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Runtime error
import numpy as np | |
img_vec = np.array([[0.001183745, 0.0006474623, 0.9962907, 1.0715525e-05, 0.00025993327, 0.0016074692], [1.0901107e-06, 0.9997192, 0.00015559935, 6.622048e-05, 4.5755154e-05, 1.2046643e-05], [6.780453e-05, 0.9600331, 0.00020796066, 0.00074749015, 0.0033559885, 0.03558765], [9.2790346e-05, 6.0882794e-06, 1.5991038e-05, 1.153297e-05, 0.9985512, 0.0013223367], [0.00064366346, 0.0011190572, 0.9874978, 4.3241245e-05, 0.00935937, 0.0013369184], [5.2049214e-05, 1.2801657e-06, 2.9498646e-05, 1.7153299e-06, 2.559748e-05, 0.99988985], [0.0011970395, 9.731338e-06, 2.1237669e-05, 2.5094421e-05, 0.9977976, 0.000949231], [3.6162317e-06, 0.99877614, 2.023129e-05, 4.7237274e-05, 0.0011406511, 1.2184895e-05], [1.0230211e-05, 1.4835642e-06, 1.3025513e-05, 2.512241e-06, 0.003777029, 0.9961958], [0.0008493654, 1.1132153e-05, 0.00043108794, 1.4500493e-05, 0.00025561644, 0.9984382], [0.0019356995, 0.00017562874, 2.8180919e-05, 0.9975648, 4.8310343e-05, 0.00024738634], [0.0011779362, 0.00065545057, 0.99803513, 1.6797849e-06, 0.00010095722, 2.8904156e-05], [9.089741e-05, 0.978512, 0.005697065, 0.0005912572, 0.0011946606, 0.0139141055], [0.0005574316, 1.5321544e-05, 0.998833, 1.0735541e-06, 1.3806449e-05, 0.00057943986], [3.3686506e-06, 0.9970419, 0.0023412593, 5.4328397e-05, 0.0005123902, 4.668524e-05], [0.00033546498, 7.646274e-06, 0.00027485247, 3.023207e-06, 0.00021423706, 0.9991648], [1.8367424e-05, 4.2896054e-06, 0.000106551015, 4.9463824e-06, 0.0002757381, 0.9995901], [0.9992767, 1.0295089e-05, 0.0005470521, 6.5369866e-05, 2.390274e-05, 7.664138e-05], [0.9813146, 2.0880283e-05, 0.00026747925, 5.134113e-05, 0.00017709624, 0.018168535], [0.000114715054, 3.5882879e-06, 6.4218184e-05, 9.025374e-07, 2.9348028e-05, 0.99978715], [8.362681e-05, 3.8040469e-06, 3.1611694e-06, 5.5942483e-06, 2.07715e-05, 0.99988306], [2.381162e-05, 5.388778e-06, 0.9998869, 3.7252593e-07, 5.261669e-06, 7.830333e-05], [2.0016892e-06, 5.1701e-05, 3.349846e-06, 3.764534e-06, 3.08445e-07, 0.99993885], 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[4.482766e-06, 6.223699e-06, 3.5390046e-06, 4.342299e-06, 0.99970275, 0.00027860425], [3.9287097e-05, 0.0020286934, 0.0008471617, 8.362652e-05, 0.9968947, 0.0001063732], [0.0129518965, 0.00033974875, 0.08617414, 0.00016099395, 0.69730043, 0.20307274], [2.6996162e-05, 1.084152e-05, 0.00022171428, 1.6419244e-06, 0.9996277, 0.000110937355], [5.8929345e-05, 6.492622e-05, 0.99986804, 3.7554253e-06, 6.2047457e-07, 3.7039154e-06], [0.00014001715, 0.00014139229, 0.90270936, 4.0953986e-05, 0.0012192992, 0.095749], [5.321392e-05, 0.005229926, 0.9947114, 8.4837706e-07, 1.0425964e-06, 3.5788794e-06], [0.5962114, 8.891845e-05, 0.00045324978, 0.013933459, 0.018822728, 0.37049025], [0.01565143, 0.16958201, 0.010636462, 0.13116585, 0.002017021, 0.6709472], [2.6996162e-05, 1.084152e-05, 0.00022171428, 1.6419244e-06, 0.9996277, 0.000110937355], [0.08493126, 0.00039129914, 0.27819133, 0.0005301628, 0.002267074, 0.6336889], [4.1572894e-05, 1.0464674e-05, 5.7111407e-05, 4.235746e-06, 0.9998714, 1.5242573e-05], [2.133291e-06, 6.2130794e-06, 6.4672317e-06, 