Callum Leslie
3 years ago
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Apache License
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@ -0,0 +1,58 @@
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import numpy as np
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from transformers import AutoTokenizer, AutoModel
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import torch
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# Mean Pooling - Take attention mask into account for correct averaging
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def mean_pooling(model_output, attention_mask):
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token_embeddings = model_output[0] # First element of model_output contains all token embeddings
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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# This function sort the given threads based on their total similarity with the given keywords
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def sort_by_similarity(thread_objects, keywords):
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# Initialize tokenizer + model.
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tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
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model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
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# Transform the generator to a list of Submission Objects, so we can sort later based on context similarity to
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# keywords
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thread_objects = list(thread_objects)
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threads_sentences = []
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for i, thread in enumerate(thread_objects):
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threads_sentences.append(' '.join([thread.title, thread.selftext]))
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# Threads inference
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encoded_threads = tokenizer(threads_sentences, padding=True, truncation=True, return_tensors='pt')
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with torch.no_grad():
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threads_embeddings = model(**encoded_threads)
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threads_embeddings = mean_pooling(threads_embeddings, encoded_threads['attention_mask'])
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# Keywords inference
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encoded_keywords = tokenizer(keywords, padding=True, truncation=True, return_tensors='pt')
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with torch.no_grad():
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keywords_embeddings = model(**encoded_keywords)
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keywords_embeddings = mean_pooling(keywords_embeddings, encoded_keywords['attention_mask'])
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# Compare every keyword w/ every thread embedding
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threads_embeddings_tensor = torch.tensor(threads_embeddings)
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total_scores = torch.zeros(threads_embeddings_tensor.shape[0])
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cosine_similarity = torch.nn.CosineSimilarity()
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for keyword_embedding in keywords_embeddings:
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keyword_embedding = torch.tensor(keyword_embedding).repeat(threads_embeddings_tensor.shape[0], 1)
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similarity = cosine_similarity(keyword_embedding, threads_embeddings_tensor)
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total_scores += similarity
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similarity_scores, indices = torch.sort(total_scores, descending=True)
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threads_sentences = np.array(threads_sentences)[indices.numpy()]
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thread_objects = np.array(thread_objects)[indices.numpy()].tolist()
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#print('Similarity Thread Ranking')
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#for i, thread in enumerate(thread_objects):
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# print(f'{i}) {threads_sentences[i]} score {similarity_scores[i]}')
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return thread_objects, similarity_scores
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@ -0,0 +1,75 @@
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import re
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import textwrap
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from PIL import Image, ImageDraw, ImageFont
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from rich.progress import track
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from TTS.engine_wrapper import process_text
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def draw_multiple_line_text(image, text, font, text_color, padding, wrap=50):
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"""
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Draw multiline text over given image
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"""
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draw = ImageDraw.Draw(image)
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Fontperm = font.getsize(text)
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image_width, image_height = image.size
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lines = textwrap.wrap(text, width=wrap)
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y = (image_height / 2) - (
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((Fontperm[1] + (len(lines) * padding) / len(lines)) * len(lines)) / 2
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)
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for line in lines:
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line_width, line_height = font.getsize(line)
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draw.text(((image_width - line_width) / 2, y), line, font=font, fill=text_color)
