
+        #Model Manager
+        self.model_manager = model_widget.model_manager_container()
+        self.model_scroller.set_child(self.model_manager)
+
+        #Chat History
+        self.load_history()
+
         #Instance
         self.ollama_instance = connection_handler.instance(local_port, remote_url, remote, tweaks, overrides, bearer_token, idle_timer_delay)
 
+        #Model Manager P.2
+        self.model_manager.update_available_list()
+        self.model_manager.update_local_list()
+
         #User Preferences
         for element in list(list(list(list(self.tweaks_group)[0])[1])[0]):
             if element.get_name() in self.ollama_instance.tweaks:
@@ -832,23 +851,13 @@ Generate a title following these rules:
         self.remote_connection_switch.set_active(self.ollama_instance.remote)
         self.instance_idle_timer.set_value(self.ollama_instance.idle_timer_delay)
 
-        #Model Manager
-        self.model_manager = model_widget.model_manager_container()
-        self.model_scroller.set_child(self.model_manager)
-
-        #Chat History
-        self.load_history()
-
-        #Model Manager P.2
-        self.model_manager.update_available_list()
-        self.model_manager.update_local_list()
-        self.get_application().lookup_action("manage_models").set_enabled(True)
-
         #Save preferences
         if save:
             self.save_server_config()
-
         self.send_button.set_sensitive(True)
+        self.attachment_button.set_sensitive(True)
+        self.get_application().lookup_action('manage_models').set_enabled(True)
+        self.get_application().lookup_action('preferences').set_enabled(True)
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
@@ -895,7 +904,9 @@ Generate a title following these rules:
 
         for action_name, data in universal_actions.items():
             self.get_application().create_action(action_name, data[0], data[1] if len(data) > 1 else None)
-        self.get_application().lookup_action("manage_models").set_enabled(False)
+
+        self.get_application().lookup_action('manage_models').set_enabled(False)
+        self.get_application().lookup_action('preferences').set_enabled(False)
 
         self.file_preview_remove_button.connect('clicked', lambda button : dialogs.remove_attached_file(self, button.get_name()))
         self.attachment_button.connect("clicked", lambda button, file_filter=self.file_filter_attachments: dialogs.attach_file(self, file_filter))

src/window.ui

@@ -14,6 +14,7 @@
       <object class="AdwBreakpoint">
         <condition>max-width: 690sp</condition>
         <setter object="split_view_overlay" property="collapsed">true</setter>
+        <setter object="terminal_dialog" property="width-request">400</setter>
       </object>
     </child>
     <property name="content">
@@ -180,6 +181,7 @@
                               <object class="GtkButton" id="attachment_button">
                                 <property name="vexpand">false</property>
                                 <property name="valign">3</property>
+                                <property name="sensitive">false</property>
                                 <property name="tooltip-text" translatable="yes">Attach File</property>
                                 <style>
                                   <class name="circular"/>
@@ -474,6 +476,29 @@
       </child>
     </object>
 
+    <object class="AdwDialog" id="terminal_dialog">
+      <accessibility>
+        <property name="label" translatable="yes">Manage models dialog</property>
+      </accessibility>
+      <property name="title" translatable="yes">Terminal</property>
+      <property name="can-close">true</property>
+      <property name="width-request">600</property>
+      <property name="height-request">600</property>
+      <child>
+        <object class="AdwToolbarView">
+          <style>
+            <class name="osd"/>
+          </style>
+          <child type="top">
+            <object class="AdwHeaderBar"/>
+          </child>
+          <property name="content">
+            <object class="GtkScrolledWindow" id="terminal_scroller"/>
+          </property>
+        </object>
+      </child>
+    </object>
+
     <object class="AdwDialog" id="manage_models_dialog">
       <accessibility>
         <property name="label" translatable="yes">Manage models dialog</property>