1.1287385e-06, 0.9999156, 6.8471454e-05], [1.6995278e-06, 2.342457e-05, 1.42225235e-05, 4.236806e-07, 0.9999294, 3.0858777e-05], [0.0001755997, 5.640895e-05, 0.0018053413, 4.406857e-06, 0.9962534, 0.0017048786], [2.035718e-06, 1.3238883e-07, 7.22156e-07, 5.645577e-07, 0.9999943, 2.2428483e-06], [0.04246518, 0.00010571361, 0.56788075, 0.00022048036, 0.0035982258, 0.38572967], [0.001647752, 0.0002518864, 0.98668367, 0.00014124898, 0.0012370957, 0.010038363], [0.00042718378, 1.0114699e-05, 4.4205146e-05, 3.138485e-05, 0.00033253324, 0.9991547]]) | |
import gradio as gr | |
from transformers import BertTokenizer, BertForSequenceClassification | |
import re | |
import torch | |
import cv2 | |
import random | |
from sklearn.metrics.pairwise import cosine_similarity | |
tokenizer = BertTokenizer.from_pretrained(r'./model') | |
model = BertForSequenceClassification.from_pretrained(r'./model', num_labels = 6) | |
labels = ['伤感(sadness)', '快乐(happiness)', '励志(inspiration)', '宣泄(catharsis)', '平静(placid)', '感人(touching)'] | |
def preprocess(temp): | |
temp = re.sub(u"\n\n", "\n", temp) | |
temp = re.sub(u"(^\n)|(\n$)", "", temp) | |
temp = re.sub(u",", ",", temp) | |
temp = re.sub(u"\?", "?", temp) | |
temp = re.sub(u"!", "!", temp) | |
temp = re.sub(u"\.", "。", temp) | |
temp = re.sub('[^\u4e00-\u9fa5,。?!\n]+', '', temp) | |
temp = re.sub(u"\n", ",", temp) | |
for _ in range(int(len(temp) / 2)): | |
temp = re.sub(u",,|!!|??|。。", ",", temp) | |
temp = re.sub(u",!|!,", "!", temp) | |
temp = re.sub(u",?|?,", "?", temp) | |
temp = re.sub(u",。|。,", "。", temp) | |
temp = temp.strip(',') | |
return temp | |
def match_img(txt_vec): | |
sim_matrix = [] | |
for vec in img_vec: | |
# vec = vec.reshape(-1, 1) | |
# txt_vec = txt_vec.reshape(-1, 1) | |
sim = cosine_similarity([list(txt_vec)], [list(vec)])[0][0] | |
print(sim) | |
sim_matrix.append(sim) | |
sim_matrix = np.array(sim_matrix) | |
rank_idx = np.argsort(-sim_matrix) | |
rd = random.randint(0,4) | |
img_idx = rank_idx[rd] | |
print(img_idx) | |
if img_idx in [189, 205]: | |
img_idx = 128 | |
return img_idx | |
def classify_text(input_chinese_lyrics): | |
# print(inp) | |
inp = preprocess(input_chinese_lyrics) | |
# print(inp) | |
inp = tokenizer(inp, padding=True, max_length=512, truncation=True, return_tensors="pt") | |
# print(inp) | |
with torch.no_grad(): | |
logits = model(**inp).logits | |
# print(logits) | |
# print(logits.shape) | |
# logits = logits.argsort().squeeze(0) | |
logits = torch.nn.Softmax(dim=1)(logits).squeeze(0).cpu().numpy() | |
img_idx = match_img(logits) | |
img_path = r'./imgs/' + str(img_idx) +'.png' | |
img_output = cv2.imread(img_path) | |
img_output = cv2.cvtColor(img_output,cv2.COLOR_BGR2RGB) | |
# print(logits) | |
#return {labels[i]: float(logits[i].item()) for i in range(len(labels))} | |
return [{labels[i]: float(logits[i].item()) for i in range(len(labels))}, img_output] | |
gr.Interface( | |
classify_text, | |
# gr.inputs.Image(), | |
gr.inputs.Textbox(lines=5,default=""), | |
outputs = ['label','image'] | |
# inputs='image', | |
# outputs='label', | |
# examples=[["images/cheetah1.jpg"], ["images/lion.jpg"]], | |
).launch(debug=True) |