change structure of the project
Changes: Move some modules for it's own file and move config to the `config.py`
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swrneko
88
app.py
88
app.py
@@ -1,79 +1,14 @@
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import gradio as gr
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from faster_whisper import WhisperModel
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from pathlib import Path
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from dotenv import load_dotenv
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from typing import Optional
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import os
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# Подгрузка сервисов
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from services.llm import Llm
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from services.fasterWhisper import FasterWhisper
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load_dotenv()
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# Стандартный API ключ
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DEFAULT_API_KEY=os.getenv('API_KEY')
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# Задаем выходную директорию
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OUTPUT_PATH='outputs'
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# Стандартный системный промпт
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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.
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Your task is to rewrite this unstructured transcript into a clear, logically organized, and detailed lecture summary (lecture notes).
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Guidelines:
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1. Structure:
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- Organize the text into a hierarchy of sections and subsections.
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- Use headings, bullet points, or numbering where appropriate.
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- Present the material in a logical flow (from introduction → main points → details → examples → conclusion).
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2. Clarity & Cohesion:
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- Remove filler words, repetitions, and irrelevant fragments.
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- Rewrite incomplete sentences into full, grammatically correct sentences.
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- Ensure smooth transitions between topics, making the summary feel continuous and well-connected.
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3. Depth & Detail:
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- Capture all important concepts, definitions, examples, and explanations from the lecture.
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- Expand shorthand or fragmented thoughts into full, precise explanations.
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- Where appropriate, rephrase or clarify confusing passages for better understanding.
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4. Accuracy:
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- Preserve the lecturer’s original meaning, intent, and terminology.
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- Avoid adding personal opinions or new information that was not in the lecture.
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5. Style:
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- Write in a formal, academic tone suitable for study notes.
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- Aim for readability: concise sentences, but thorough coverage of concepts.
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- Use emphasis (e.g., bold or italic text) only when it improves comprehension.
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Final Output: A cohesive, detailed, and well-structured lecture summary, suitable for later studying and revision.
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Use only russian language!
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'''
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faster_whisper_models_list = ['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']
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llm_models_list = ['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']
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# Загрузка параметров конфигурации
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from config import *
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# Функция транскрибации
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def recognize(model, audioFile, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText):
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model = WhisperModel(model, device='cuda', compute_type='float16') # Задаем модель
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segments, _ = model.transcribe( # Распознаем текст
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audioFile,
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beam_size=beamSize,
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vad_filter=vadFilter,
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vad_parameters={
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"min_silence_duration_ms": minSilenceDurationMs,
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"speech_pad_ms": speechPadMs
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},
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temperature= [temp0, temp1, temp2],
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word_timestamps=wordTimestamps,
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no_speech_threshold=noSpeechThreshold,
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condition_on_previous_text=conditionOnPreviousText
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)
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text = ''
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for seg in segments:
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text += f"[{format_timestamp(seg.start)} -> {format_timestamp(seg.end)}] {seg.text}" + '\n'
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return(text)
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def generateByCondition(api_key, llm_model, system_prompt, recognized_text, llm_temperature, is_pipeline_enabled, trigger, isSaveFile):
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llm = Llm(api_key)
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@@ -98,13 +33,6 @@ def generateByCondition(api_key, llm_model, system_prompt, recognized_text, llm_
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# если нет чекбокса и было событие change
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return gr.skip(), gr.skip()
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def format_timestamp(seconds: float) -> str:
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millis = int(seconds * 1000)
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hours = millis // (3600 * 1000)
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minutes = (millis % (3600 * 1000)) // (60 * 1000)
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seconds_int = (millis % (60 * 1000)) // 1000
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millis = millis % 1000
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return f"{hours:02d}:{minutes:02d}:{seconds_int:02d},{millis:03d}"
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# Функция сохранения файла
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def saveFile(filename, text):
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@@ -123,7 +51,6 @@ def updateButton(isChecked):
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return gr.update(interactive=not isChecked, variant=variant)
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def updateTextbox(isChecked):
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return gr.update(visible=isChecked)
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@@ -161,7 +88,7 @@ with gr.Blocks() as demo:
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with gr.Row():
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# Левая колонка в акордионе
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with gr.Column():
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fastWhisperModel = gr.Dropdown(label='Model', choices=faster_whisper_models_list, interactive=True)
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fastWhisperModel = gr.Dropdown(label='Model', choices=FAST_WHISPER_MODELS, value=FAST_WHISPER_MODELS[11], interactive=True)
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beamSize = gr.Number(label='beam_size', value=8, interactive=True)
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noSpeechThreshold = gr.Number(label='no_speech_threshold', value=0.5, interactive=True)
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@@ -188,7 +115,7 @@ with gr.Blocks() as demo:
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systemPrompt = gr.Textbox(label='', value=DEFAULT_SYSTEM_PROMPT, interactive=True)
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with gr.Row():
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llmModel = gr.Dropdown(label='models', choices=llm_models_list, value=llm_models_list[0], interactive=True)
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llmModel = gr.Dropdown(label='models', choices=LLM_MODELS, value=LLM_MODELS[0], interactive=True)
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llmTemperature = gr.Number(label='Temperature', value=0.8, interactive=True )
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with gr.Column():
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@@ -206,7 +133,7 @@ with gr.Blocks() as demo:
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isPipelineEnabledCheckbox.change(updateButton, inputs=[isPipelineEnabledCheckbox], outputs=refineTextBtn)
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saveFileCheckbox.change(updateTextbox, inputs=saveFileCheckbox, outputs=filename)
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recognizeBtn.click(recognize, outputs=[recognizedText], inputs=[fastWhisperModel, audioFile, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText])
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recognizeBtn.click(FasterWhisper.recognize, outputs=[recognizedText], inputs=[fastWhisperModel, audioFile, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText])
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# Если пайплайн включен то тогда делаем автоматически
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# автоматический пайплайн
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@@ -223,5 +150,4 @@ with gr.Blocks() as demo:
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outputs=[refinedText, refinedTextMD]
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)
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demo.launch()
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