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y += line_height + padding
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# theme=bgcolor,reddit_obj=reddit_object,txtclr=txtcolor
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def imagemaker(theme, reddit_obj: dict, txtclr, padding=5):
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"""
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Render Images for video
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"""
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title = process_text(reddit_obj["thread_title"], False) #TODO if second argument cause any error
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texts = reddit_obj["thread_post"]
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id = re.sub(r"[^\w\s-]", "", reddit_obj["thread_id"])
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tfont = ImageFont.truetype("fonts\\Roboto-Bold.ttf", 27) # for title
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font = ImageFont.truetype(
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"fonts\\Roboto-Regular.ttf", 20
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) # for despcription|comments
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size = (500, 176)
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image = Image.new("RGBA", size, theme)
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draw = ImageDraw.Draw(image)
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# for title
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if len(title) > 40:
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draw_multiple_line_text(image, title, tfont, txtclr, padding, wrap=30)
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else:
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Fontperm = tfont.getsize(title)
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draw.text(
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((image.size[0] - Fontperm[0]) / 2, (image.size[1] - Fontperm[1]) / 2),
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font=tfont,
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text=title,
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) # (image.size[1]/2)-(Fontperm[1]/2)
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image.save(f"assets/temp/{id}/png/title.png")
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# for comment|description
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for idx, text in track(enumerate(texts), "Rendering Image"):#, total=len(texts)):
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image = Image.new("RGBA", size, theme)
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draw = ImageDraw.Draw(image)
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text = process_text(text,False)
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if len(text) > 50:
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draw_multiple_line_text(image, text, font, txtclr, padding)
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||||||
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|
else:
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||||||
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Fontperm = font.getsize(text)
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draw.text(
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((image.size[0] - Fontperm[0]) / 2, (image.size[1] - Fontperm[1]) / 2),
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||||||
|
font=font,
|
||||||
|
text=text,
|
||||||
|
) # (image.size[1]/2)-(Fontperm[1]/2)
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image.save(f"assets/temp/{id}/png/img{idx}.png")
|
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@ -0,0 +1,29 @@
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|
import re
|
||||||
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|
||||||
|
import spacy
|
||||||
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|
||||||
|
from utils.console import print_step
|
||||||
|
from utils.voice import sanitize_text
|
||||||
|
|
||||||
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|
||||||
|
# working good
|
||||||
|
def posttextparser(obj):
|
||||||
|
text = re.sub("\n", "", obj)
|
||||||
|
|
||||||
|
try:
|
||||||
|
nlp = spacy.load('en_core_web_sm')
|
||||||
|
except OSError:
|
||||||
|
print_step("The spacy model can't load. You need to install it with \npython -m spacy download en")
|
||||||
|
exit()
|
||||||
|
|
||||||
|
doc = nlp(text)
|
||||||
|
|
||||||
|
newtext: list = []
|
||||||
|
|
||||||
|
# to check for space str
|
||||||
|
for line in doc.sents:
|
||||||
|
if sanitize_text(line.text):
|
||||||
|
newtext.append(line.text)
|
||||||
|
# print(line)
|
||||||
|
|
||||||
|
return newtext
|
||||||
@ -0,0 +1,37 @@
|
|||||||
|
from PIL import ImageDraw, ImageFont
|
||||||
|
|
||||||
|
|
||||||
|
def create_thumbnail(thumbnail, font_family, font_size, font_color, width, height, title):
|
||||||
|
|
||||||
|
font = ImageFont.truetype(font_family + ".ttf", font_size)
|
||||||
|
Xaxis = width - (width * 0.2) # 20% of the width
|
||||||
|
sizeLetterXaxis = font_size * 0.5 # 50% of the font size
|
||||||
|
XaxisLetterQty = round(Xaxis / sizeLetterXaxis) # Quantity of letters that can fit in the X axis
|
||||||
|
MarginYaxis = (height * 0.12) # 12% of the height
|
||||||
|
MarginXaxis = (width * 0.05) # 5% of the width
|
||||||
|
# 1.1 rem
|
||||||
|
LineHeight = font_size * 1.1
|
||||||
|
# rgb = "255,255,255" transform to list
|
||||||
|
rgb = font_color.split(",")
|
||||||
|
rgb = (int(rgb[0]), int(rgb[1]), int(rgb[2]))
|
||||||
|
|
||||||
|
arrayTitle = []
|
||||||
|
for word in title.split():
|
||||||
|
if len(arrayTitle) == 0:
|
||||||
|
# colocar a primeira palavra no arrayTitl# put the first word in the arrayTitle
|
||||||
|
arrayTitle.append(word)
|
||||||
|
else:
|
||||||
|
# if the size of arrayTitle is less than qtLetters
|
||||||
|
if len(arrayTitle[-1]) + len(word) < XaxisLetterQty:
|
||||||
|
arrayTitle[-1] = arrayTitle[-1] + " " + word
|
||||||
|
else:
|
||||||
|
arrayTitle.append(word)
|
||||||
|
|
||||||
|
draw = ImageDraw.Draw(thumbnail)
|
||||||
|
# loop for put the title in the thumbnail
|
||||||
|
for i in range(0, len(arrayTitle)):
|
||||||
|
# 1.1 rem
|
||||||
|
draw.text((MarginXaxis, MarginYaxis + (LineHeight * i)),
|
||||||
|
arrayTitle[i], rgb, font=font)
|
||||||
|
|
||||||
|
return thumbnail
|
||||||
Loading…
Reference in new issue