- 
-
-
-## Screenshots
-
-
-
-
-## Requirements
- - python-conda or miniconda;
- - python 3.10 or above;
- - linux (windows not tested but probably working);
-
- Python requirements are listed in `requirements.txt`.
-
-## Installation
-1. Clone repository:
- ```
- git clone https://github.com/swrneko/faster-whisper-n-ionet-llm.git
- cd faster-whisper-n-ionet-llm
- ```
- also (if not insatlled)
- - Insatll conda:
- ```
- wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh && bash miniconda.sh
- ```
-
-2. Create virtual env:
- ```
- conda create -n faster-whisper-n-ionet-llm python=3.10
- conda activate faster-whisper-n-ionet-llm
- conda install nvidia::cudnn cuda-version=12
- ```
-
-3. Install requirements:
- ```
- pip install -r requirements.txt
- ```
-
-4. Get api key from [io.net](https://ai.io.net/ai/api-keys) and insert into `.env` file (need to create it in root of repository directory).
-It should looks like this:
- ```
- API_KEY='your_api_key_without_qoutes'
- ```
-
-5. Done! Now you can just run it like that:
- ```shell
- python app.py
- ```
+# FWAL WebUI (Faster Whisper And LLM WebUI) by swrneko
+
+
- 
-
-
-
- 
- 
-
-
-
-
+
+
+
+## Screenshots
+
+
+
+
+## Requirements
+ - python-conda or miniconda;
+ - python 3.10 or above;
+ - linux (windows not tested but probably working);
+
+ Python requirements are listed in `requirements.txt`.
+
+## Installation
+1. Clone repository:
+ ```
+ git clone https://github.com/swrneko/faster-whisper-n-ionet-llm.git
+ cd faster-whisper-n-ionet-llm
+ ```
+ also (if not insatlled)
+ - Insatll conda:
+ ```
+ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh && bash miniconda.sh
+ ```
+
+2. Create virtual env:
+ ```
+ conda create -n faster-whisper-n-ionet-llm python=3.10
+ conda activate faster-whisper-n-ionet-llm
+ conda install nvidia::cudnn cuda-version=12
+ ```
+
+3. Install requirements:
+ ```
+ pip install -r requirements.txt
+ ```
+
+4. Get api key from [io.net](https://ai.io.net/ai/api-keys) and insert into `.env` file (need to create it in root of repository directory).
+It should looks like this:
+ ```
+ API_KEY='your_api_key_without_qoutes'
+ ```
+
+5. Done! Now you can just run it like that:
+ ```shell
+ python app.py
+ ```
diff --git a/app.py b/app.py
index e525b9a..97fd86d 100644
--- a/app.py
+++ b/app.py
@@ -1,123 +1,145 @@
-import gradio as gr
-
-# Загрузка параметров конфигурации
-from config import *
-
-# Подгрузка сервисов
-from services.llm import Llm
-
-# Загрузка доп. модулей
-from handlers.gradioHandler import GradioHandlers
-from handlers.fileHandlers import FileHandlers
-from services.fasterWhisper import FasterWhisper
-from handlers.convertMdToPdf import ConvertMdToPdf
-from handlers.glueAudio import GlueAudio
-
-gh = GradioHandlers(gr, Llm, ConvertMdToPdf, FileHandlers, FasterWhisper, GlueAudio)
-
-def main():
- with gr.Blocks() as demo:
- gr.HTML('''
-
+
+
+
+
+
+
+
+
+
+
-
- ''')
-
- with gr.Row():
- # Вкладка с основным взаимодействием
- with gr.Tab('Actions'):
- isPipelineEnabledCheckbox = gr.Checkbox(label='is pipeline enabled', value=True, interactive=True)
-
- with gr.Row():
- with gr.Accordion(label='Recognization and integration'):
- with gr.Column():
- audioFiles = gr.Files(label='Load audio for transcribe', type="filepath", file_types=['audio'])
- images = gr.Files(label='Upload images', file_types=['image'])
- recognizeBtn = gr.Button('recognize and integrate', variant='primary')
-
- with gr.Accordion(label='Recognized text'):
- recognizedText = gr.TextArea(label='')
-
- with gr.Accordion(label='LLM'):
- with gr.Column():
- refineTextBtn = gr.Button('refine text', variant='secondary', interactive=False)
-
- with gr.Accordion(label='Refined text raw'):
- refinedText = gr.Textbox(label='', show_copy_button=True)
-
- with gr.Accordion(label='Refined text md formated'):
- refinedTextMD = gr.Markdown(label='')
-
- # Вкладка с настройками
- with gr.Tab('Settings'):
- with gr.Column():
- # Первое поле на всю ширину в акордионе настроек
- with gr.Accordion('File settings'):
- saveFileCheckbox = gr.Checkbox(label='save file', value=True, interactive=True)
- filename = gr.Textbox(label='Output filename', value='output.txt', interactive=True)
- filenamePdf = gr.Textbox(label='Output filename for pdf', value='output.pdf', interactive=True)
-
- # Акордион настроек faster whisper
- with gr.Accordion(label='Faster whisper settings'):
- with gr.Row():
- # Левая колонка в акордионе
- with gr.Column():
- device = gr.Dropdown(label='Device', choices=DEVICES, value=DEVICES[1], interactive=True)
- compute_type = gr.Dropdown(label='compute_type', choices=COMPUTE_TYPE, value=COMPUTE_TYPE[0], interactive=True)
- fastWhisperModel = gr.Dropdown(label='Model', choices=FAST_WHISPER_MODELS, value=FAST_WHISPER_MODELS[11], interactive=True)
-
- beamSize = gr.Number(label='beam_size', value=8, interactive=True)
- noSpeechThreshold = gr.Number(label='no_speech_threshold', value=0.5, interactive=True)
- vadFilter = gr.Checkbox(label='vad_filter', value=True, interactive=True)
- wordTimestamps = gr.Checkbox(label='word_timestamps', value=True, interactive=True)
- conditionOnPreviousText = gr.Checkbox(label='condition_on_previous_text', value=False, interactive=True)
-
- # Правая колонка в акордионе
- with gr.Column():
- with gr.Accordion(label='Vad parameters'):
