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Update main.py
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main.py
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@@ -1,17 +1,20 @@
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import gradio
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import numpy
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import pandas
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import sentence_transformers
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import datasets
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import faiss
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model = sentence_transformers.SentenceTransformer('allenai-specter')
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full_data = datasets.load_dataset("ccm/publications")['train'].to_pandas()
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data
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dimensionality = len(data['embedding'][0])
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index = faiss.IndexFlatL2(dimensionality)
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def search(query, k):
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query = numpy.expand_dims(model.encode(query), axis=0)
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_, I =
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top_five = data.loc[I[0]]
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search_results = ""
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for i in range(k):
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search_results +=
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search_results += '"' + top_five["bibtex"].values[i]["title"] + '" '
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search_results += top_five["bibtex"].values[i]["citation"]
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if top_five["pub_url"].values[i] is not None:
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search_results += " [Paper](" + top_five["pub_url"].values[i] + ")"
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search_results += "\n"
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return search_results
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with gradio.Blocks() as demo:
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with gradio.Group():
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query = gradio.Textbox(placeholder="Enter search terms...", show_label=False, lines=1, max_lines=1)
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import json
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import gradio
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import datasets
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import numpy
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import pandas
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import sentence_transformers
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import faiss
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model = sentence_transformers.SentenceTransformer('allenai-specter')
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full_data = datasets.load_dataset("ccm/publications")['train'].to_pandas()
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filter = ["\"abstract\": null" in json.dumps(bibdict) for bibdict in full_data['bibtex'].values]
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data = full_data[~pandas.Series(filter)]
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data.reset_index(inplace=True)
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dimensionality = len(data['embedding'][0])
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index = faiss.IndexFlatL2(dimensionality)
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def search(query, k):
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query = numpy.expand_dims(model.encode(query), axis=0)
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_, I = index.search(query, k)
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top_five = data.loc[I[0]]
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search_results = ""
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for i in range(k):
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search_results += '## ' + top_five["bibtex"].values[i]["title"] + '\n'
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search_results += top_five["bibtex"].values[i]["citation"]
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if top_five["pub_url"].values[i] is not None:
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search_results += ", [Paper](" + top_five["pub_url"].values[i] + ")"
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search_results += "\t\n```\n"
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search_results += json.dumps(top_five["bibtex"].values[i], indent=4)
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search_results += "\t\n```\n"
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return search_results
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with gradio.Blocks() as demo:
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with gradio.Group():
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query = gradio.Textbox(placeholder="Enter search terms...", show_label=False, lines=1, max_lines=1)
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