Gist 5cb8bf884b466e5a35b2c9d12489454b

100%

Gist 5cb8bf884b466e5a35b2c9d12489454b

✓ Published0🌍 Public

Last edited Feb 24, 2020

Created on Feb 24, 2020

This example shows a web service that computes sentence similarity, returning a JSON payload with the target sentence, compared texts, and their cosine similarity scores. The user submits a POST request to the `/sim` endpoint containing a target and list of texts. The application, built with Flask, uses the BERT model to extract `[CLS]` token features from each sentence, then calculates cosine similarity between the target and each comparison. The code explicitly uses `get_futures` for feature extraction and `calc_simlarity` for the similarity computation, all orchestrated through Flask's routing and `jsonify` for the response.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist fdb22d9ec25b96538814323e0609c895

The example is a curated markdown document that catalogs online resources for synthetic media, organized by categories such as synthetic text, voice, music, art, and gaming. It lists specific websites and tools, including thispersondoesnotexist.com, Hugging Face’s BERT, OpenAI’s GPT-3 and Jukebox, Descript, and AI Dungeon, with hyperlinks for each entry. The page is a static text file, not an interactive visualization, using no JavaScript libraries or rendering APIs. It functions as a reference list, showing the breadth of generative media platforms rather than demonstrating any particular code or chart.

CCliffordAnderson

50% match

Loading thumbnail…

Gist 89f2cb981924f2b2ad1b68db99a087af

This example demonstrates a secure HTTP POST request that sends a small JSON payload containing a message, an ID, and a timestamp to a local server endpoint. The Python script builds the request using the `requests` library, formats the data with `json.dumps` (using compact separators and sorted keys), and includes custom authentication headers (`x-pk` and `x-pass`). The server responds with an encrypted string, which the script prints alongside the original data and formatted payload for verification.

CCarlos-Augusto

50% match

Loading thumbnail…

Gist d2f2bc6be1716820160ed5183ea07eaa

This example shows a browser-based text analytics pipeline that scrapes and processes web content before visualizing topic models. The code first uses Selenium with Chrome WebDriver to scrape data, then applies NLP libraries including NLTK, spaCy, TextBlob, and gensim’s LdaModel for topic modeling and coherence scoring. The final visualization employs Plotly’s `plotly.express` and `plotly.graph_objs` to render interactive charts, with `pyo.init_notebook_mode()` enabling notebook display. The rendering approach is evident from the use of `go` traces and `px` figures, though the specific chart type is not shown in the code snippet. The data source is dynamic web pages, as indicated by the Selenium scraping logic.

AArizonaTay

50% match

Loading thumbnail…

Gist e049bb6d7696d1b2d4ff242e68fcd062

This example analyzes text data by performing topic modeling and visualization. It shows the resulting topics and their associated terms, revealing latent themes in the dataset. The code uses NLTK for tokenization and stopword removal, TextBlob for sentiment analysis, and Gensim’s LdaModel and CoherenceModel for topic extraction. Plotly’s `scatter` and `bar` traces render the interactive visualizations, with data processed from a pandas DataFrame. The rendering approach relies on Plotly’s offline mode, initiated via `pyo.init_notebook_mode()`.

AArizonaTay

49% match

Loading thumbnail…

Gist 6499e03ffdfc7479408da70f20af3625

This visualization maps the semantic relationships between words extracted from a text corpus. It shows a network of nodes and edges, where connected words cluster together based on their contextual similarity. The layout uses a force-directed graph, likely powered by d3.forceSimulation, to position nodes and links. The code loads a spaCy NLP model (`en_core_web_lg`) to compute word vectors, and filters out stop words using NLTK’s stopwords list before processing the raw text.

AArizonaTay

49% match

Loading thumbnail…

Gist 34f3e588a35b33357c08853c2370036b

This visualization catalogs AI tools and resources, organizing hundreds of links to libraries and APIs for foundation models, model development, vector databases, and multimodal systems. The layout groups entries by category—such as OpenAI function calling, Microsoft’s BitNet framework, and Hugging Face collections—using a hierarchical list format. It references tools like LM Studio for local deployment, Pyannote for speaker diarization, and Edge-TTS for text-to-speech, with GitHub repositories and documentation pages as primary sources. The rendering relies on standard Markdown headers and hyperlinks to create a browsable, text-based reference.

JJastor11

48% match

Loading thumbnail…

Gist e6556a05221bd9819d98cc5a5363a364

This example shows a live speech-to-text service that streams raw PCM audio from an HTTP request body into Microsoft Azure's Speech SDK for real-time transcription. The Python code implements a custom `PullAudioInputStream` callback that incrementally reads bytes from the Gunicorn request stream and writes them into a `BytesIO` buffer, while the falcon endpoint's `on_post` method triggers recognition. As Azure processes the audio, callback functions capture recognized and intermediate results, which are accumulated into lists along with timing and confidence data. The implementation uses the `speechsdk.audio` module, datetime for soft timeouts, and JSON parsing to extract transcription details from recognition events.

CCheckroth

48% match

Loading thumbnail…

summarize text

This example demonstrates automatic text summarization using the Sumy library in Python, reducing a document to two key sentences. It shows how to extract and summarize text from both plain strings and HTML files, with BeautifulSoup used for parsing HTML content. The code initializes an LSA (Latent Semantic Analysis) summarizer with an English stemmer and stop words, then processes documents through Sumy’s `PlaintextParser` or `HtmlParser` with a `Tokenizer`. The visible output prints the selected sentences, but does not render charts or graphs.

NN0taN3rd

47% match