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Detecting_vaccine_misinformation_with_Sentence_Embeddings

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Detecting_vaccine_misinformation_with_Sentence_Embeddings / backend /inference.py

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	import gzip
	import json
	from collections import Counter

	import pandas as pd
	import numpy as np
	import jax.numpy as jnp
	import tqdm

	from sentence_transformers import util
	from typing import List, Union
	import torch

	from backend.utils import load_model, filter_questions, load_embeddings
	from sklearn.manifold import TSNE

	def cos_sim(a, b):
	return jnp.matmul(a, jnp.transpose(b)) / (jnp.linalg.norm(a) * jnp.linalg.norm(b))


	# We get similarity between embeddings.
	def text_similarity(anchor: str, inputs: List[str], model_name: str, model_dict: dict):
	print(model_name)
	model = load_model(model_name, model_dict)

	# Creating embeddings
	if hasattr(model, 'encode'):
	anchor_emb = model.encode(anchor)[None, :]
	inputs_emb = model.encode(inputs)
	else:
	assert len(model) == 2
	anchor_emb = model[0].encode(anchor)[None, :]
	inputs_emb = model[1].encode(inputs)

	# Obtaining similarity
	similarity = list(jnp.squeeze(cos_sim(anchor_emb, inputs_emb)))

	# Returning a Pandas' dataframe
	d = {'inputs': inputs,
	'score': [round(similarity[i], 3) for i in range(len(similarity))]}
	df = pd.DataFrame(d, columns=['inputs', 'score'])

	return df


	# Search
	def text_search(anchor: str, n_answers: int, model_name: str, model_dict: dict):
	# Proceeding with model
	print(model_name)
	assert model_name == "distilbert_qa"
	model = load_model(model_name, model_dict)

	# Creating embeddings
	query_emb = model.encode(anchor, convert_to_tensor=True)[None, :]

	print("loading embeddings")
	corpus_emb = load_embeddings()

	# Getting hits
	hits = util.semantic_search(query_emb, corpus_emb, score_function=util.dot_score, top_k=n_answers)[0]

	filtered_posts = filter_questions("python")
	print(f"{len(filtered_posts)} posts found with tag: python")

	hits_titles = []
	hits_scores = []
	urls = []
	for hit in hits:
	post = filtered_posts[hit['corpus_id']]
	hits_titles.append(post['title'])
	hits_scores.append("{:.3f}".format(hit['score']))
	urls.append(f"https://stackoverflow.com/q/{post['id']}")

	return hits_titles, hits_scores, urls


	def text_cluster(anchor: str, n_answers: int, model_name: str, model_dict: dict):
	# Proceeding with model
	print(model_name)
	assert model_name == "distilbert_qa"
	model = load_model(model_name, model_dict)

	# Creating embeddings
	query_emb = model.encode(anchor, convert_to_tensor=True)[None, :]

	print("loading embeddings")
	corpus_emb = load_embeddings()

	# Getting hits
	hits = util.semantic_search(query_emb, corpus_emb, score_function=util.dot_score, top_k=n_answers)[0]

	filtered_posts = filter_questions("python")

	hits_dict = [filtered_posts[hit['corpus_id']] for hit in hits]
	hits_dict.append(dict(id = '1', title = anchor, tags = ['']))

	hits_emb = torch.stack([corpus_emb[hit['corpus_id']] for hit in hits])
	hits_emb = torch.cat((hits_emb, query_emb))

	# Dimensionality reduction with t-SNE
	tsne = TSNE(n_components=3, verbose=1, perplexity=15, n_iter=1000)
	tsne_results = tsne.fit_transform(hits_emb.cpu())
	df = pd.DataFrame(hits_dict)
	tags = list(df['tags'])

	counter = Counter(tags[0])
	for i in tags[1:]:
	counter.update(i)

	df_tags = pd.DataFrame(counter.most_common(), columns=['Tag', 'Mentions'])
	most_common_tags = list(df_tags['Tag'])[1:5]

	labels = []

	for tags_list in list(df['tags']):
	for common_tag in most_common_tags:
	if common_tag in tags_list:
	labels.append(common_tag)
	break
	elif common_tag != most_common_tags[-1]:
	continue
	else:
	labels.append('others')

	df['title'] = [post['title'] for post in hits_dict]
	df['labels'] = labels
	df['tsne_x'] = tsne_results[:, 0]
	df['tsne_y'] = tsne_results[:, 1]
	df['tsne_z'] = tsne_results[:, 2]

	df['size'] = [2 for i in range(len(df))]

	# Making the query bigger than the rest of the observations
	df['size'][len(df) - 1] = 10
	df['labels'][len(df) - 1] = 'QUERY'
	import plotly.express as px

	fig = px.scatter_3d(df, x='tsne_x', y='tsne_y', z='tsne_z', color='labels', size='size',
	color_discrete_sequence=px.colors.qualitative.D3, hover_data=[df.title])
	return fig



	# We get similarity between embeddings.
	def tweets_vaccine(anchor: str, model_name: str, model_dict: dict):
	print(model_name)
	model = load_model(model_name, model_dict)

	# Keywords common in disinformation tweets
	keywords = '''abolish big pharma,
	no forced flu shots,
	antivaccine,
	No Forced Vaccines,
	Arrest Bill Gates,
	not mandatory vaccines,
	No Vaccine,
	big pharma mafia,
	No Vaccine For Me,
	big pharma kills,
	no vaccine mandates,
	parents over pharma,
	say no to vaccines,
	stop mandatory vaccination,
	vaccines are poison,
	learn the risk,
	vaccines cause,
	medical freedom,
	vaccines kill,
	medical freedom of choice,
	vaxxed,
	my body my choice,
	vaccines have very dangerous consequences,
	Vaccines harm your organism'''




	# Creating embeddings
	if hasattr(model, 'encode'):
	anchor_emb = model.encode(anchor)[None, :]
	inputs_emb = model.encode(keywords)
	else:
	assert len(model) == 2
	anchor_emb = model[0].encode(anchor)[None, :]
	inputs_emb = model[1].encode(keywords)


	# Obtaining similarity
	similarity = jnp.squeeze(jnp.matmul(anchor_emb, jnp.transpose(inputs_emb)) / (jnp.linalg.norm(anchor_emb) * jnp.linalg.norm(inputs_emb))).tolist()

	# Returning a Pandas' dataframe
	d = dict(tweet = anchor,
	score = [round(similarity, 3)])
	df = pd.DataFrame(d, columns=['tweet', 'score'])


	return df