- minSilenceDurationMs = gr.Number(label='min_silence_duration_ms', value=300, interactive=True)
- speechPadMs = gr.Number(label='speech_pad_ms', value=200, interactive=True)
-
- with gr.Accordion(label='Temperature'):
- temp0 = gr.Number(label='temp_0', value=0.0, interactive=True)
- temp1 = gr.Number(label='temp_1', value=0.2, interactive=True)
- temp2 = gr.Number(label='temp_2', value=0.4, interactive=True)
-
- # Нижний акордион настроек для api ключа llm
- with gr.Accordion(label='ai.io.net api settings'):
- apiKey = gr.Textbox(label='API key', value=DEFAULT_API_KEY, interactive=True)
-
- with gr.Accordion(label='System prompt'):
- systemPrompt = gr.Textbox(label='', value=DEFAULT_SYSTEM_PROMPT, interactive=True)
-
- with gr.Row():
- llmModel = gr.Dropdown(label='models', choices=LLM_MODELS, value=LLM_MODELS[1], interactive=True)
- llmTemperature = gr.Number(label='Temperature', value=0.8, interactive=True )
-
- isPipelineEnabledCheckbox.change(gh.updateButton, inputs=[isPipelineEnabledCheckbox], outputs=refineTextBtn)
- saveFileCheckbox.change(gh.updateTextbox, inputs=saveFileCheckbox, outputs=filename)
- saveFileCheckbox.change(gh.updateTextbox, inputs=saveFileCheckbox, outputs=filenamePdf)
-
- recognizeBtn.click(gh.handleRecognizeBtn, outputs=[recognizedText], inputs=[audioFiles, fastWhisperModel, device, compute_type, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText, gr.State(GLUED_AUDIO_FILENAME), gr.State(OUTPUT_PATH)])
-
- # Если пайплайн включен то тогда делаем автоматически
- # автоматический пайплайн
- recognizedText.change(
- gh.generateByCondition,
- inputs=[apiKey, llmModel, systemPrompt, recognizedText, llmTemperature, isPipelineEnabledCheckbox, gr.State("change"), saveFileCheckbox, filename, filenamePdf, gr.State(OUTPUT_PATH)],
- outputs=[refinedText, refinedTextMD]
- )
-
- # ручной запуск по кнопке
- refineTextBtn.click(
- gh.generateByCondition,
- inputs=[apiKey, llmModel, systemPrompt, recognizedText, llmTemperature, isPipelineEnabledCheckbox, gr.State("click"), saveFileCheckbox, filename, filenamePdf, gr.State(OUTPUT_PATH)],
- outputs=[refinedText, refinedTextMD]
- )
-
- demo.launch()
-
-if __name__ == '__main__':
- main()
+import gradio as gr
+
+# Загрузка параметров конфигурации
+from config import *
+
+# Подгрузка сервисов
+from services.llm_factory import get_llm_provider
+
+
+
+# Загрузка доп. модулей
+from handlers.gradioHandler import GradioHandlers
+from handlers.fileHandlers import FileHandlers
+from services.fasterWhisper import FasterWhisper
+from handlers.convertMdToPdf import ConvertMdToPdf
+from handlers.glueAudio import GlueAudio
+
+gh = GradioHandlers(get_llm_provider, ConvertMdToPdf, FileHandlers, FasterWhisper, GlueAudio)
+
+def main():
+ with gr.Blocks() as demo:
+ gr.HTML('''
+ - Faster Whisper WebUI -
-
+
+ ''')
+
+ with gr.Row():
+ # Вкладка с основным взаимодействием
+ with gr.Tab('Actions'):
+ isPipelineEnabledCheckbox = gr.Checkbox(label='is pipeline enabled', value=True, interactive=True)
+
+ with gr.Row():
+ with gr.Accordion(label='Recognization and integration'):
+ with gr.Column():
+ audioFiles = gr.Files(label='Load audio for transcribe', type="filepath")
+ images = gr.Files(label='Upload images', file_types=['image'])
+ recognizeBtn = gr.Button('recognize and integrate', variant='primary')
+
+ with gr.Accordion(label='Recognized text'):
+ recognizedText = gr.TextArea(label='')
+
+ with gr.Accordion(label='LLM'):
+ with gr.Column():
+ refineTextBtn = gr.Button('refine text', variant='secondary', interactive=False)
+
+ with gr.Accordion(label='Refined text raw'):
+ refinedText = gr.Textbox(label='', show_copy_button=True)
+
+ with gr.Accordion(label='Refined text md formated'):
+ refinedTextMD = gr.Markdown(label='')
+
+ # Вкладка с настройками
+ with gr.Tab('Settings'):
+ with gr.Column():
+ # Первое поле на всю ширину в акордионе настроек
+ with gr.Accordion('File settings'):
+ saveFileCheckbox = gr.Checkbox(label='save file', value=True, interactive=True)
+ filename = gr.Textbox(label='Output filename', value='output.txt', interactive=True)
+ filenamePdf = gr.Textbox(label='Output filename for pdf', value='output.pdf', interactive=True)
+
+ # Акордион настроек faster whisper
+ with gr.Accordion(label='Faster whisper settings'):
+ with gr.Row():
+ # Левая колонка в акордионе
+ with gr.Column():
+ device = gr.Dropdown(label='Device', choices=DEVICES, value=DEVICES[1], interactive=True)
+ compute_type = gr.Dropdown(label='compute_type', choices=COMPUTE_TYPE, value=COMPUTE_TYPE[0], interactive=True)
+ fastWhisperModel = gr.Dropdown(label='Model', choices=FAST_WHISPER_MODELS, value=FAST_WHISPER_MODELS[11], interactive=True)
+
+ beamSize = gr.Number(label='beam_size', value=8, interactive=True)
+ noSpeechThreshold = gr.Number(label='no_speech_threshold', value=0.5, interactive=True)
+ vadFilter = gr.Checkbox(label='vad_filter', value=True, interactive=True)
+ wordTimestamps = gr.Checkbox(label='word_timestamps', value=True, interactive=True)
+ conditionOnPreviousText = gr.Checkbox(label='condition_on_previous_text', value=False, interactive=True)
+
+ # Правая колонка в акордионе
+ with gr.Column():
+ with gr.Accordion(label='Vad parameters'):
+ minSilenceDurationMs = gr.Number(label='min_silence_duration_ms', value=300, interactive=True)
+ speechPadMs = gr.Number(label='speech_pad_ms', value=200, interactive=True)
