Update app.py

app.py

@@ -1,85 +1,74 @@
-import streamlit as st
-from transformers import WhisperProcessor, WhisperForConditionalGeneration, RagTokenizer, RagRetriever, RagSequenceForGeneration
-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, trust_remote_code=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
-
-st.title("Audio and Video Transcription & Summarization")
-
-# Audio Upload Section
-st.header("Upload an Audio File")
-audio_file = st.file_uploader("Choose an audio file...", type=["wav", "mp3", "m4a"])
-
-if audio_file is not None:
-    audio_path = os.path.join("/tmp", audio_file.name)
-    with open(audio_path, "wb") as f:
-        f.write(audio_file.getbuffer())
-    
-    st.audio(audio_file)
-    st.write("Transcribing audio...")
-    transcription = transcribe_audio(audio_path)
-    st.write("Transcription:", transcription)
-    
-    st.write("Translating and summarizing...")
-    summary = translate_and_summarize(transcription)
-    st.write("Translated Summary:", summary)
-# Video Upload Section
-st.header("Upload a Video File")
-video_file = st.file_uploader("Choose a video file...", type=["mp4", "mkv", "avi", "mov"])
-
-if video_file is not None:
-    video_path = os.path.join("/tmp", video_file.name)
-    with open(video_path, "wb") as f:
-        f.write(video_file.getbuffer())
-    
-    st.video(video_file)
-    st.write("Extracting audio from video...")
-    audio_path = extract_audio_from_video(video_path, "/tmp/extracted_audio.wav")
-    
-    if audio_path is not None:
-        st.write("Transcribing audio...")
-        transcription = transcribe_audio(audio_path)
-        st.write("Transcription:", transcription)
-        
-        st.write("Translating and summarizing...")
-        summary = translate_and_summarize(transcription)
-        st.write("Translated Summary:", summary)
-    else:
-        st.write("No audio track found in the video file.")
+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()