awacke1 commited on
Commit
093c4ba
1 Parent(s): b81715b

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +14 -19
app.py CHANGED
@@ -3,10 +3,9 @@ import pandas as pd
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  import numpy as np
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  import spacy
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  from spacy import displacy
 
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- from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer
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-
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- def linkify():
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  import pandas as pd
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  import streamlit as st
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  link1 = "https://stackoverflow.com/questions/71641666/hyperlink-in-streamlit-dataframe"
@@ -49,7 +48,6 @@ def bulk_function(filename):
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  print(filename, type(filename))
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  print(filename.name)
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-
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  # check type of input file
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  if filename.name.split(".")[1] == "csv":
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  print("entered")
@@ -65,12 +63,6 @@ def bulk_function(filename):
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  else:
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  return
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-
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- # read csv
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- # even if index given, drop it
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- #df_input = pd.read_csv(filename.name, index_col=False)
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- #print("df_input", df_input)
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-
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  # expect csv format to be in:
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  # 1: ID
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  # 2: Texts
@@ -130,13 +122,16 @@ def bulk_function(filename):
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  # return dataframe for space output
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  return YOUR_FILENAME
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- gr.Interface(bulk_function,
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- inputs=[gr.inputs.File(file_count="single", type="file", label="Upload file", optional=False),],
 
 
 
 
 
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  outputs=[gr.outputs.File(label="Output file")],
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- # examples=[["YOUR_FILENAME.csv"]], # computes, doesn't export df so far
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- #["highlight", "json", "html"],
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- theme="huggingface",
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- title="Emotion Classification from CSV",
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- description="Upload csv file with 2 columns (in order): (a) ID column, (b) text column. Model: https://huggingface.co/j-hartmann/emotion-english-distilroberta-base.",
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- allow_flagging=False,
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- ).launch(debug=True)
 
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  import numpy as np
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  import spacy
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  from spacy import displacy
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+ from transformers import AutoTokenizer,AutoModelForSequenceClassification, Trainer
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+ def linkifyHTML():
 
 
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  import pandas as pd
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  import streamlit as st
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  link1 = "https://stackoverflow.com/questions/71641666/hyperlink-in-streamlit-dataframe"
 
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  print(filename, type(filename))
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  print(filename.name)
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  # check type of input file
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  if filename.name.split(".")[1] == "csv":
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  print("entered")
 
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  else:
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  return
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  # expect csv format to be in:
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  # 1: ID
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  # 2: Texts
 
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  # return dataframe for space output
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  return YOUR_FILENAME
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+ gr.Interface(
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+ bulk_function,
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+ inputs=[gr.inputs.File(file_count="single",
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+ type="file",
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+ label="Upload file",
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+ optional=False),
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+ ],
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  outputs=[gr.outputs.File(label="Output file")],
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+ theme="huggingface",
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+ title="CSV File to Sentence Emotion Classification",
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+ description="Upload csv file with 2 columns (in order): (a) ID column, (b) text column. Model: https://huggingface.co/j-hartmann/emotion-english-distilroberta-base.",
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+ allow_flagging=False,
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+ ).launch(debug=True)