Delete app.py

app.py

@@ -1,74 +0,0 @@
-import gradio as gr
-import torch
-import soundfile as sf
-import librosa
-from moviepy.editor import VideoFileClip
-import os
-
-# Load Whisper base model and processor
-whisper_model_name = "openai/whisper-base"
-whisper_processor = WhisperProcessor.from_pretrained(whisper_model_name)
-whisper_model = WhisperForConditionalGeneration.from_pretrained(whisper_model_name)
-
-# Load RAG sequence model and tokenizer
-rag_model_name = "facebook/rag-sequence-nq"
-rag_tokenizer = RagTokenizer.from_pretrained(rag_model_name)
-rag_retriever = RagRetriever.from_pretrained(rag_model_name, index_name="exact", use_dummy_dataset=True)
-rag_model = RagSequenceForGeneration.from_pretrained(rag_model_name, retriever=rag_retriever)
-
-def transcribe_audio(audio_path, language="ru"):
-    speech, rate = librosa.load(audio_path, sr=16000)
-    inputs = whisper_processor(speech, return_tensors="pt", sampling_rate=16000)
-    input_features = whisper_processor.feature_extractor(speech, return_tensors="pt", sampling_rate=16000).input_features
-    predicted_ids = whisper_model.generate(input_features, forced_decoder_ids=whisper_processor.get_decoder_prompt_ids(language=language, task="translate"))
-    transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
-    return transcription
-
-def translate_and_summarize(text):
-    inputs = rag_tokenizer(text, return_tensors="pt")
-    input_ids = inputs["input_ids"]
-    attention_mask = inputs["attention_mask"]
-    outputs = rag_model.generate(input_ids=input_ids, attention_mask=attention_mask)
-    return rag_tokenizer.batch_decode(outputs, skip_special_tokens=True)
-
-def extract_audio_from_video(video_path, output_audio_path):
-    video_clip = VideoFileClip(video_path)
-    audio_clip = video_clip.audio
-    if audio_clip is not None:
-        audio_clip.write_audiofile(output_audio_path)
-        return output_audio_path
-    else:
-        return None
-
-def transcribe_audio_interface(audio_file):
-    audio_path = os.path.join("/tmp", audio_file.name)
-    with open(audio_path, "wb") as f:
-        f.write(audio_file.getvalue())
-    transcription = transcribe_audio(audio_path)
-    return transcription
-
-def summarize_text_interface(text):
-    summary = translate_and_summarize(text)
-    return summary
-
-def summarize_video_interface(video_file):
-    video_path = os.path.join("/tmp", video_file.name)
-    with open(video_path, "wb") as f:
-        f.write(video_file.getvalue())
-    audio_path = extract_audio_from_video(video_path, "/tmp/extracted_audio.wav")
-    if audio_path is not None:
-        transcription = transcribe_audio(audio_path)
-        summary = translate_and_summarize(transcription)
-        return summary
-    else:
-        return "No audio track found in the video file."
-
-# Create interfaces
-audio_transcription_interface = gr.Interface(transcribe_audio_interface, inputs="audio", outputs="text", title="Audio Transcription")
-text_summarization_interface = gr.Interface(summarize_text_interface, inputs="text", outputs="text", title="Text Summarization")
-video_summarization_interface = gr.Interface(summarize_video_interface, inputs="video", outputs="text", title="Video Summarization")
-
-# Launch the interfaces
-audio_transcription_interface.launch()
-text_summarization_interface.launch()
-video_summarization_interface.launch()