leadr64 commited on
Commit
ecf02ba
·
1 Parent(s): b815515
Files changed (2) hide show
  1. .env +2 -2
  2. app.py +2 -12
.env CHANGED
@@ -1,4 +1,4 @@
1
- QDRANT_URL=https://ebe79742-e3ac-4d09-a2c6-63946024cc7a.us-east4-0.gcp.cloud.qdrant.io
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- QDRANT_KEY=_NnGLuSMH4Qwv-ancoFh88YvzuR7WbyidAorVOVQ_eMCbPhxTb2TSw
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  AWS_ACCESS_KEY_ID=AKIAWOUASMWP5DM6RZG2
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  AWS_SECRET_ACCESS_KEY=HfD73+MKijEgNlVRAkTEgRuNeivyFeYdrtLUqOmq
 
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+ QDRANT_URL=https://006817a4-0b45-4db8-a4e5-1f916808e19b.us-east4-0.gcp.cloud.qdrant.io:6333
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+ QDRANT_KEY=d9MUWQOMUA7JGBdpoFRtmou-h4Pf-e9uCrr0jMtUfAvvk4osqL_JtA
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  AWS_ACCESS_KEY_ID=AKIAWOUASMWP5DM6RZG2
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  AWS_SECRET_ACCESS_KEY=HfD73+MKijEgNlVRAkTEgRuNeivyFeYdrtLUqOmq
app.py CHANGED
@@ -12,8 +12,6 @@ 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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- # Vérifier les valeurs récupérées
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-
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  try:
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  # Vérifier que les variables sont correctement récupérées
@@ -35,25 +33,18 @@ try:
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  def sound_search(query):
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  try:
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- print(f"[DEBUG] Received query: {query}")
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  text_inputs = processor(text=query, return_tensors="pt")
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- print(f"[DEBUG] Text inputs: {text_inputs}")
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  text_embed = model.get_text_features(**text_inputs)[0]
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- print(f"Text embed: {text_embed}")
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  hits = client.search(
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- collection_name="music_db",
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  query_vector=text_embed.tolist(),
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  limit=max_results,
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  )
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- print(f"Search results: {hits}")
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- print([
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- hit.payload['audio_s3url']
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- for hit in hits])
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  return [
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  gr.Audio(
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- hit.payload['url'],
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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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  ]
@@ -65,7 +56,6 @@ try:
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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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- print(sound_search("fun"))
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  inp.submit(sound_search, inputs=inp, outputs=out)
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  demo.launch()
 
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  QDRANT_URL = os.getenv('QDRANT_URL')
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  QDRANT_KEY = os.getenv('QDRANT_KEY')
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  try:
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  # Vérifier que les variables sont correctement récupérées
 
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  def sound_search(query):
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  try:
 
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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.tolist(),
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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_s3url'],
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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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  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.submit(sound_search, inputs=inp, outputs=out)
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  demo.launch()