Update README.md

README.md

@@ -9,275 +9,3 @@ app_file: app.py
 pinned: false
 license: apache-2.0
 ----
-
-# Auto-Research
-![Auto-Research][logo]
-
-[logo]: https://github.com/sidphbot/Auto-Research/blob/main/logo.png
-A no-code utility to generate a detailed well-cited survey with topic clustered sections (draft paper format) and other interesting artifacts from a single research query.
-
-Data Provider: [arXiv](https://arxiv.org/) Open Archive Initiative OAI
-
-Requirements:
- - python 3.7 or above
- - poppler-utils - `sudo apt-get install build-essential libpoppler-cpp-dev pkg-config python-dev`
- - list of requirements in requirements.txt - `cat requirements.txt | xargs pip install`
- - 8GB disk space 
- - 13GB CUDA(GPU) memory - for a survey of 100 searched papers(max_search) and 25 selected papers(num_papers)
-
-#### Demo : 
-
-Video Demo : https://drive.google.com/file/d/1-77J2L10lsW-bFDOGdTaPzSr_utY743g/view?usp=sharing
-
-Kaggle Re-usable Demo : https://www.kaggle.com/sidharthpal/auto-research-generate-survey-from-query 
-
-(`[TIP]` click 'edit and run' to run the demo for your custom queries on a free GPU)
-
-
-#### Installation:
-```
-sudo apt-get install build-essential poppler-utils libpoppler-cpp-dev pkg-config python-dev
-pip install git+https://github.com/sidphbot/Auto-Research.git
-```
-
-#### Run Survey (cli):
-```
-python survey.py [options] <your_research_query>
-```
-
-#### Run Survey (Streamlit web-interface - new):
-```
-streamlit run app.py
-```
-
-#### Run Survey (Python API):
-```
-from survey import Surveyor
-mysurveyor = Surveyor()
-mysurveyor.survey('quantum entanglement')
-```
-
-### Research tools: 
-
-These are independent tools for your research or document text handling needs.
-
-```
-*[Tip]* :(models can be changed in defaults or passed on during init along with `refresh-models=True`)
-```
-
-- `abstractive_summary` - takes a long text document (`string`) and returns a 1-paragraph abstract or “abstractive” summary (`string`)
-
-	Input: 		
-		
-		`longtext` : string
-		
-	Returns: 		
-		
-		`summary` : string
-
-- `extractive_summary` - takes a long text document (`string`) and returns a 1-paragraph of extracted highlights or “extractive” summary (`string`)
-
-	Input: 		
-		
-		`longtext` : string
-		
-	Returns: 		
-		
-		`summary` : string
-
-- `generate_title` - takes a long text document (`string`) and returns a generated title (`string`)
-
-	Input: 		
-		
-		`longtext` : string
-		
-	Returns: 		
-		
-		`title` : string
-
-- `extractive_highlights` - takes a long text document (`string`) and returns a list of extracted highlights (`[string]`), a list of keywords (`[string]`) and key phrases (`[string]`)
-
-	Input: 		
-		
-		`longtext` : string
-		
-	Returns: 		
-		
-		`highlights` : [string]
-		`keywords` : [string]
-		`keyphrases` : [string]
-
-- `extract_images_from_file` - takes a pdf file name (`string`) and returns a list of image filenames (`[string]`).
-
-	Input: 		
-		
-		`pdf_file` : string
-		
-	Returns: 		
-		
-		`images_files` : [string]
-
-- `extract_tables_from_file` - takes a pdf file name (`string`) and returns a list of csv filenames (`[string]`).
-
-	Input: 		
-		
-		`pdf_file` : string
-		
-	Returns: 		
-		
-		`images_files` : [string]
-
-- `cluster_lines` - takes a list of lines (`string`) and returns the topic-clustered sections (`dict(generated_title: [cluster_abstract])`) and clustered lines (`dict(cluster_id: [cluster_lines])`)
-
-	Input: 		
-		
-		`lines` : [string]
-		
-	Returns: 		
-		
-		`sections` : dict(generated_title: [cluster_abstract])
-		`clusters` : dict(cluster_id: [cluster_lines])
-
-- `extract_headings` - *[for scientific texts - Assumes an ‘abstract’ heading present]* takes a text file name (`string`) and returns a list of headings (`[string]`) and refined lines (`[string]`). 
-    
-    `[Tip 1]` : Use `extract_sections` as a wrapper (e.g. `extract_sections(extract_headings(“/path/to/textfile”)`) to get heading-wise sectioned text with refined lines instead (`dict( heading: text)`)
-    
-    `[Tip 2]` : write the word ‘abstract’ at the start of the file text to get an extraction for non-scientific texts as well !!
-
-	Input: 		
-		
-		`text_file` : string 		
-		
-	Returns: 
-		
-		`refined` : [string], 
-		`headings` : [string]
-		`sectioned_doc` : dict( heading: text) (Optional - Wrapper case)
-
-
-## Access/Modify defaults:
-
-- inside code 
-```
-from survey.Surveyor import DEFAULTS
-from pprint import pprint
-
-pprint(DEFAULTS)
-```
-or,
-
-- Modify static config file - `defaults.py`
-
-or,
-
-- At runtime (utility)
-
-```
-python survey.py --help
-```
-```
-usage: survey.py [-h] [--max_search max_metadata_papers]
-                   [--num_papers max_num_papers] [--pdf_dir pdf_dir]
-                   [--txt_dir txt_dir] [--img_dir img_dir] [--tab_dir tab_dir]
-                   [--dump_dir dump_dir] [--models_dir save_models_dir]
-                   [--title_model_name title_model_name]
-                   [--ex_summ_model_name extractive_summ_model_name]
-                   [--ledmodel_name ledmodel_name]
-                   [--embedder_name sentence_embedder_name]
-                   [--nlp_name spacy_model_name]
-                   [--similarity_nlp_name similarity_nlp_name]
-                   [--kw_model_name kw_model_name]
-                   [--refresh_models refresh_models] [--high_gpu high_gpu]
-                   query_string
-
-Generate a survey just from a query !!
