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