MJ3128 commited on
Commit
1df4b1a
1 Parent(s): 154ba99

Uploaded Manually due to github not working

Browse files
Files changed (2) hide show
  1. README.md +9 -1
  2. app.py +8 -4
README.md CHANGED
@@ -23,4 +23,12 @@ I will not be using Docker for the purpose of this project so everything will be
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  Milestone-2:
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  The link below will take you to the hugging face space for the sentiment analysis web app
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- https://huggingface.co/spaces/MJ3128/CS-GY-6613-Project
 
 
 
 
 
 
 
 
 
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  Milestone-2:
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  The link below will take you to the hugging face space for the sentiment analysis web app
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+ https://huggingface.co/spaces/MJ3128/CS-GY-6613-Project
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+
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+ Milestone-3:
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+ The same link will take you to the viability page. The sentiment analysis code has be commented out for convenience.
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+ https://huggingface.co/spaces/MJ3128/CS-GY-6613-Project
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+
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+ Milestone-4:
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+ Landing page for the project, contains a video demo. Demo can also be found in the repository.
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+ https://sites.google.com/nyu.edu/mj3128-ai-project/home
app.py CHANGED
@@ -11,8 +11,9 @@ if "score" not in st.session_state:
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  def get_patent_score(pipeline, abstract, claims):
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- abstract_score = pipeline(abstract)
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- claims_score = pipeline(claims)
 
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  abstract_label = abstract_score[0]["label"]
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  claims_label = claims_score[0]["label"]
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  st.session_state.score = "{:.2f}".format(
@@ -21,7 +22,7 @@ def get_patent_score(pipeline, abstract, claims):
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  if abstract_label == claims_label:
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  st.session_state.viability = abstract_label
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  else:
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- if abstract_score[0]["score"] > claims_score[0]["label"]:
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  st.session_state.viability = abstract_label
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  else:
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  st.session_state.viability = claims_label
@@ -29,7 +30,8 @@ def get_patent_score(pipeline, abstract, claims):
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  checkpoint_file = "./checkpoint-3024"
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  model = AutoModelForSequenceClassification.from_pretrained(checkpoint_file)
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- tokenizer = AutoTokenizer.from_pretrained(checkpoint_file)
 
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  pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
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  dataset_dict = load_dataset('HUPD/hupd',
@@ -70,6 +72,8 @@ st.button("Check Viability", on_click=get_patent_score,
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  st.markdown(body="Outcome: {}, Score: {}%".format(
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  st.session_state.viability, st.session_state.score))
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  # Milestone-2
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  # if "sentiment" not in st.session_state:
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  # st.session_state.sentiment = ""
 
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  def get_patent_score(pipeline, abstract, claims):
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+ abstract_score = pipeline(
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+ abstract, pad_to_max_length=True, truncation=True)
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+ claims_score = pipeline(claims, pad_to_max_length=True, truncation=True)
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  abstract_label = abstract_score[0]["label"]
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  claims_label = claims_score[0]["label"]
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  st.session_state.score = "{:.2f}".format(
 
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  if abstract_label == claims_label:
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  st.session_state.viability = abstract_label
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  else:
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+ if abstract_score[0]["score"] > claims_score[0]["score"]:
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  st.session_state.viability = abstract_label
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  else:
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  st.session_state.viability = claims_label
 
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  checkpoint_file = "./checkpoint-3024"
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  model = AutoModelForSequenceClassification.from_pretrained(checkpoint_file)
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+ tokenizer = AutoTokenizer.from_pretrained(
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+ checkpoint_file, pad_to_max_length=True)
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  pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
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  dataset_dict = load_dataset('HUPD/hupd',
 
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  st.markdown(body="Outcome: {}, Score: {}%".format(
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  st.session_state.viability, st.session_state.score))
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+ get_patent_score(pipeline=pipeline, abstract=abstract, claims=claims)
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+
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  # Milestone-2
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  # if "sentiment" not in st.session_state:
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  # st.session_state.sentiment = ""