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Update app.py
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app.py
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@@ -4,14 +4,11 @@ from transformers import pipeline
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from sentence_transformers import SentenceTransformer
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from sklearn.metrics.pairwise import cosine_similarity
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import PyPDF2
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from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
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# Load models for TTS from Hugging Face Hub
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models, cfg, task = load_model_ensemble_and_task_from_hf_hub("facebook/fastspeech2-en-ljspeech")
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# Load local models for inference
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stt_model = pipeline("automatic-speech-recognition", model="openai/whisper-base")
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conversation_model = pipeline("text-generation", model="facebook/blenderbot-400M-distill")
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# Load a pre-trained model for vector embeddings
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embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
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@@ -50,10 +47,9 @@ def generate_question(user_input, resume_embeddings):
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# Generate TTS output
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def generate_audio(text):
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"""Convert text to audio using
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return text # Replace with actual waveform generation
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# Gradio interface
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class MockInterview:
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from sentence_transformers import SentenceTransformer
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from sklearn.metrics.pairwise import cosine_similarity
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import PyPDF2
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# Load local models for inference
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stt_model = pipeline("automatic-speech-recognition", model="openai/whisper-base")
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conversation_model = pipeline("text-generation", model="facebook/blenderbot-400M-distill")
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tts_model = pipeline("text-to-speech", model="facebook/fastspeech2-en-ljspeech")
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# Load a pre-trained model for vector embeddings
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embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
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# Generate TTS output
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def generate_audio(text):
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"""Convert text to audio using Hugging Face TTS model."""
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audio_data = tts_model(text, return_tensors=True)["waveform"]
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return audio_data
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# Gradio interface
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class MockInterview:
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