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import pickle |
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import os |
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import gradio as gr |
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import gradio as gr |
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import pandas as pd |
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from sentence_transformers import SentenceTransformer, util |
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def encode_column(model, filename, col_name): |
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df = pd.read_csv(filename) |
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df["embedding"] = list(model.encode(df[col_name])) |
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return df |
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def item_level_ccr(data_encoded_df, questionnaire_encoded_df): |
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q_embeddings = questionnaire_encoded_df.embedding |
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d_embeddings = data_encoded_df.embedding |
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similarities = util.pytorch_cos_sim(d_embeddings, q_embeddings) |
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for i in range(1,len(questionnaire_encoded_df)+1): |
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data_encoded_df["sim_item_{}".format(i)] = similarities[:, i-1] |
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return data_encoded_df |
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def ccr_wrapper(data_file, data_col, q_file, q_col, model='all-MiniLM-L6-v2'): |
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""" |
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Returns a Dataframe that is the content of data_file with one additional column for CCR value per question |
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Parameters: |
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data_file (str): path to the file containing user text |
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data_col (str): column that includes user text |
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q_file (str): path to the file containing questionnaires |
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q_col (str): column that includes questions |
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model (str): name of the SBERT model to use for CCR see https://www.sbert.net/docs/pretrained_models.html for full list |
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""" |
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try: |
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model = SentenceTransformer(model) |
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except: |
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print("model name was not included, using all-MiniLM-L6-v2") |
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model = SentenceTransformer('all-MiniLM-L6-v2') |
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questionnaire_filename = q_file.name |
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data_filename = data_file.name |
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q_encoded_df = encode_column(model, questionnaire_filename, q_col) |
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data_encoded_df = encode_column(model, data_filename, data_col) |
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ccr_df = item_level_ccr(data_encoded_df, q_encoded_df) |
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ccr_df.to_csv("ccr_results.csv") |
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return "ccr_results.csv" |
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def read_dataframe(data_file, data_col, q_file, q_col): |
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return data_file.name |
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def single_text_ccr(text, question): |
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model = SentenceTransformer('all-MiniLM-L6-v2') |
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text_embedding = model.encode(text) |
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question_embedding = model.encode(question) |
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return round(util.pytorch_cos_sim(text_embedding, question_embedding).item(),3) |
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with gr.Blocks() as demo: |
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gr.Markdown("""<h1><center>Contextual Construct Representations</center></h1> |
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<h3><center>Ali Omrani and Mohammad Atari</center></h3>""") |
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gr.Markdown("""<br><h4>Play around with your items!</h4>""") |
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with gr.Row(): |
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user_txt = gr.Textbox(label="Input Text", placeholder="Enter your desired text here ...") |
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question = gr.Textbox(label="Question", placeholder="Enter the question text here ...") |
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submit2 = gr.Button("Get CCR for this Text!") |
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submit2.click(single_text_ccr, inputs=[user_txt, question], outputs=gr.Textbox(label="CCR Value")) |
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gr.Markdown("""<br><h4>Or process a whole file!</h4>""") |
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with gr.Row(): |
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model_name = gr.Dropdown(label="Choose the Model", |
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choices=["all-mpnet-base-v2","multi-qa-mpnet-base-dot-v1", "distiluse-base-multilingual-cased-v2", |
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"distiluse-base-multilingual-cased-v1", "paraphrase-MiniLM-L3-v2", "paraphrase-multilingual-MiniLM-L12-v2", |
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"paraphrase-albert-small-v2", "paraphrase-multilingual-mpnet-base-v2", "multi-qa-MiniLM-L6-cos-v1", |
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"all-MiniLM-L6-v2", "multi-qa-distilbert-cos-v1", "all-MiniLM-L12-v2", "all-distilroberta-v1"]) |
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with gr.Row(): |
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with gr.Column(): |
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user_data = gr.File(label="Participant Data File") |
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text_col = gr.Textbox(label="Text Column", placeholder="text column ... ") |
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with gr.Column(): |
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questionnaire_data = gr.File(label="Questionnaire File") |
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q_col = gr.Textbox(label="Question Column", placeholder="questionnaire column ... ") |
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submit = gr.Button("Get CCR!") |
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outputs=gr.File() |
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submit.click(ccr_wrapper, inputs=[user_data, text_col,questionnaire_data,q_col, model_name], outputs=[outputs]) |
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demo.launch(share=True) |
