Spaces:
Runtime error
Runtime error
File size: 2,354 Bytes
9e0e237 74811f2 182b923 9e0e237 182b923 9e0e237 182b923 9e0e237 182b923 9e0e237 182b923 9e0e237 182b923 9e0e237 182b923 9e0e237 182b923 9e0e237 182b923 9e0e237 |
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 |
import os
import numpy as np
import gradio as gr
import tensorflow as tf
from sentence_transformers import SentenceTransformer
from huggingface_hub import from_pretrained_keras
# load pre-trained model
emb_model = SentenceTransformer('distiluse-base-multilingual-cased-v2')
EMB_DIM = 512
# load model for first classification with defining classes
feedback_class_model = from_pretrained_keras('vitiugin/loop_feedback')
feedback_labels = ['Request', 'Thanks', 'Question', 'Opinion', 'Concern']
# load model for second classification with defining classes
area_class_model = from_pretrained_keras('vitiugin/loop_area')
area_labels = ['cross-cutting', 'education', 'food security', 'governance', 'health', 'protection', 'shelter', 'wash']
def process(text_1, text_2):
'''
process(str, str) -> Union[List[torch.Tensor], numpy.ndarray, torch.Tensor]
Function encodes texts from to embeddings.
'''
tokenized_text = emb_model.encode([text_1])
tokenized_org = emb_model.encode([text_2])
return tokenized_text, tokenized_org
def feedback_classification(input_text, input_org):
'''
process(str, str) -> Dict, Dict
Function fits texts (fedback and organization title) into predefined classes
'''
train_text, train_org = process(input_text, input_org) #tokenization
# reshaping tensors
X_text = tf.reshape(train_text, [-1, 1, EMB_DIM])
X_org = tf.reshape(train_org, [-1, 1, EMB_DIM])
# getting scores from classification model
feedback_scores = feedback_class_model.predict([X_text, X_org])
area_scores = area_class_model.predict([X_text, X_org])
# create dict with classification-based probabilities
feedback_scores = {feedback_labels[num]: feedback_scores[0][0][num] for num in range(len(feedback_labels))}
area_scores = {area_labels[num]: area_scores[0][0][num] for num in range(len(area_labels))}
return feedback_scores, area_scores
demo = gr.Interface(
fn=feedback_classification, #define type of the app
inputs=[gr.Textbox(placeholder='Enter a feedback text'),
gr.Textbox(placeholder='Enter a title of organization')], # define the interface fields
outputs=['label', 'label'], # output interface
examples=[['Thank you, but next time just send us more fresh vegetables!', 'Red Cross']]) # definition of data example
demo.launch()
|