-
-positional arguments:
-  query_string          your research query/keywords
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --max_search max_metadata_papers
-                        maximium number of papers to gaze at - defaults to 100
-  --num_papers max_num_papers
-                        maximium number of papers to download and analyse -
-                        defaults to 25
-  --pdf_dir pdf_dir     pdf paper storage directory - defaults to
-                        arxiv_data/tarpdfs/
-  --txt_dir txt_dir     text-converted paper storage directory - defaults to
-                        arxiv_data/fulltext/
-  --img_dir img_dir     image storage directory - defaults to
-                        arxiv_data/images/
-  --tab_dir tab_dir     tables storage directory - defaults to
-                        arxiv_data/tables/
-  --dump_dir dump_dir   all_output_dir - defaults to arxiv_dumps/
-  --models_dir save_models_dir
-                        directory to save models (> 5GB) - defaults to
-                        saved_models/
-  --title_model_name title_model_name
-                        title model name/tag in hugging-face, defaults to
-                        'Callidior/bert2bert-base-arxiv-titlegen'
-  --ex_summ_model_name extractive_summ_model_name
-                        extractive summary model name/tag in hugging-face,
-                        defaults to 'allenai/scibert_scivocab_uncased'
-  --ledmodel_name ledmodel_name
-                        led model(for abstractive summary) name/tag in
-                        hugging-face, defaults to 'allenai/led-
-                        large-16384-arxiv'
-  --embedder_name sentence_embedder_name
-                        sentence embedder name/tag in hugging-face, defaults
-                        to 'paraphrase-MiniLM-L6-v2'
-  --nlp_name spacy_model_name
-                        spacy model name/tag in hugging-face (if changed -
-                        needs to be spacy-installed prior), defaults to
-                        'en_core_sci_scibert'
-  --similarity_nlp_name similarity_nlp_name
-                        spacy downstream model(for similarity) name/tag in
-                        hugging-face (if changed - needs to be spacy-installed
-                        prior), defaults to 'en_core_sci_lg'
-  --kw_model_name kw_model_name
-                        keyword extraction model name/tag in hugging-face,
-                        defaults to 'distilbert-base-nli-mean-tokens'
-  --refresh_models refresh_models
-                        Refresh model downloads with given names (needs
-                        atleast one model name param above), defaults to False
-  --high_gpu high_gpu   High GPU usage permitted, defaults to False
-
-```
-
-- At runtime (code)
-
-    > during surveyor object initialization with `surveyor_obj = Surveyor()`
-    - `pdf_dir`: String, pdf paper storage directory - defaults to `arxiv_data/tarpdfs/`
-    - `txt_dir`: String, text-converted paper storage directory - defaults to `arxiv_data/fulltext/`
-    - `img_dir`: String, image image storage directory - defaults to `arxiv_data/images/`
-    - `tab_dir`: String, tables storage directory - defaults to `arxiv_data/tables/`
-    - `dump_dir`: String, all_output_dir - defaults to `arxiv_dumps/`
-    - `models_dir`: String, directory to save to huge models, defaults to `saved_models/`
-    - `title_model_name`: String, title model name/tag in hugging-face, defaults to `Callidior/bert2bert-base-arxiv-titlegen`
-    - `ex_summ_model_name`: String, extractive summary model name/tag in hugging-face, defaults to `allenai/scibert_scivocab_uncased`
-    - `ledmodel_name`: String, led model(for abstractive summary) name/tag in hugging-face, defaults to `allenai/led-large-16384-arxiv`
-    - `embedder_name`: String, sentence embedder name/tag in hugging-face, defaults to `paraphrase-MiniLM-L6-v2`
-    - `nlp_name`: String, spacy model name/tag in hugging-face (if changed - needs to be spacy-installed prior), defaults to `en_core_sci_scibert`
-    - `similarity_nlp_name`: String, spacy downstream trained model(for similarity) name/tag in hugging-face (if changed - needs to be spacy-installed prior), defaults to `en_core_sci_lg`
-    - `kw_model_name`: String, keyword extraction model name/tag in hugging-face, defaults to `distilbert-base-nli-mean-tokens`
-    - `high_gpu`: Bool, High GPU usage permitted, defaults to `False`
-    - `refresh_models`: Bool, Refresh model downloads with given names (needs atleast one model name param above), defaults to False
-    
-    > during survey generation with `surveyor_obj.survey(query="my_research_query")`
-    - `max_search`: int maximium number of papers to gaze at - defaults to `100`
-    - `num_papers`: int maximium number of papers to download and analyse - defaults to `25`
-
-
-
-#### Artifacts generated (zipped):
-- Detailed survey draft paper as txt file
-- A curated list of top 25+ papers as pdfs and txts
-- Images extracted from above papers as jpegs, bmps etc
-- Heading/Section wise highlights extracted from above papers as a re-usable pure python joblib dump
-- Tables extracted from papers(optional)
-- Corpus of metadata highlights/text of top 100 papers as a re-usable pure python joblib dump
-
-
-Please cite this repo if it helped you :)
-