Update app.py

app.py

@@ -5,68 +5,68 @@ import torch
 from utils import convert_segments_object_to_text, check_password
 from gigiachat_requests import get_access_token, get_completion_from_gigachat
 
-    
-st.title('Audio Transcription App')
-st.sidebar.title("Settings")
-# Sidebar inputs
-device = st.sidebar.selectbox("Device", ["cpu", "cuda"], index=1)
-batch_size = st.sidebar.number_input("Batch Size", min_value=1, value=16)
-compute_type = st.sidebar.selectbox("Compute Type", ["float16", "int8"], index=0)
+if check_password():    
+    st.title('Audio Transcription App')
+    st.sidebar.title("Settings")
+    # Sidebar inputs
+    device = st.sidebar.selectbox("Device", ["cpu", "cuda"], index=1)
+    batch_size = st.sidebar.number_input("Batch Size", min_value=1, value=16)
+    compute_type = st.sidebar.selectbox("Compute Type", ["float16", "int8"], index=0)
 
-initial_giga_base_prompt = os.getenv('GIGA_BASE_PROMPT')
-initial_giga_processing_prompt = os.getenv('GIGA_PROCCESS_PROMPT')
+    initial_giga_base_prompt = os.getenv('GIGA_BASE_PROMPT')
+    initial_giga_processing_prompt = os.getenv('GIGA_PROCCESS_PROMPT')
 
-giga_base_prompt = st.sidebar.text_area("Промпт ГигаЧата для резюмирования", value=initial_giga_base_prompt)
-giga_max_tokens = st.sidebar.number_input("Максимальное количество токенов при резюмировании", min_value=1, value=1024)
+    giga_base_prompt = st.sidebar.text_area("Промпт ГигаЧата для резюмирования", value=initial_giga_base_prompt)
+    giga_max_tokens = st.sidebar.number_input("Максимальное количество токенов при резюмировании", min_value=1, value=1024)
 
-enable_summarization = st.sidebar.checkbox("Добавить обработку транскрибации", value=False)
-giga_processing_prompt = st.sidebar.text_area("Промпт ГигаЧата для обработки транскрибации", value=initial_giga_processing_prompt)
+    enable_summarization = st.sidebar.checkbox("Добавить обработку транскрибации", value=False)
+    giga_processing_prompt = st.sidebar.text_area("Промпт ГигаЧата для обработки транскрибации", value=initial_giga_processing_prompt)
 
-ACCESS_TOKEN = st.secrets["HF_TOKEN"]
+    ACCESS_TOKEN = st.secrets["HF_TOKEN"]
 
-uploaded_file = st.file_uploader("Загрузите аудиофайл", type=["mp4", "wav", "m4a"])
+    uploaded_file = st.file_uploader("Загрузите аудиофайл", type=["mp4", "wav", "m4a"])
 
-if uploaded_file is not None:
-    st.audio(uploaded_file)
-    file_extension = uploaded_file.name.split(".")[-1]  # Получаем расширение файла
-    temp_file_path = f"temp_file.{file_extension}"  # Создаем временное имя файла с правильным расширением
+    if uploaded_file is not None:
+        st.audio(uploaded_file)
+        file_extension = uploaded_file.name.split(".")[-1]  # Получаем расширение файла
+        temp_file_path = f"temp_file.{file_extension}"  # Создаем временное имя файла с правильным расширением
     
-    with open(temp_file_path, "wb") as f:
-        f.write(uploaded_file.getbuffer())
+        with open(temp_file_path, "wb") as f:
+            f.write(uploaded_file.getbuffer())
     
-    with st.spinner('Транскрибируем...'):
-        # Load model
-        model = whisperx.load_model(os.getenv('WHISPER_MODEL_SIZE'), device, compute_type=compute_type)
-        # Load and transcribe audio
-        audio = whisperx.load_audio(temp_file_path)
-        result = model.transcribe(audio, batch_size=batch_size, language="ru")
-        print('Transcribed, now aligning')
+        with st.spinner('Транскрибируем...'):
+            # Load model
+            model = whisperx.load_model(os.getenv('WHISPER_MODEL_SIZE'), device, compute_type=compute_type)
+            # Load and transcribe audio
+            audio = whisperx.load_audio(temp_file_path)
+            result = model.transcribe(audio, batch_size=batch_size, language="ru")
+            print('Transcribed, now aligning')
         
-        model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
-        result = whisperx.align(result["segments"], model_a, metadata, audio, device, return_char_alignments=False)
-        print('Aligned, now diarizing')
+            model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
+            result = whisperx.align(result["segments"], model_a, metadata, audio, device, return_char_alignments=False)
+            print('Aligned, now diarizing')
         
-        diarize_model = whisperx.DiarizationPipeline(use_auth_token=st.secrets["HF_TOKEN"], device=device)
-        diarize_segments = diarize_model(audio)
-        result_diar = whisperx.assign_word_speakers(diarize_segments, result)
+            diarize_model = whisperx.DiarizationPipeline(use_auth_token=st.secrets["HF_TOKEN"], device=device)
+            diarize_segments = diarize_model(audio)
+            result_diar = whisperx.assign_word_speakers(diarize_segments, result)
         
-    st.write("Результат транскрибации:")
-    transcript = convert_segments_object_to_text(result_diar)
-    st.text(transcript)
+        st.write("Результат транскрибации:")
+        transcript = convert_segments_object_to_text(result_diar)
+        st.text(transcript)
 
-    access_token = get_access_token()
+        access_token = get_access_token()
     
-    if (enable_summarization):
-        with st.spinner('Обрабатываем транскрибацию...'):
-            transcript = get_completion_from_gigachat(giga_processing_prompt + transcript, 32768, access_token)
+        if (enable_summarization):
+            with st.spinner('Обрабатываем транскрибацию...'):
+                transcript = get_completion_from_gigachat(giga_processing_prompt + transcript, 32768, access_token)
 
-            st.write("Результат обработки:")
-            st.text(transcript)
+                st.write("Результат обработки:")
+                st.text(transcript)
 
         
     
-    with st.spinner('Резюмируем...'):
-        summary_answer = get_completion_from_gigachat(giga_base_prompt + transcript, giga_max_tokens, access_token)
+        with st.spinner('Резюмируем...'):
+            summary_answer = get_completion_from_gigachat(giga_base_prompt + transcript, giga_max_tokens, access_token)
         
-        st.write("Результат резюмирования:")
-        st.text(summary_answer)
+            st.write("Результат резюмирования:")
+            st.text(summary_answer)