change prompt

.gitignore

@@ -1,5 +1,5 @@
-.env
+.env
-__pycache__/
+__pycache__/
-outputs/*
+outputs/*
-venv/
+venv/
-
+

app.py

@@ -5,11 +5,13 @@ from config import *
 
 # Подгрузка сервисов
 from services.llm_factory import get_llm_provider
-from services.fasterWhisper import FasterWhisper
+
+
 
 # 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
 
@@ -113,20 +115,13 @@ def main():
         saveFileCheckbox.change(gh.updateTextbox, inputs=saveFileCheckbox, outputs=filename)
         saveFileCheckbox.change(gh.updateTextbox, inputs=saveFileCheckbox, outputs=filenamePdf)
 
-        recognizeBtn.click(
+        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)])
-            gh.handleRecognizeBtn, 
-            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)],
-            outputs=[recognizedText], 
-            )
         
         # Если пайплайн включен то тогда делаем автоматически
         # автоматический пайплайн
         recognizedText.change(
             gh.generateByCondition,
-            inputs=[apiKey, llmProvider, llmModel, systemPrompt, recognizedText, llmTemperature, 
+            inputs=[apiKey, llmProvider, llmModel, systemPrompt, recognizedText, llmTemperature, isPipelineEnabledCheckbox, gr.State("change"), saveFileCheckbox, filename, filenamePdf, gr.State(OUTPUT_PATH)],
-                    isPipelineEnabledCheckbox, gr.State("change"), saveFileCheckbox, filename, filenamePdf, gr.State(OUTPUT_PATH)],
             outputs=[refinedText, refinedTextMD]
         )
         
@@ -136,12 +131,11 @@ def main():
         llmProvider.change(
             gh.update_model_dropdown, 
             inputs=llmProvider, 
-            outputs=[llmModel, apiKey]
+            outputs=llmModel
         )
         refineTextBtn.click(
             gh.generateByCondition,
-            inputs=[apiKey, llmProvider, llmModel, systemPrompt, recognizedText, llmTemperature, 
+            inputs=[apiKey, llmProvider, llmModel, systemPrompt, recognizedText, llmTemperature, isPipelineEnabledCheckbox, gr.State("click"), saveFileCheckbox, filename, filenamePdf, gr.State(OUTPUT_PATH)],
-                    isPipelineEnabledCheckbox, gr.State("click"), saveFileCheckbox, filename, filenamePdf, gr.State(OUTPUT_PATH)],
             outputs=[refinedText, refinedTextMD]
         )
 

config.py

@@ -8,10 +8,7 @@ DEVICES = ['cpu', 'cuda']
 COMPUTE_TYPE = ['auto', 'int8', 'float16', 'float32']
 
 # Стандартный API ключ
-IO_API_KEY=os.getenv('IO_API_KEY')
+DEFAULT_API_KEY=os.getenv('API_KEY')
-GEMINI_API_KEY=os.getenv('GEMINI_API_KEY')
-DEFAULT_API_KEY=IO_API_KEY
-
 
 # Словарь провайдеров и их моделей
 LLM_PROVIDERS = ['io.net', 'Gemini', 'gpt4free']
@@ -49,38 +46,42 @@ 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. 
+DEFAULT_SYSTEM_PROMPT='''
-Your task is to rewrite this unstructured transcript into a clear, logically organized, and detailed lecture summary (lecture notes).  
+You are a smart university student creating easy-to-understand study notes summary of lesson  for a classmate who is a beginner. Your source is a raw text/audio transcript.
 
-Guidelines:
+GOAL: rewrite the information into a clear, structured summary in RUSSIAN.
-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:
+KEY RULES FOR CONTENT:
-   - Remove filler words, repetitions, and irrelevant fragments.  
+1.  **Logical Structure:** Use Markdown headers (#, ##), bullet points, and short paragraphs.
-   - Rewrite incomplete sentences into full, grammatically correct sentences.  
+2.  **No "Water":** Remove filler words. Keep only practical information.
-   - Ensure smooth transitions between topics, making the summary feel continuous and well-connected.  
+3.  **Student Tone:** Write naturally, as if sharing notes with a friend. Avoid robotic phrases like "It is important to note".
 
-3. Depth & Detail: 
+KEY RULES FOR LATEX (CRITICAL FOR PYLATEXENC):
-   - Capture all important concepts, definitions, examples, and explanations from the lecture.  
+1.  **Math Mode:** ANY variable (like t, L, C), number in a formula, or equation MUST be wrapped in dollar signs `$`.
-   - Expand shorthand or fragmented thoughts into full, precise explanations.  
+    *   BAD: i(t) = i_pr + i_sv
-   - Where appropriate, rephrase or clarify confusing passages for better understanding.  
+    *   GOOD: $i(t) = i_{pr} + i_{sv}$
+2.  **Subscripts:** Always use curly braces `{}` for subscripts longer than one character.
+    *   BAD: $i_pr$
+    *   GOOD: $i_{pr}$ (or $i_{пр}$ if using cyrillic)
+3.  **Symbols:** Use standard LaTeX commands for symbols.
+    *   Arrow: use `\to` (e.g., $t \to \infty$).
+    *   Infinity: use `\infty`.
+    *   Multiplication: use `\cdot` or just space.
+4.  **Consistency:** Never leave a mathematical symbol as plain text. If you mention "current i", write "ток $i$".
 