+
+ with gr.Accordion(label='Temperature'):
+ temp0 = gr.Number(label='temp_0', value=0.0, interactive=True)
+ temp1 = gr.Number(label='temp_1', value=0.2, interactive=True)
+ temp2 = gr.Number(label='temp_2', value=0.4, interactive=True)
+
+ # Нижний акордион настроек для api ключа llm
+ with gr.Accordion(label='LLM settings'):
+ apiKey = gr.Textbox(label='API key (required for io.net, Gemini)', value=DEFAULT_API_KEY, interactive=True)
+
+ with gr.Accordion(label='System prompt'):
+ systemPrompt = gr.Textbox(label='', value=DEFAULT_SYSTEM_PROMPT, interactive=True)
+
+ with gr.Row():
+ # ВЫБОР ПРОВАЙДЕРА
+ llmProvider = gr.Dropdown(
+ label='LLM Provider',
+ choices=LLM_PROVIDERS,
+ value=LLM_PROVIDERS[0],
+ interactive=True
+ )
+ # СПИСОК МОДЕЛЕЙ (теперь зависит от провайдера)
+ llmModel = gr.Dropdown(
+ label='Models',
+ choices=LLM_MODELS[LLM_PROVIDERS[0]], # Модели для провайдера по умолчанию
+ value=LLM_MODELS[LLM_PROVIDERS[0]][1],
+ interactive=True
+ )
+ llmTemperature = gr.Number(label='Temperature', value=0.8, interactive=True)
+
+ isPipelineEnabledCheckbox.change(gh.updateButton, inputs=[isPipelineEnabledCheckbox], outputs=refineTextBtn)
+ saveFileCheckbox.change(gh.updateTextbox, inputs=saveFileCheckbox, outputs=filename)
+ saveFileCheckbox.change(gh.updateTextbox, inputs=saveFileCheckbox, outputs=filenamePdf)
+
+ recognizeBtn.click(gh.handleRecognizeBtn, outputs=[recognizedText], inputs=[audioFiles, fastWhisperModel, device, compute_type, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText, gr.State(GLUED_AUDIO_FILENAME), gr.State(OUTPUT_PATH)])
+
+ # Если пайплайн включен то тогда делаем автоматически
+ # автоматический пайплайн
+ recognizedText.change(
+ gh.generateByCondition,
+ inputs=[apiKey, llmProvider, llmModel, systemPrompt, recognizedText, llmTemperature, isPipelineEnabledCheckbox, gr.State("change"), saveFileCheckbox, filename, filenamePdf, gr.State(OUTPUT_PATH)],
+ outputs=[refinedText, refinedTextMD]
+ )
+
+
+
+ # ручной запуск по кнопке
+ llmProvider.change(
+ gh.update_model_dropdown,
+ inputs=llmProvider,
+ outputs=llmModel
+ )
+ refineTextBtn.click(
+ gh.generateByCondition,
+ inputs=[apiKey, llmProvider, llmModel, systemPrompt, recognizedText, llmTemperature, isPipelineEnabledCheckbox, gr.State("click"), saveFileCheckbox, filename, filenamePdf, gr.State(OUTPUT_PATH)],
+ outputs=[refinedText, refinedTextMD]
+ )
+
+ demo.launch()
+
+if __name__ == '__main__':
+ main()
diff --git a/config.py b/config.py
index fdc64d6..f97901c 100644
--- a/config.py
+++ b/config.py
@@ -1,52 +1,83 @@
-import os
-from dotenv import load_dotenv
-
-load_dotenv()
-
-FAST_WHISPER_MODELS = ['tiny', 'base', 'small', 'medium', 'large-v1', 'large-v2', 'large-v3', 'large', 'distil-large-v2', 'distil-large-v3', 'distil-large-v3.5', 'large-v3-turbo', 'turbo']
-LLM_MODELS = ['openai/gpt-oss-120b', 'Qwen/Qwen3-235B-A22B-Thinking-2507', 'deepseek-ai/DeepSeek-R1-0528', 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8', 'openai/gpt-oss-20b', 'Intel/Qwen3-Coder-480B-A35B-Instruct-int4-mixed-ar', 'meta-llama/Llama-3.2-90B-Vision-Instruct', 'mistralai/Mistral-Nemo-Instruct-2407', 'Qwen/Qwen2.5-VL-32B-Instruct', 'meta-llama/Llama-3.3-70B-Instruct', 'mistralai/Devstral-Small-2505', 'mistralai/Magistral-Small-2506', 'mistralai/Mistral-Large-Instruct-2411', 'CohereForAI/aya-expanse-32b']
-DEVICES = ['cpu', 'cuda']
-COMPUTE_TYPE = ['auto', 'int8', 'float16', 'float32']
-
-# Стандартный API ключ
-DEFAULT_API_KEY=os.getenv('API_KEY')
-# Задаем выходную директорию
-OUTPUT_PATH='outputs'
-
-GLUED_AUDIO_FILENAME='glued.mp3'
-
-DEFAULT_SYSTEM_PROMPT='''You are a diligent university student who has recorded a lecture as an audio file and later transcribed it into raw text.
-Your task is to rewrite this unstructured transcript into a clear, logically organized, and detailed lecture summary (lecture notes).
-
-Guidelines:
-1. Structure:
- - Organize the text into a hierarchy of sections and subsections.
- - Use headings, bullet points, or numbering where appropriate.
- - Present the material in a logical flow (from introduction → main points → details → examples → conclusion).
-
-2. Clarity & Cohesion:
- - Remove filler words, repetitions, and irrelevant fragments.
- - Rewrite incomplete sentences into full, grammatically correct sentences.
- - Ensure smooth transitions between topics, making the summary feel continuous and well-connected.
-
-3. Depth & Detail:
- - Capture all important concepts, definitions, examples, and explanations from the lecture.
- - Expand shorthand or fragmented thoughts into full, precise explanations.
- - Where appropriate, rephrase or clarify confusing passages for better understanding.
-
-4. Accuracy:
- - Preserve the lecturer’s original meaning, intent, and terminology.
- - Avoid adding personal opinions or new information that was not in the lecture.
-
-5. Style:
- - Write in a formal, academic tone suitable for study notes.
- - Aim for readability: concise sentences, but thorough coverage of concepts.
- - Use emphasis (e.g., bold or italic text) only when it improves comprehension.
-
-Final Output: A cohesive, detailed, and well-structured lecture summary, suitable for later studying and revision.
-Use only russian language!
-USE LATEX IN DOLLAR SIGN ($)!
-EXTRA BIG LENTH OF CONSPECT!
-MAKE AS LONG AS POSIBLE AND AS BE GOOD!
