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import os |
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import gradio as gr |
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from qdrant_client import QdrantClient |
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from transformers import ClapModel, ClapProcessor |
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from dotenv import load_dotenv |
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import requests |
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load_dotenv() |
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QDRANT_URL = os.getenv('QDRANT_URL') |
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QDRANT_KEY = os.getenv('QDRANT_KEY') |
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print(f"QDRANT_URL: {QDRANT_URL}") |
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print(f"QDRANT_KEY: {QDRANT_KEY}") |
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try: |
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response = requests.get(QDRANT_URL) |
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print(f"Test de la connexion à Qdrant: {response.status_code}") |
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if not QDRANT_URL or not QDRANT_KEY: |
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raise ValueError("Les variables d'environnement QDRANT_URL ou QDRANT_KEY ne sont pas définies") |
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client = QdrantClient(QDRANT_URL, api_key=QDRANT_KEY) |
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print("[INFO] Client created...") |
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print("[INFO] Loading the model...") |
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model_name = "laion/larger_clap_general" |
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model = ClapModel.from_pretrained(model_name) |
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processor = ClapProcessor.from_pretrained(model_name) |
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max_results = 10 |
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def sound_search(query): |
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text_inputs = processor(text=query, return_tensors="pt") |
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text_embed = model.get_text_features(**text_inputs)[0] |
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hits = client.search( |
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collection_name="demo_spaces_db", |
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query_vector=text_embed, |
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limit=max_results, |
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) |
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return [ |
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gr.Audio( |
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hit.payload['audio_path'], |
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label=f"style: {hit.payload['style']} -- score: {hit.score}") |
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for hit in hits |
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] |
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with gr.Blocks() as demo: |
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gr.Markdown("# Sound search database") |
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inp = gr.Textbox(placeholder="What sound are you looking for ?") |
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out = [gr.Audio(label=f"{x}") for x in range(max_results)] |
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inp.change(sound_search, inp, out) |
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demo.launch() |
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except Exception as e: |
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print(f"[ERROR] Failed to create Qdrant client: {e}") |
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