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drop_down_l=['Autoencoders',
'Dirichlet Processes',
'Gaussian graphical models',
'Manifold Learning',
'Markov Random Fields',
'Markov chains',
'Mean Field Approximation',
'Message Passing',
'Meta-Learning',
'Mixture Models',
'Naive Bayes',
'Principal Component Analysis',
'ResNet',
'Sampling',
'Sequence to sequence',
'State Space Models',
'Unsupervised learning',
'Variations of GANs',
'Visual QA',
'a* search',
'adversarial search',
'agent-based view of ai',
'automated essay scoring',
'autonomous cars',
'backpropagation',
'bag of words model',
'bayes theorem',
'bayesian network',
'beam search',
'bio text mining',
'character level language models',
'chinese nlp',
'chomsky hierarchy',
'citation networks',
'cky parsing',
'classic parsing methods',
'classification',
'clustering',
'computation theory',
'computational phonology',
'computer vision',
'context free grammar',
'context free grammars',
'context sensitive grammar',
'convolutional neural network',
'convolutional neural networks',
'course introduction',
'deep learning introduction',
'dependency parsing',
'dependency syntax',
'dialog systems',
'dimensionality reduction',
'discourse analysis',
'discourse parsing',
'document ranking',
'document representation',
'dual decomposition',
'dynamic programming',
'edit distance',
'entailment',
'evaluation of dependency parsing',
'evaluation of language modeling',
'evaluation of question answering',
'evaluation of text classification',
'event detection',
'expectation maximization algorithm',
'expert systems',
'feature learning',
'feature selection',
'finite state machines',
'finite state transducers',
'first order logic',
'first-order logic',
'game playing in ai',
'gated recurrent units',
'generative adversarial networks',
'generative and discriminative models',
'gibbs sampling',
'grammar checker',
'graph convolutional networks',
'graph theory',
'graph-based nlp',
'graphical models',
'harmonic functions',
'heuristic search',
'hidden markov models',
'image retrieval',
'information extraction',
'information retrieval',
'informed search',
'kernel function',
'kernels',
'knowledge representation',
'language identification',
'language modeling',
'latent dirichlet allocation',
'latent semantic indexing',
'latent variable models',
'lexical semantics',
'lexicalized parsing',
'lexicography',
'linear algebra',
'linear discriminant analysis',
'linear regression',
'linguistics basics',
'log-linear models',
'logic and logical agents',
'logic and reasoning',
'logistic regression',
'long short term memory networks',
'loss function',
'machine learning resources',
'machine translation',
'machine translation techniques',
'markov chain monte carlo',
'markov decision processes',
'mathematical models',
'matrix factorization',
'matrix multiplication',
'maximum likelihood estimation',
'memory networks',
'monte carlo methods',
'monte carlo tree search',
'morphology and lexicon',
'morphology and semantics in machine translation',
'multi-agent systems',
'multilingual word embedding',
'n-gram models',
'natural language processing intro',
'neural language modeling',
'neural machine translation',
'neural networks',
'neural parsing',
'neural question answering',
'neural summarization',
'neural turing machine',
'newton method',
'nlp and vision',
'nlp for biology',
'nlp for the humanities',
'noisy channel model',
'optimization',
'pagerank',
'parsing',
'parsing evaluation',
'part of speech tagging',
'particle filter',
'parts of speech',
'penn treebank',
'phonetics',
'planning',
'pointer networks',
'predicate logic',
'preprocessing',
'probabilistic context free grammars',
'probabilistic grammars',
'probabilities',
'problem solving and search',
'programming languages',
'propositional logic',
'prosody',
'python',
'question answering',
'random walks',
'random walks and harmonic functions',
'recommendation system',
'recurrent neural networks',
'recursive neural network',
'recursive neural networks',
'regular expressions',
'reinforcement learning',
'relation extraction',
'robotic locomotion',
'robotics',
'scientific article summarization',
'search',
'search engines',
'semantic parsing',
'semantic role labeling',
'semantic similarity',
'semi supervised learning',
'semi-supervised learning',
'sentence boundary recognition',
'sentence representations',
'sentence simplification',
'sentiment analysis',
'seq2seq',
'sequence classification and conditional random fields',
'shallow parsing',
'shift-reduce parsing',
'singular value decomposition',
'social media analysis',
'social network extraction',
'spectral clustering',
'spectral methods',
'speech processing',
'speech signal analysis',
'speech synthesis',
'spelling correction',
'stack lstm',
'statistical machine translation',
'statistical parsing',
'statistical part of speech tagging',
'structured learning',
'structured sparsity',
'summarization evaluation',
'syntax',
'syntax based machine translation',
'syntaxnet',
'text generation',
'text mining',
'text similarity',
'text summarization',
'text to speech generation',
'the ibm models',
'thesaurus-based similarity',
'tokenization',
'toolkits for information retrieval',
'tools for dl',
'topic modeling',
'training neural networks',
'transition based dependency parsing',
'tree adjoining grammar',
'uncertainty',
'variational bayes models',
'vector representations',
'vector semantics',
'weakly-supervised learning',
'word distributions',
'word embedding',
'word embedding variations',
'word segmentation',
'word sense disambiguation',
'wordnet']
domain_list=['ml', 'nlp', 'dl', 'deep-rl', 'pr', 'ai']
import gradio as gr
from py2neo import Graph
def greet(topic_name, lecture_name, URL_link,author,year,domain):
graph = Graph("neo4j+s://e4991d17.databases.neo4j.io", auth=("neo4j", "ohM5LvdhcutqyRQTqk6PpuiDofvpA4o3pXLpC9kb4_g"),routing=True)
qs="MATCH (T:topic)\
Where T.id_topic=$topic\
CREATE (test:lecture{topic:$topic,name:$lecture,id:$URL,year:$year,author:$author,domain:$domain})-[:lecture_of]->(T);"
graph.run(qs,topic=topic_name,lecture=lecture_name,URL=URL_link,author=author,year=year,domain=domain)
return 'Finished Adding'
iface = gr.Interface(
fn=greet,
inputs=[
gr.inputs.Dropdown(drop_down_l, label="Topic belonging"),
'text','text','text','text',
gr.inputs.Radio(domain_list, label="Which Domain this lecture is belonging ?"),
],
outputs=['text'])
iface.launch() |