Update app.py

app.py

@@ -6,11 +6,6 @@ import librosa
 from moviepy.editor import VideoFileClip
 import os
 import tempfile
-import logging
-
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
 
 # Load Whisper base model and processor
 whisper_model_name = "openai/whisper-base"
@@ -20,26 +15,8 @@ whisper_model = WhisperForConditionalGeneration.from_pretrained(whisper_model_na
 # Load RAG sequence model and tokenizer
 rag_model_name = "facebook/rag-sequence-nq"
 rag_tokenizer = RagTokenizer.from_pretrained(rag_model_name)
-
-# Try to load RagRetriever with trust_remote_code=True
-try:
-    rag_retriever = RagRetriever.from_pretrained(
-        rag_model_name,
-        index_name="exact",
-        use_dummy_dataset=True,
-        dataset_path=local_dataset_path,
-        trust_remote_code=True
-    )
-    logger.info("Successfully loaded RagRetriever with trust_remote_code=True")
-except ValueError as e:
-    logger.error(f"Error loading RagRetriever: {e}")
-    rag_retriever = None
-
-if rag_retriever is not None:
-    rag_model = RagSequenceForGeneration.from_pretrained(rag_model_name, retriever=rag_retriever)
-else:
-    logger.error("RagRetriever is not available, unable to proceed with loading RAG model.")
-    st.error("RagRetriever is not available, unable to proceed with loading RAG model.")
+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)
@@ -50,8 +27,6 @@ def transcribe_audio(audio_path, language="ru"):
     return transcription
 
 def translate_and_summarize(text):
-    if rag_retriever is None:
-        return ["Translation and summarization feature is not available due to RAG retriever loading issue."]
     inputs = rag_tokenizer(text, return_tensors="pt")
     input_ids = inputs["input_ids"]
     attention_mask = inputs["attention_mask"]