File size: 4,224 Bytes
4a6159c 7de7bf4 4a6159c 685552c 570b6e4 685552c 4a6159c 685552c 4a6159c 048a702 fb4cce0 4a6159c 685552c 4a6159c 596accd 685552c fb4cce0 7de7bf4 4a6159c 7de7bf4 4a6159c f9949bb 4a6159c 685552c 7de7bf4 1a4b146 048a702 1a4b146 685552c 1a4b146 3f0df44 570b6e4 4a6159c e4c96ff 4a6159c 048a702 f9949bb 048a702 685552c 048a702 2caced7 4a6159c 685552c 4a6159c 1d3978a 685552c 1d3978a 4a6159c 499fe35 e4c96ff 685552c 4a6159c 1d3978a 4a6159c 685552c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
from haystack.nodes import TransformersDocumentClassifier
from haystack.schema import Document
from typing import List, Tuple
import configparser
import logging
from pandas import DataFrame, Series
from utils.preprocessing import processingpipeline
try:
import streamlit as st
except ImportError:
logging.info("Streamlit not installed")
config = configparser.ConfigParser()
try:
config.read_file(open('paramconfig.cfg'))
except Exception:
logging.info("paramconfig file not found")
st.info("Please place the paramconfig file in the same directory as app.py")
@st.cache(allow_output_mutation=True)
def load_sdgClassifier():
"""
loads the document classifier using haystack, where the name/path of model
in HF-hub as string is used to fetch the model object.
1. https://docs.haystack.deepset.ai/reference/document-classifier-api
2. https://docs.haystack.deepset.ai/docs/document_classifier
Return: document classifier model
"""
logging.info("Loading classifier")
doc_classifier_model = config.get('sdg','MODEL')
doc_classifier = TransformersDocumentClassifier(
model_name_or_path=doc_classifier_model,
task="text-classification")
return doc_classifier
@st.cache(allow_output_mutation=True)
def sdg_classification(haystackdoc:List[Document])->Tuple[DataFrame,Series]:
"""
Text-Classification on the list of texts provided. Classifier provides the
most appropriate label for each text. these labels are in terms of if text
belongs to which particular Sustainable Devleopment Goal (SDG).
Params
---------
haystackdoc: List of haystack Documents. The output of Preprocessing Pipeline
contains the list of paragraphs in different format,here the list of
Haystack Documents is used.
Returns
----------
df: Dataframe with two columns['SDG:int', 'text']
x: Series object with the unique SDG covered in the document uploaded and
the number of times it is covered/discussed/count_of_paragraphs.
"""
logging.info("Working on SDG Classification")
threshold = float(config.get('sdg','THRESHOLD'))
classifier = load_sdgClassifier()
results = classifier.predict(haystackdoc)
labels_= [(l.meta['classification']['label'],
l.meta['classification']['score'],l.content,) for l in results]
df = DataFrame(labels_, columns=["SDG","Relevancy","text"])
df = df.sort_values(by="Relevancy", ascending=False).reset_index(drop=True)
df.index += 1
df =df[df['Relevancy']>threshold]
x = df['SDG'].value_counts()
df= df.drop(['Relevancy'], axis = 1)
return df, x
def runSDGPreprocessingPipeline(filePath, fileName)->List[Document]:
"""
creates the pipeline and runs the preprocessing pipeline,
the params for pipeline are fetched from paramconfig
Params
------------
file_name: filename, in case of streamlit application use
st.session_state['filename']
file_path: filepath, in case of streamlit application use
st.session_state['filepath']
Return
--------------
List[Document]: When preprocessing pipeline is run, the output dictionary
has four objects. For the Haysatck implementation of SDG classification we,
need to use the List of Haystack Document, which can be fetched by
key = 'documents' on output.
"""
sdg_processing_pipeline = processingpipeline()
split_by = config.get('sdg','SPLIT_BY')
split_length = int(config.get('sdg','SPLIT_LENGTH'))
split_overlap = int(config.get('sdg','SPLIT_OVERLAP'))
remove_punc = bool(int(config.get('sdg','REMOVE_PUNC')))
output_sdg_pre = sdg_processing_pipeline.run(file_paths = filePath,
params= {"FileConverter": {"file_path": filePath, \
"file_name": fileName},
"UdfPreProcessor": {"removePunc": remove_punc, \
"split_by": split_by, \
"split_length":split_length,\
"split_overlap": split_overlap}})
return output_sdg_pre
|