-'''
-
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+FAST_WHISPER_MODELS = ['tiny', 'base', 'small', 'medium', 'large-v1', 'large-v2', 'large-v3', 'large', 'distil-large-v2', 'distil-large-v3', 'distil-large-v3.5', 'large-v3-turbo', 'turbo']
+DEVICES = ['cpu', 'cuda']
+COMPUTE_TYPE = ['auto', 'int8', 'float16', 'float32']
+
+# Стандартный API ключ
+DEFAULT_API_KEY=os.getenv('API_KEY')
+
+# Словарь провайдеров и их моделей
+LLM_PROVIDERS = ['io.net', 'Gemini', 'gpt4free']
+LLM_MODELS = {
+ 'io.net': [
+ 'openai/gpt-oss-120b', 'Qwen/Qwen3-235B-A22B-Thinking-2507',
+ 'deepseek-ai/DeepSeek-R1-0528', 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8',
+ 'openai/gpt-oss-20b', 'Intel/Qwen3-Coder-480B-A35B-Instruct-int4-mixed-ar',
+ 'meta-llama/Llama-3.2-90B-Vision-Instruct', 'mistralai/Mistral-Nemo-Instruct-2407',
+ 'Qwen/Qwen2.5-VL-32B-Instruct', 'meta-llama/Llama-3.3-70B-Instruct',
+ 'mistralai/Devstral-Small-2505', 'mistralai/Magistral-Small-2506',
+ 'mistralai/Mistral-Large-Instruct-2411', 'CohereForAI/aya-expanse-32b'
+ ],
+ 'Gemini': [
+ 'gemini-2.5-pro',
+ 'gemini-2.5-flash',
+ 'gemini-2.5-flash-lite'
+ ],
+ 'gpt4free': [ # Модели могут меняться, проверьте документацию g4f
+ 'default',
+ 'gpt-4',
+ 'sonar-reasoning',
+ 'command-r-plus',
+ 'llama-3.3-70b',
+ 'hermes-3-llama-3.1-405b'
+ 'qwen-3-235b',
+ 'gpt-4o-mini',
+ 'deepseek-r1',
+ 'PollinationsAI:gpt-5-nano'
+ ]
+}
+
+# Задаем выходную директорию
+OUTPUT_PATH='outputs'
+
+GLUED_AUDIO_FILENAME='glued.mp3'
+
+DEFAULT_SYSTEM_PROMPT='''You are a diligent university student who has recorded a lecture as an audio file and later transcribed it into raw text.
+Your task is to rewrite this unstructured transcript into a clear, logically organized, and detailed lecture summary (lecture notes).
+
+Guidelines:
+1. Structure:
+ - Organize the text into a hierarchy of sections and subsections.
+ - Use headings, bullet points, or numbering where appropriate.
+ - Present the material in a logical flow (from introduction → main points → details → examples → conclusion).
+
+2. Clarity & Cohesion:
+ - Remove filler words, repetitions, and irrelevant fragments.
+ - Rewrite incomplete sentences into full, grammatically correct sentences.
+ - Ensure smooth transitions between topics, making the summary feel continuous and well-connected.
+
+3. Depth & Detail:
+ - Capture all important concepts, definitions, examples, and explanations from the lecture.
+ - Expand shorthand or fragmented thoughts into full, precise explanations.
+ - Where appropriate, rephrase or clarify confusing passages for better understanding.
+
+4. Accuracy:
+ - Preserve the lecturer’s original meaning, intent, and terminology.
+ - Avoid adding personal opinions or new information that was not in the lecture.
+
+5. Style:
+ - Write in a formal, academic tone suitable for study notes.
+ - Aim for readability: concise sentences, but thorough coverage of concepts.
+ - Use emphasis (e.g., bold or italic text) only when it improves comprehension.
+
+Final Output: A cohesive, detailed, and well-structured lecture summary, suitable for later studying and revision.
+Use only russian language!
+USE LATEX IN DOLLAR SIGN ($)!
+EXTRA BIG LENTH OF CONSPECT!
+MAKE AS LONG AS POSIBLE AND AS BE GOOD!
+'''
+
diff --git a/handlers/convertMdToPdf.py b/handlers/convertMdToPdf.py
index 3e20506..6bf19a4 100644
--- a/handlers/convertMdToPdf.py
+++ b/handlers/convertMdToPdf.py
@@ -1,36 +1,36 @@
-import re
-from pylatexenc.latex2text import LatexNodes2Text
-from markdown_pdf import MarkdownPdf
-from markdown_pdf import Section
-
-class ConvertMdToPdf:
- # Конвертирует md в pdf
- def convertLatexToText(self, text:str):
- '''
- Функция для конвертации LaTeX в текст;
-
- Args:
- :param text: текст содержащий LaTeX.
- '''
-
- # Обрабатываем только математические выражения
- text = re.sub(
- r'\$\$(.*?)\$\$|\$(.*?)\$',
- self.replace_math,
- text,
- flags=re.DOTALL
- )
- pdf = MarkdownPdf(toc_level=0, optimize=True)
- pdf.add_section(Section(text))
- return pdf, text
-
- def replace_math(self, match):
- math_content = match.group(1) or match.group(2) # $$...$$ или $...$
- try:
- # Преобразуем только математическое выражение
- converted = LatexNodes2Text().latex_to_text(math_content)
-
- return converted
- except:
- return math_content # В случае ошибки оставляем как есть
-
+import re
+from pylatexenc.latex2text import LatexNodes2Text
+from markdown_pdf import MarkdownPdf
+from markdown_pdf import Section
+
+class ConvertMdToPdf:
+ # Конвертирует md в pdf
+ def convertLatexToText(self, text:str):
+ '''
+ Функция для конвертации LaTeX в текст;
+
+ Args:
+ :param text: текст содержащий LaTeX.
+ '''
+
+ # Обрабатываем только математические выражения
+ text = re.sub(
+ r'\$\$(.*?)\$\$|\$(.*?)\$',
+ self.replace_math,
+ text,
+ flags=re.DOTALL
+ )
+ pdf = MarkdownPdf(toc_level=0, optimize=True)
+ pdf.add_section(Section(text))
+ return pdf, text
+
+ def replace_math(self, match):
+ math_content = match.group(1) or match.group(2) # $$...$$ или $...$
+ try:
+ # Преобразуем только математическое выражение
+ converted = LatexNodes2Text().latex_to_text(math_content)
+
+ return converted
+ except:
+ return math_content # В случае ошибки оставляем как есть
+
diff --git a/handlers/fileHandlers.py b/handlers/fileHandlers.py
index d466100..889353e 100644
--- a/handlers/fileHandlers.py
+++ b/handlers/fileHandlers.py
@@ -1,31 +1,31 @@
-from pathlib import Path
-
-# Для аннотации типов
-from markdown_pdf import MarkdownPdf
-from pydub import AudioSegment
-
-class FileHandlers:
- # Функция сохранения файла
- def saveFile(self, filename, content, output_path, format='mp3'):
- '''
- Сохраняет текст, pdf из markdown_pdf или склеенный аудиофайл в файл с указанным названием и директорией.
