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# imports
import gradio as gr
import pandas as pd
import tempfile
import itertools
import torch
import numpy as np
from numpy import dot
from numpy.linalg import norm, multi_dot
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer
# compute dot product of inputs
# summary function - test for single gradio function interfrace
def gr_cosine_similarity(sentence1, sentence2):
# Create class for data preparation
class SimpleDataset:
def __init__(self, tokenized_texts):
self.tokenized_texts = tokenized_texts
def __len__(self):
return len(self.tokenized_texts["input_ids"])
def __getitem__(self, idx):
return {k: v[idx] for k, v in self.tokenized_texts.items()}
# load tokenizer and model, create trainer
model_name = "j-hartmann/emotion-english-distilroberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
trainer = Trainer(model=model)
# sentences in list
lines_s = [sentence1, sentence2]
print(type(sentence1), type(sentence2))
print(sentence1, sentence2)
print(lines_s)
# Tokenize texts and create prediction data set
tokenized_texts = tokenizer(lines_s, truncation=True, padding=True)
pred_dataset = SimpleDataset(tokenized_texts)
# Run predictions -> predict whole df
predictions = trainer.predict(pred_dataset)
# Transform predictions to labels
preds = predictions.predictions.argmax(-1)
labels = pd.Series(preds).map(model.config.id2label)
scores = (np.exp(predictions[0])/np.exp(predictions[0]).sum(-1,keepdims=True)).max(1)
# scores raw
temp = (np.exp(predictions[0])/np.exp(predictions[0]).sum(-1, keepdims=True)).tolist()
# work in progress
# container
anger = []
disgust = []
fear = []
joy = []
neutral = []
sadness = []
surprise = []
print(temp)
# extract scores (as many entries as exist in pred_texts)
for i in range(len(lines_s)):
anger.append(temp[i][0])
disgust.append(temp[i][1])
fear.append(temp[i][2])
joy.append(temp[i][3])
neutral.append(temp[i][4])
sadness.append(temp[i][5])
surprise.append(temp[i][6])
# define both vectors for the dot product
# each include all values for both predictions
v1 = temp[0]
v2 = temp[1]
print(type(v1), type(v2))
# compute dot product of all
dot_product = dot(v1, v2)
# define df
df = pd.DataFrame(list(zip(lines_s,labels, anger, disgust, fear, joy, neutral, sadness, surprise)), columns=['text','label', 'anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'surprise'])
# compute cosine similarity
# is dot product of vectors n / norms 1*..*n vectors
cosine_similarity = dot_product / (norm(v1) * norm(v2))
# return dataframe for space output
return df, cosine_similarity
gr.Interface(gr_cosine_similarity,
[
gr.inputs.Textbox(lines=1, placeholder="This movie always makes me cry..", default="", label="Text 1"),
gr.inputs.Textbox(lines=1, placeholder="Her dog is sad.", default="", label="Text 2"),
#gr.outputs.Textbox(type="auto", label="Cosine similarity"),
],
["dataframe","text"]
).launch(debug=True) |