Spaces:
Build error
Build error
| 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() |