-
- Args:
- :param filename: название файла;
- :param content: содержание файла;
- :param output_path: выходная диретория файла.
- '''
- # Создание объекта директории
- directory = Path(output_path)
- filePath = directory / filename # Добавление пути директории
- filePath.parent.mkdir(parents=True, exist_ok=True) # Создание директории если не существует
-
- # Сохранение для разных типов
- if type(content) == MarkdownPdf:
- return content.save(filePath)
-
- elif type(content) == str:
- return filePath.write_text(content, encoding='utf-8')
-
- elif type(content) == AudioSegment:
- return content.export(filePath, format=format)
+from pathlib import Path
+
+# Для аннотации типов
+from markdown_pdf import MarkdownPdf
+from pydub import AudioSegment
+
+class FileHandlers:
+ # Функция сохранения файла
+ def saveFile(self, filename, content, output_path, format='mp3'):
+ '''
+ Сохраняет текст, pdf из markdown_pdf или склеенный аудиофайл в файл с указанным названием и директорией.
+
+ Args:
+ :param filename: название файла;
+ :param content: содержание файла;
+ :param output_path: выходная диретория файла.
+ '''
+ # Создание объекта директории
+ directory = Path(output_path)
+ filePath = directory / filename # Добавление пути директории
+ filePath.parent.mkdir(parents=True, exist_ok=True) # Создание директории если не существует
+
+ # Сохранение для разных типов
+ if type(content) == MarkdownPdf:
+ return content.save(filePath)
+
+ elif type(content) == str:
+ return filePath.write_text(content, encoding='utf-8')
+
+ elif type(content) == AudioSegment:
+ return content.export(filePath, format=format)
diff --git a/handlers/glueAudio.py b/handlers/glueAudio.py
index 4c07a46..55e10c1 100644
--- a/handlers/glueAudio.py
+++ b/handlers/glueAudio.py
@@ -1,11 +1,11 @@
-from pydub import AudioSegment
-
-class GlueAudio():
- def glue(self, audioFiles):
- glued = AudioSegment.empty()
-
- for audioFile in audioFiles:
- audio = AudioSegment.from_file(audioFile)
- glued += audio
-
- return glued
+from pydub import AudioSegment
+
+class GlueAudio():
+ def glue(self, audioFiles):
+ glued = AudioSegment.empty()
+
+ for audioFile in audioFiles:
+ audio = AudioSegment.from_file(audioFile)
+ glued += audio
+
+ return glued
diff --git a/handlers/gradioHandler.py b/handlers/gradioHandler.py
index f5286c5..14f6c45 100644
--- a/handlers/gradioHandler.py
+++ b/handlers/gradioHandler.py
@@ -1,62 +1,67 @@
-class GradioHandlers:
- def __init__(self, gr, Llm, ConvertMdToPdf, FileHandlers, FasterWhisper, GlueAudio):
- # Объект для работы с файлами
- self.fh = FileHandlers()
- self.ga = GlueAudio()
- self.ConvertMdToPdf = ConvertMdToPdf()
- self.FasterWhisper = FasterWhisper()
- self.Llm = Llm
- self.gr = gr
-
- def handleRecognizeBtn(self, audioFiles, model, device, compute_type, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText, filename, outPath):
- audioFile = self.ga.glue(audioFiles)
- file = self.fh.saveFile(filename, audioFile, outPath)
-
- return self.FasterWhisper.recognize(model, device, compute_type, file, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText)
-
- # Функция улучшения текста
- def generateByCondition(self, api_key, llm_model, system_prompt, recognized_text, llm_temperature, is_pipeline_enabled, trigger, isSaveFile, filename, filenamePdf, output_path):
- llm = self.Llm(api_key)
-
- # если чекбокс включен и событие было change → обрабатываем
- if is_pipeline_enabled and trigger == "change":
- result, md = llm.generate(llm_model, system_prompt, recognized_text, llm_temperature)
-
- # Конвертируем текст с латексом в юникод
- pdf, unicodeText = self.ConvertMdToPdf.convertLatexToText(md)
-
- if isSaveFile:
- self.fh.saveFile(filenamePdf, pdf, output_path)
- self.fh.saveFile(filename, result, output_path)
-
- return result, unicodeText
-
- # если чекбокс выключен и событие было click → обрабатываем
- if not is_pipeline_enabled and trigger == "click":
- result, md = llm.generate(llm_model, system_prompt, recognized_text, llm_temperature)
-
- # Конвертируем текст с латексом в юникод
- pdf, unicodeText = self.ConvertMdToPdf.convertLatexToText(md)
-
- if isSaveFile:
- self.fh.saveFile(filenamePdf, pdf, output_path)
- self.fh.saveFile(filename, result, output_path)
-
- return result, unicodeText
-
- # если нет чекбокса и было событие change
- return self.gr.skip(), self.gr.skip()
-
- # Функция для динамического обновления кнопки в зависимости от состояния checkbox
- def updateButton(self, isChecked):
- if not isChecked:
- variant = 'primary'
- else:
- variant = 'secondary'
-
- return self.gr.update(interactive=not isChecked, variant=variant)
-
-
- def updateTextbox(self, isChecked):
- return self.gr.update(visible=isChecked)
-
+from config import LLM_MODELS # Импортируем словарь моделей
+import gradio as gr
+
+
+class GradioHandlers:
+ def __init__(self, llm_factory, ConvertMdToPdf, FileHandlers, FasterWhisper, GlueAudio):
+ # Объект для работы с файлами
+ self.fh = FileHandlers()
+ self.ga = GlueAudio()
+ self.ConvertMdToPdf = ConvertMdToPdf()
+ self.FasterWhisper = FasterWhisper()
+ self.llm_factory = llm_factory # Сохраняем фабрику
+
+ def handleRecognizeBtn(self, audioFiles, model, device, compute_type, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText, filename, outPath):
+ audioFile = self.ga.glue(audioFiles)
+ file = self.fh.saveFile(filename, audioFile, outPath)
+
+ return self.FasterWhisper.recognize(model, device, compute_type, file, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText)
+
+ # Функция улучшения текста
+ def generateByCondition(self, api_key, llm_provider, llm_model, system_prompt, recognized_text, llm_temperature, is_pipeline_enabled, trigger, isSaveFile, filename, filenamePdf, output_path):
+ try:
+ # Получаем нужный провайдер через фабрику
+ provider = self.llm_factory(llm_provider, api_key)
+ except ValueError as e:
+ # Если API ключ не предоставлен для нужного провайдера, выводим ошибку
+ self.gr.Warning(str(e))
+ return self.gr.skip(), self.gr.skip()
+
+ def process():
+ result, md = provider.generate(llm_model, system_prompt, recognized_text, llm_temperature)
+ pdf, unicodeText = self.ConvertMdToPdf.convertLatexToText(md)
+ if isSaveFile:
+ self.fh.saveFile(filenamePdf, pdf, output_path)
+ self.fh.saveFile(filename, result, output_path)
+ return result, unicodeText
+
+ if (is_pipeline_enabled and trigger == "change") or (not is_pipeline_enabled and trigger == "click"):
+ return process()
+
+ return gr.skip(), gr.skip()
+
+ # НОВАЯ ФУНКЦИЯ для обновления списка моделей
+ def update_model_dropdown(self, provider):
+ """
+ Вызывается при изменении llmProvider.