-4. Accuracy:
+EXAMPLE OF DESIRED OUTPUT FORMAT:
-   - Preserve the lecturer’s original meaning, intent, and terminology.  
+# Тема лекции
-   - Avoid adding personal opinions or new information that was not in the lecture.  
+## Основные понятия
+*   **Переходный процесс** — это когда цепь перестраивается с одного режима на другой (например, щелкнули выключателем).
+*   Математически это описывается дифференциальными уравнениями. Порядок уравнения = количеству реактивных элементов ($L$ и $C$).
 
-5. Style:
+## Классический метод
-   - Write in a formal, academic tone suitable for study notes.  
+Решение ищется в виде суммы двух частей:
-   - Aim for readability: concise sentences, but thorough coverage of concepts.  
+$$i(t) = i_{pr} + i_{sv}$$
-   - 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.
+1.  **Принужденная составляющая** ($i_{pr}$) — это режим, который установится в будущем, когда все успокоится ($t \to \infty$).
-Use only russian language!
+2.  **Свободная составляющая** ($i_{sv}$) — это то, что происходит "само по себе" из-за энергии, запасенной в $L$ и $C$.
-USE LATEX IN DOLLAR SIGN ($)!
+
-EXTRA BIG LENTH OF CONSPECT!
+***
-MAKE AS LONG AS POSIBLE AND AS BE GOOD!
+STRICTLY FOLLOW THESE FORMATTING RULES. OUTPUT IN RUSSIAN.
 '''
-

handlers/glueAudio.py

@@ -1,61 +1,11 @@
-import subprocess
+from pydub import AudioSegment
-from pathlib import Path
-
 
 class GlueAudio():
-    def glue(self, audio_files: list, output_path: str, output_filename: str) -> Path:
+    def glue(self, audioFiles):
-        """
+        glued = AudioSegment.empty()
-        Склеивает аудиофайлы с помощью FFmpeg, используя промежуточный список файлов.
-        Этот метод чрезвычайно эффективен по памяти и скорости.
 
-        Args:
+        for audioFile in audioFiles:
-            audio_files (list): Список путей к исходным аудиофайлам.
+            audio = AudioSegment.from_file(audioFile)
-            output_path (str): Директория для сохранения итогового файла.
+            glued += audio
-            output_filename (str): Имя итогового склеенного файла.
 
-        Returns:
+        return glued
-            Path: Путь к созданному склеенному файлу.
-        """
-        output_dir = Path(output_path)
-        output_dir.mkdir(parents=True, exist_ok=True)
-        final_audio_path = output_dir / output_filename
-
-        if not audio_files:
-            raise ValueError("Список аудиофайлов для склейки пуст.")
-
-        # 1. Формируем часть команды с входными файлами (-i file1 -i file2 ...)
-        input_args = []
-        for file_path in audio_files:
-            input_args.extend(['-i', str(Path(file_path).resolve())])
-
-        # 2. Формируем строку для filter_complex
-        num_files = len(audio_files)
-        stream_specifiers = "".join([f"[{i}:a]" for i in range(num_files)])
-        filter_complex_str = f"{stream_specifiers}concat=n={num_files}:v=0:a=1[outa]"
-
-        # 3. Собираем полную команду
-        command = [
-            'ffmpeg',
-            *input_args,  # Распаковываем список входных файлов
-            '-filter_complex', filter_complex_str,
-            '-map', '[outa]',
-            '-c:a', 'libmp3lame',
-            '-q:a', '2',
-            str(final_audio_path),
-            '-y'
-        ]
-
-        try:
-            # 4. Выполняем команду
-            print(f"Выполнение команды FFmpeg: {' '.join(command)}")
-            subprocess.run(command, check=True, capture_output=True, text=True)
-            print("FFmpeg успешно завершил склейку.")
-
-        except FileNotFoundError:
-            raise FileNotFoundError("FFmpeg не найден. Убедитесь, что он установлен и доступен в системной переменной PATH.")
-        except subprocess.CalledProcessError as e:
-            print("Ошибка при выполнении FFmpeg!")
-            print("Stderr:", e.stderr)
-            raise RuntimeError(f"Ошибка FFmpeg при склейке файлов: {e.stderr}")
-        
-        return final_audio_path

handlers/gradioHandler.py

@@ -1,6 +1,6 @@
 from config import LLM_MODELS # Импортируем словарь моделей
 import gradio as gr 
-from config import GEMINI_API_KEY, IO_API_KEY
+
 
 class GradioHandlers:
     def __init__(self, llm_factory, ConvertMdToPdf, FileHandlers, FasterWhisper, GlueAudio):
@@ -9,39 +9,23 @@ class GradioHandlers:
         self.ga = GlueAudio()
         self.ConvertMdToPdf = ConvertMdToPdf()
         self.FasterWhisper = FasterWhisper()
-        self.llm_factory = llm_factory
+        self.llm_factory = llm_factory # Сохраняем фабрику
 
