Petra Vidnerova commited on
Commit
38bfbd5
·
1 Parent(s): 5642ff7

before sleep

Browse files
Files changed (3) hide show
  1. README.md +1 -1
  2. app.py +5 -2
  3. utils.py +2 -0
README.md CHANGED
@@ -8,7 +8,7 @@ sdk_version: 6.5.1
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  app_file: app.py
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  pinned: false
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  license: mit
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- short_description: simple app for the purpose of novelty challange
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  ---
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  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
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  app_file: app.py
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  pinned: false
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  license: mit
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+ short_description: simple app for the purpose of novelty challenge
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  ---
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  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py CHANGED
@@ -114,12 +114,15 @@ def process_id(id_number, session_id):
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  ref_embeddings = result
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  yield "Calculating the final score...", api_data_display, None, None
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- score = calculate_score(paper_embedding, ref_embeddings)
 
 
 
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  time.sleep(0.5)
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  result_message = f"🎉 Processing complete! Score calculated successfully."
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- yield result_message, api_data_display, score, None
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  # Create Gradio interface
 
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  ref_embeddings = result
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  yield "Calculating the final score...", api_data_display, None, None
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+ score1 = calculate_score(paper_embedding, ref_embeddings)
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+ score2 = calculate_score(None, ref_embeddings)
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+ score = (score1 * score2) / (score1 + score2)
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+ normalized_score = (score - 0.01) / (0.1 - 0.01) # todo adjust min/max based on real data
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  time.sleep(0.5)
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  result_message = f"🎉 Processing complete! Score calculated successfully."
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+ yield result_message, api_data_display, score, normalized_score
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  # Create Gradio interface
utils.py CHANGED
@@ -80,6 +80,8 @@ def create_abstract(abstract_index):
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  def calculate_score(paper_embedding, ref_embeddings):
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  if ref_embeddings.shape[0] == 0:
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  return 0.0
 
 
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  similarities = torch.nn.functional.cosine_similarity(
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  paper_embedding,
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  ref_embeddings,
 
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  def calculate_score(paper_embedding, ref_embeddings):
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  if ref_embeddings.shape[0] == 0:
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  return 0.0
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+ if paper_embedding is None:
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+ paper_embedding = ref_embeddings.mean(axis=0).unsqueeze(0)
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  similarities = torch.nn.functional.cosine_similarity(
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  paper_embedding,
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  ref_embeddings,