+ Возвращает обновленный компонент Dropdown для моделей.
+ """
+ # Получаем список моделей для выбранного провайдера
+ models = LLM_MODELS.get(provider, [])
+
+ # Выбираем первое значение по умолчанию, если список не пуст
+ default_value = models[0] if models else None
+
+ # Возвращаем обновленный компонент. Используем 'gr' напрямую.
+ return gr.update(choices=models, value=default_value)
+
+ # Функция для динамического обновления кнопки
+ def updateButton(self, isChecked):
+ if not isChecked:
+ variant = 'primary'
+ else:
+ variant = 'secondary'
+ return gr.update(interactive=not isChecked, variant=variant)
+
+ def updateTextbox(self, isChecked):
+ return gr.update(visible=isChecked)
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 570730f..f077318 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,102 +1,102 @@
-aiofiles==24.1.0
-annotated-types==0.7.0
-anyio==4.10.0
-av==15.1.0
-beautifulsoup4==4.13.5
-Brotli==1.1.0
-bs4==0.0.2
-certifi==2025.8.3
-cffi==2.0.0
-charset-normalizer==3.4.3
-click==8.2.1
-coloredlogs==15.0.1
-colour==0.1.5
-cssselect2==0.8.0
-ctranslate2==4.6.0
-distro==1.9.0
-dotenv==0.9.9
-exceptiongroup==1.3.0
-fastapi==0.116.1
-faster-whisper==1.2.0
-ffmpeg-python==0.2.0
-ffmpy==0.6.1
-filelock==3.19.1
-flatbuffers==25.2.10
-flatlatex==0.15
-fonttools==4.59.2
-fsspec==2025.9.0
-future==1.0.0
-gradio==5.44.1
-gradio_client==1.12.1
-groovy==0.1.2
-h11==0.16.0
-hf-xet==1.1.9
-httpcore==1.0.9
-httpx==0.28.1
-huggingface-hub==0.34.4
-humanfriendly==10.0
-idna==3.10
-iso639-lang==2.6.3
-Jinja2==3.1.6
-jiter==0.10.0
-joblib==1.5.2
-langdetect==1.0.9
-littleutils==0.2.4
-markdown-it-py==3.0.0
-markdown_pdf==1.9
-MarkupSafe==3.0.2
-mdurl==0.1.2
-mpmath==1.3.0
-nltk==3.9.1
-numpy==2.2.6
-onnxruntime==1.22.1
-openai==1.106.1
-orjson==3.11.3
-outdated==0.2.2
-packaging==25.0
-pandas==2.3.2
-pillow==11.3.0
-protobuf==6.32.0
-pycparser==2.22
-pydantic==2.11.7
-pydantic_core==2.33.2
-pydub==0.25.1
-pydyf==0.11.0
-Pygments==2.19.2
-pylatexenc==2.10
-pymultidictionary==1.3.2
-PyMuPDF==1.26.4
-pyperclip==1.9.0
-pyphen==0.17.2
-python-dateutil==2.9.0.post0
-python-dotenv==1.1.1
-python-multipart==0.0.20
-pytz==2025.2
-PyYAML==6.0.2
-regex==2025.9.1
-requests==2.32.5
-rich==14.1.0
-ruff==0.12.12
-safehttpx==0.1.6
-semantic-version==2.10.0
-shellingham==1.5.4
-six==1.17.0
-sniffio==1.3.1
-soupsieve==2.8
-starlette==0.47.3
-sympy==1.14.0
-tinycss2==1.4.0
-tinyhtml5==2.0.0
-tkmacosx==1.0.5
-tokenizers==0.22.0
-tomlkit==0.13.3
-tqdm==4.67.1
-typer==0.17.4
-typing-inspection==0.4.1
-typing_extensions==4.15.0
-tzdata==2025.2
-urllib3==2.5.0
-uvicorn==0.35.0
-webencodings==0.5.1
-websockets==15.0.1
-zopfli==0.2.3.post1
+aiofiles==24.1.0
+annotated-types==0.7.0
+anyio==4.10.0
+av==15.1.0
+beautifulsoup4==4.13.5
+Brotli==1.1.0
+bs4==0.0.2
+certifi==2025.8.3
+cffi==2.0.0
+charset-normalizer==3.4.3
+click==8.2.1
+coloredlogs==15.0.1
+colour==0.1.5
+cssselect2==0.8.0
+ctranslate2==4.6.0
+distro==1.9.0
+dotenv==0.9.9
+exceptiongroup==1.3.0
+fastapi==0.116.1
+faster-whisper==1.2.0
+ffmpeg-python==0.2.0
+ffmpy==0.6.1
+filelock==3.19.1
+flatbuffers==25.2.10
+flatlatex==0.15
+fonttools==4.59.2
+fsspec==2025.9.0
+future==1.0.0
+gradio==5.44.1
+gradio_client==1.12.1
+groovy==0.1.2
+h11==0.16.0
+hf-xet==1.1.9
+httpcore==1.0.9
+httpx==0.28.1
+huggingface-hub==0.34.4
+humanfriendly==10.0
+idna==3.10
+iso639-lang==2.6.3
+Jinja2==3.1.6
+jiter==0.10.0
+joblib==1.5.2
+langdetect==1.0.9
+littleutils==0.2.4
+markdown-it-py==3.0.0
+markdown_pdf==1.9
+MarkupSafe==3.0.2
+mdurl==0.1.2
+mpmath==1.3.0
+nltk==3.9.1
+numpy==2.2.6
+onnxruntime==1.22.1
+openai==1.106.1
+orjson==3.11.3
+outdated==0.2.2
+packaging==25.0
+pandas==2.3.2
+pillow==11.3.0
+protobuf==6.32.0
+pycparser==2.22
+pydantic==2.11.7
+pydantic_core==2.33.2
+pydub==0.25.1
+pydyf==0.11.0
+Pygments==2.19.2
+pylatexenc==2.10
+pymultidictionary==1.3.2
+PyMuPDF==1.26.4
+pyperclip==1.9.0
+pyphen==0.17.2
+python-dateutil==2.9.0.post0
+python-dotenv==1.1.1
+python-multipart==0.0.20
+pytz==2025.2
+PyYAML==6.0.2
+regex==2025.9.1
+requests==2.32.5
+rich==14.1.0
+ruff==0.12.12
+safehttpx==0.1.6
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.17.0
+sniffio==1.3.1
+soupsieve==2.8
+starlette==0.47.3
+sympy==1.14.0
+tinycss2==1.4.0
+tinyhtml5==2.0.0
+tkmacosx==1.0.5
+tokenizers==0.22.0
+tomlkit==0.13.3
+tqdm==4.67.1
+typer==0.17.4
+typing-inspection==0.4.1
+typing_extensions==4.15.0
+tzdata==2025.2
+urllib3==2.5.0
+uvicorn==0.35.0
+webencodings==0.5.1
+websockets==15.0.1
+zopfli==0.2.3.post1
diff --git a/services/fasterWhisper.py b/services/fasterWhisper.py
index 1d0349d..7e32d52 100644
--- a/services/fasterWhisper.py
+++ b/services/fasterWhisper.py