-    def handleRecognizeBtn(
+    def handleRecognizeBtn(self, audioFiles, model, device, compute_type, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText, filename, outPath):
-            self, audioFiles, model, device, compute_type, beamSize, vadFilter, 
+        audioFile = self.ga.glue(audioFiles)
-            minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, 
+        file = self.fh.saveFile(filename, audioFile, outPath)
-            wordTimestamps, noSpeechThreshold, conditionOnPreviousText, filename, outPath
-            ):
-        try:
-            glued_audio_path = self.ga.glue(
-                audio_files=[f.name for f in audioFiles], # Передаем список путей
-                output_path=outPath,
-                output_filename=filename
-            )
-        except (FileNotFoundError, RuntimeError) as e:
-            # Если FFmpeg не найден или произошла ошибка, сообщаем пользователю
-            gr.Warning(str(e))
-            return "" # Возвращаем пустую строку в текстовое поле
 
-        # Передаем путь к склеенному файлу в FasterWhisper
+        return self.FasterWhisper.recognize(model, device, compute_type, file, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText)
-        return self.FasterWhisper.recognize(model, device, compute_type, str(glued_audio_path), beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText)
 
     # Функция улучшения текста
-    def generateByCondition(self, api_key, llm_provider, 
+    def generateByCondition(self, api_key, llm_provider, llm_model, system_prompt, recognized_text, llm_temperature, is_pipeline_enabled, trigger, isSaveFile, filename, filenamePdf, output_path):
-                            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
-            gr.Warning(str(e))
+            self.gr.Warning(str(e))
-            return gr.skip(), gr.skip()
+            return self.gr.skip(), self.gr.skip()
 
         def process():
             result, md = provider.generate(llm_model, system_prompt, recognized_text, llm_temperature)
@@ -69,9 +53,7 @@ class GradioHandlers:
         default_value = models[0] if models else None
         
         # Возвращаем обновленный компонент. Используем 'gr' напрямую.
-        if provider == 'io.net': return gr.update(choices=models, value=default_value), gr.update(label='API key (required for io.net, Gemini)', value=IO_API_KEY, interactive=True)
+        return gr.update(choices=models, value=default_value)
-        if provider == 'Gemini': return gr.update(choices=models, value=default_value), gr.update(label='API key (required for Oio.net, Gemini)', value=GEMINI_API_KEY, interactive=True)
-        if provider == 'gpt4free': return gr.update(choices=models, value=default_value), gr.update(label='API key (required for Oio.net, Gemini)', value="", interactive=True)
 
      # Функция для динамического обновления кнопки
     def updateButton(self, isChecked):

requirements.txt

@@ -100,118 +100,3 @@ uvicorn==0.35.0
 webencodings==0.5.1
 websockets==15.0.1
 zopfli==0.2.3.post1
-aiofiles==24.1.0
-aiohappyeyeballs==2.6.1
-aiohttp==3.13.0
-aiosignal==1.4.0
-annotated-types==0.7.0
-anyio==4.10.0
-attrs==25.4.0
-audioop-lts==0.2.2
-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
-frozenlist==1.8.0
-fsspec==2025.9.0
-future==1.0.0
-g4f==0.6.3.5
-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
-multidict==6.7.0
-nest-asyncio==1.6.0
-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
-propcache==0.4.0
-protobuf==6.32.0
-pycparser==2.22
-pycryptodome==3.23.0
-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
-setuptools==80.9.0
-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
-yarl==1.22.0
-zopfli==0.2.3.post1

services/fasterWhisper.py

@@ -1,11 +1,7 @@
 from faster_whisper import WhisperModel
 
 class FasterWhisper:
-    def recognize(self, model, device, compute_type, 
+    def recognize(self, model, device, compute_type, audioFile, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText):
-                  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
@@ -26,7 +22,6 @@ class FasterWhisper:
 
         for seg in segments:
             text += f"[{self.format_timestamp(seg.start)} -> {self.format_timestamp(seg.end)}] {seg.text}" + '\n'
-            print(f"[{self.format_timestamp(seg.start)} -> {self.format_timestamp(seg.end)}] {seg.text}")
         
         return text
 

test.py

@@ -1,19 +0,0 @@
-from handlers.convertMdToPdf import ConvertMdToPdf
-import re
-# Создаем экземпляр класса
-converter = ConvertMdToPdf()
-
-# Тестовые примеры дробей
-test_cases = [
-    r"$\frac{1}{2}$",          # простая дробь
-    r"$\dfrac{3}{4}$",         # дробь с displaystyle
-    r"$\frac{a}{b} + \frac{c}{d}$",  # сложение дробей
-    r"$$\frac{x^2}{y^3}$$",    # блочная дробь
-    r"$\frac{\partial f}{\partial x}$"  # частная производная
-]
-
-for latex in test_cases:
-    result = converter.replace_math(re.search(r'\$\$(.*?)\$\$|\$(.*?)\$', latex))
-    print(f"Input: {latex}")
-    print(f"Output: {result}")
-    print("---")