trying out the old demo

app.py

@@ -1,7 +1,50 @@
 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 = SentenceTransformer('all-MiniLM-L6-v2')
+  questionnaire_filename = q_file.name
+  data_filename = data_file.name
+  print(data_filename)
+
+  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 = ccr_df.drop(columns=["embeddings"])
+
+  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
+
+
 
+iface = gr.Interface(ccr_wrapper, [gr.inputs.File(label="User Data File"), gr.inputs.Textbox(lines=1,label="User Text Column Title", placeholder="Name of the column contatining user's text ..."), gr.inputs.File(label="Questionnaire File"),gr.inputs.Textbox(lines=1, label="Questionnaire Text Column Title",placeholder="Name of the column containing questions") ], "file", examples=[["CCR_clean.csv", "ValuesSurvey","Questionnaire - MFQ2.csv", "question"]])
+iface.launch(debug=True)
 
 
 
@@ -9,5 +52,7 @@ demo = gr.Blocks()
 with demo:
     gr.Markdown('This is the first page for CCR, info goes here!')
     
+    iface = gr.Interface(ccr_wrapper, [gr.inputs.File(label="User Data File"), gr.inputs.Textbox(lines=1,label="User Text Column Title", placeholder="Name of the column contatining user's text ..."), gr.inputs.File(label="Questionnaire File"),gr.inputs.Textbox(lines=1, label="Questionnaire Text Column Title",placeholder="Name of the column containing questions") ], "file", examples=[["CCR_clean.csv", "ValuesSurvey","Questionnaire - MFQ2.csv", "question"]])
+    iface.launch(debug=False)
 
 demo.launch()

requirements.txt

@@ -0,0 +1 @@
+sentence-transformers