@@ -1,34 +1,34 @@
-from faster_whisper import WhisperModel
-
-class FasterWhisper:
- def recognize(self, model, device, compute_type, audioFile, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText):
- model = WhisperModel(model, device=device, compute_type=compute_type) # Задаем модель
-
- segments, _ = model.transcribe( # Распознаем текст
- audioFile,
- beam_size=beamSize,
- vad_filter=vadFilter,
- vad_parameters={
- "min_silence_duration_ms": minSilenceDurationMs,
- "speech_pad_ms": speechPadMs
- },
- temperature= [temp0, temp1, temp2],
- word_timestamps=wordTimestamps,
- no_speech_threshold=noSpeechThreshold,
- condition_on_previous_text=conditionOnPreviousText
- )
-
- text = ''
-
- for seg in segments:
- text += f"[{self.format_timestamp(seg.start)} -> {self.format_timestamp(seg.end)}] {seg.text}" + '\n'
-
- return text
-
- def format_timestamp(self, seconds: float) -> str:
- millis = int(seconds * 1000)
- hours = millis // (3600 * 1000)
- minutes = (millis % (3600 * 1000)) // (60 * 1000)
- seconds_int = (millis % (60 * 1000)) // 1000
- millis = millis % 1000
- return f"{hours:02d}:{minutes:02d}:{seconds_int:02d},{millis:03d}"
+from faster_whisper import WhisperModel
+
+class FasterWhisper:
+ def recognize(self, model, device, compute_type, audioFile, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText):
+ model = WhisperModel(model, device=device, compute_type=compute_type) # Задаем модель
+
+ segments, _ = model.transcribe( # Распознаем текст
+ audioFile,
+ beam_size=beamSize,
+ vad_filter=vadFilter,
+ vad_parameters={
+ "min_silence_duration_ms": minSilenceDurationMs,
+ "speech_pad_ms": speechPadMs
+ },
+ temperature= [temp0, temp1, temp2],
+ word_timestamps=wordTimestamps,
+ no_speech_threshold=noSpeechThreshold,
+ condition_on_previous_text=conditionOnPreviousText
+ )
+
+ text = ''
+
+ for seg in segments:
+ text += f"[{self.format_timestamp(seg.start)} -> {self.format_timestamp(seg.end)}] {seg.text}" + '\n'
+
+ return text
+
+ def format_timestamp(self, seconds: float) -> str:
+ millis = int(seconds * 1000)
+ hours = millis // (3600 * 1000)
+ minutes = (millis % (3600 * 1000)) // (60 * 1000)
+ seconds_int = (millis % (60 * 1000)) // 1000
+ millis = millis % 1000
+ return f"{hours:02d}:{minutes:02d}:{seconds_int:02d},{millis:03d}"
diff --git a/services/llm.py b/services/llm.py
deleted file mode 100644
index d54f924..0000000
--- a/services/llm.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import openai
-
-class Llm:
- def __init__(self, apiKey:str):
- self.client = openai.OpenAI(
- api_key=apiKey,
- base_url='https://api.intelligence.io.solutions/api/v1/'
- )
-
- def generate(self, model:str, systemPrompt:str, userPrompt:str, temp:float):
- '''
- Функция для генирации текста по промпту.
-
- Args:
- :param model: модель llm;
- :param systemPrompt: системный промпт;
- :param userPrompt: основной промпт промпт;
- :param temp: температура генерации.
- '''
-
- # Получаем ответ от нейросети
- response = self.client.chat.completions.create(
- model=model,
- messages=[
- {'role': 'system', 'content': systemPrompt},
- {'role': 'user', 'content': userPrompt},
- ],
- temperature=temp,
- stream=False
- )
-
- # Достаем текст
- text = str(response.choices[0].message.content)
-
- return text, text
-
diff --git a/services/llm_factory.py b/services/llm_factory.py
new file mode 100644
index 0000000..32aed24
--- /dev/null
+++ b/services/llm_factory.py
@@ -0,0 +1,22 @@
+# services/llm_factory.py
+from services.llm_providers.ionet_provider import IoNetProvider
+from services.llm_providers.gemini_provider import GeminiProvider
+from services.llm_providers.gpt4free_provider import Gpt4FreeProvider
+from services.llm_providers.base_provider import BaseLLMProvider
+
+def get_llm_provider(provider_name: str, api_key: str | None) -> BaseLLMProvider:
+ """
+ Фабричная функция для получения экземпляра провайдера LLM.
+ """
+ if provider_name == 'io.net':
+ if not api_key:
+ raise ValueError("API ключ обязателен для io.net")
+ return IoNetProvider(api_key)
+ elif provider_name == 'Gemini':
+ if not api_key:
+ raise ValueError("API ключ обязателен для Gemini")
+ return GeminiProvider(api_key)
+ elif provider_name == 'gpt4free':
+ return Gpt4FreeProvider()
+ else:
+ raise ValueError(f"Неизвестный провайдер: {provider_name}")
\ No newline at end of file
diff --git a/services/llm_providers/base_provider.py b/services/llm_providers/base_provider.py
new file mode 100644
index 0000000..3b98551
--- /dev/null
+++ b/services/llm_providers/base_provider.py
@@ -0,0 +1,18 @@
+from abc import ABC, abstractmethod
+
+class BaseLLMProvider(ABC):
+ """
+ Абстрактный базовый класс для всех провайдеров LLM.
+ Каждый провайдер должен реализовать метод generate.
+ """
+ def __init__(self, api_key: str | None = None):
+ self.api_key = api_key
+
+ @abstractmethod
+ def generate(self, model: str, system_prompt: str, user_prompt: str, temp: float):
+ """
+ Основной метод для генерации текста.
+
+ Должен возвращать кортеж из двух строк: (чистый_текст, markdown_текст)
+ """
+ pass
\ No newline at end of file
diff --git a/services/llm_providers/gemini_provider.py b/services/llm_providers/gemini_provider.py
new file mode 100644
index 0000000..f87ac36
--- /dev/null
+++ b/services/llm_providers/gemini_provider.py
@@ -0,0 +1,74 @@
+# services/llm_providers/gemini_provider.py
+
+import requests
+from .base_provider import BaseLLMProvider
+
+class GeminiProvider(BaseLLMProvider):
+ """
+ Провайдер для Google Gemini, использующий прямые REST API вызовы
+ через библиотеку requests для надежной работы с SOCKS-прокси.
+ """
+ def __init__(self, api_key: str):
+ super().__init__(api_key)
+ self.base_url = "https://generativelanguage.googleapis.com/v1beta/models/"
+
+ def generate(self, model: str, system_prompt: str, user_prompt: str, temp: float):
+ """
+ Генерирует текст с помощью модели Gemini, отправляя запрос через прокси.
+ """
+ # 1. Формируем URL для запроса
+ api_url = f"{self.base_url}{model}:generateContent?key={self.api_key}"
+
+ # 2. Задаем настройки прокси из вашего примера
+ # socks5h:// означает, что DNS-запросы также будут идти через прокси
+ proxies = {
+ 'http': 'socks5://192.168.1.6:2080',
+ 'https': 'socks5h://192.168.1.6:2080'
+ }
+
+ # 3. Собираем тело запроса (payload) в формате, который ожидает Gemini API
+ data = {
+ "system_instruction": {
+ "parts": {"text": system_prompt}
+ },
+ "contents": [{
+ "parts": [{"text": user_prompt}]
+ }],
+ "generationConfig": {
+ "temperature": temp
+ }
+ }
+
+ try:
+ # 4. Отправляем POST-запрос с данными и настройками прокси
+ response = requests.post(api_url, json=data, proxies=proxies, timeout=90)
+
+ # Проверяем, не вернул ли сервер ошибку (например, 4xx или 5xx)
+ response.raise_for_status()
+
+ # 5. Парсим JSON-ответ и извлекаем сгенерированный текст
+ response_json = response.json()
+
+ # Добавим проверку на случай, если контент был заблокирован
+ if "candidates" not in response_json or not response_json["candidates"]:
+ block_reason = response_json.get("promptFeedback", {}).get("blockReason", "неизвестная причина")
+ error_message = f"Контент заблокирован. Причина: {block_reason}"
+ return error_message, error_message
+
+ text = response_json["candidates"][0]["content"]["parts"][0]["text"]
+ return text, text
+
+ except requests.exceptions.ProxyError as e:
+ error_message = f"Ошибка подключения к прокси. Убедитесь, что Nekobox запущен и слушает порт 2080. Ошибка: {e}"
+ print(error_message)
+ return error_message, error_message
+ except requests.exceptions.RequestException as e:
+ # Ловим все остальные ошибки requests (таймаут, проблемы с сетью и т.д.)
+ error_message = f"Произошла ошибка при обращении к API Gemini: {e}"
+ print(error_message)
+ return error_message, error_message
+ except (KeyError, IndexError) as e:
+ # Ловим ошибки, если структура JSON-ответа неожиданная
+ error_message = f"Не удалось разобрать ответ от API Gemini. Структура ответа изменилась. Ошибка: {e}"
+ print(error_message)
+ return error_message, error_message
\ No newline at end of file
diff --git a/services/llm_providers/gpt4free_provider.py b/services/llm_providers/gpt4free_provider.py
new file mode 100644
index 0000000..25c954c
--- /dev/null
+++ b/services/llm_providers/gpt4free_provider.py
@@ -0,0 +1,28 @@
+# services/llm_providers/gpt4free_provider.py
+from g4f.client import Client
+from .base_provider import BaseLLMProvider
+
+class Gpt4FreeProvider(BaseLLMProvider):
+ # gpt4free не требует API ключа
+ def __init__(self, api_key: str | None = None):
+ super().__init__(api_key)
+ self.client = Client()
+
+
+ def generate(self, model: str, system_prompt: str, user_prompt: str, temp: float):
+ # temp в g4f может работать не для всех внутренних провайдеров
+ try:
+ response = self.client.chat.completions.create(
+ model=model, # Пример модели, может варьироваться в зависимости от доступности провайдеров
+ messages=[
+ {"role": "system", "content": system_prompt},
+ {"role": "user", "content": user_prompt}
+ ],
+ temperature=temp
+ )
+ text = response.choices[0].message.content
+ return text, text
+ except Exception as e:
+ error_message = f"Ошибка при работе с gpt4free: {e}"
+ print(error_message)
+ return error_message, error_message
\ No newline at end of file
diff --git a/services/llm_providers/ionet_provider.py b/services/llm_providers/ionet_provider.py
new file mode 100644
index 0000000..9840f71
--- /dev/null
+++ b/services/llm_providers/ionet_provider.py
@@ -0,0 +1,23 @@
+import openai
+from .base_provider import BaseLLMProvider
+
+class IoNetProvider(BaseLLMProvider):
+ def __init__(self, api_key: str):
+ super().__init__(api_key)
+ self.client = openai.OpenAI(
+ api_key=self.api_key,
+ base_url='https://api.intelligence.io.solutions/api/v1/'
+ )
+
+ def generate(self, model: str, system_prompt: str, user_prompt: str, temp: float):
+ response = self.client.chat.completions.create(
+ model=model,
+ messages=[
+ {'role': 'system', 'content': system_prompt},
+ {'role': 'user', 'content': user_prompt},
+ ],
+ temperature=temp,
+ stream=False
+ )
+ text = str(response.choices[0].message.content)
+ return text, text # Возвращаем как чистый текст, так и Markdown
\ No newline at end of file