update: look at todo

main
Thamognya Kodi 2023-01-10 19:50:43 +07:00
parent e647f3a7d8
commit 8f21f3fba3
27 changed files with 1618 additions and 556 deletions

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@ -15,6 +15,7 @@
# Icon must end with two \r
Icon
*.tar
# Thumbnails
._*

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@ -14,22 +14,22 @@ appearance, race, religion, or sexual identity and orientation.
Examples of behavior that contributes to creating a positive environment
include:
* Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism
* Focusing on what is best for the community
* Showing empathy towards other community members
- Using welcoming and inclusive language
- Being respectful of differing viewpoints and experiences
- Gracefully accepting constructive criticism
- Focusing on what is best for the community
- Showing empathy towards other community members
Examples of unacceptable behavior by participants include:
* The use of sexualized language or imagery and unwelcome sexual attention or
advances
* Trolling, insulting/derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or electronic
address, without explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
- The use of sexualized language or imagery and unwelcome sexual attention or
advances
- Trolling, insulting/derogatory comments, and personal or political attacks
- Public or private harassment
- Publishing others' private information, such as a physical or electronic
address, without explicit permission
- Other conduct which could reasonably be considered inappropriate in a
professional setting
## Our Responsibilities

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@ -1,7 +1,7 @@
---
name: ๐Ÿ› Bug report
about: If something isn't working ๐Ÿ”ง
title: ''
title: ""
labels: bug
assignees:
---
@ -22,8 +22,8 @@ Steps to reproduce the behavior:
### Environment
* OS: [e.g. Linux / Windows / macOS]
* Python version, get it with:
- OS: [e.g. Linux / Windows / macOS]
- Python version, get it with:
```bash
python --version

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@ -1,7 +1,7 @@
---
name: ๐Ÿš€ Feature request
about: Suggest an idea for this project ๐Ÿ–
title: ''
title: ""
labels: enhancement
assignees:
---

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@ -1,7 +1,7 @@
---
name: โ“ Question
about: Ask a question about this project ๐ŸŽ“
title: ''
title: ""
labels: question
assignees:
---
@ -10,7 +10,7 @@ assignees:
<!-- Mark with an `x` all the checkboxes that apply (like `[x]`) -->
- [ ] I've searched the project's [`issues`](https://github.com/internet-ml/internet-ml/issues?q=is%3Aissue).
- [ ] I've searched the project's [`issues`](https://github.com/internet-ml/internet-ml/issues?q=is%3Aissue).
## โ“ Question

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@ -10,19 +10,19 @@
<!-- Mark with an `x` all the checkboxes that apply (like `[x]`) -->
- [ ] ๐Ÿ“š Examples / docs / tutorials / dependencies update
- [ ] ๐Ÿ”ง Bug fix (non-breaking change which fixes an issue)
- [ ] ๐Ÿฅ‚ Improvement (non-breaking change which improves an existing feature)
- [ ] ๐Ÿš€ New feature (non-breaking change which adds functionality)
- [ ] ๐Ÿ’ฅ Breaking change (fix or feature that would cause existing functionality to change)
- [ ] ๐Ÿ” Security fix
- [ ] ๐Ÿ“š Examples / docs / tutorials / dependencies update
- [ ] ๐Ÿ”ง Bug fix (non-breaking change which fixes an issue)
- [ ] ๐Ÿฅ‚ Improvement (non-breaking change which improves an existing feature)
- [ ] ๐Ÿš€ New feature (non-breaking change which adds functionality)
- [ ] ๐Ÿ’ฅ Breaking change (fix or feature that would cause existing functionality to change)
- [ ] ๐Ÿ” Security fix
## Checklist
<!-- Mark with an `x` all the checkboxes that apply (like `[x]`) -->
- [ ] I've read the [`CODE_OF_CONDUCT.md`](https://github.com/internet-ml/internet-ml/blob/master/CODE_OF_CONDUCT.md) document.
- [ ] I've read the [`CONTRIBUTING.md`](https://github.com/internet-ml/internet-ml/blob/master/CONTRIBUTING.md) guide.
- [ ] I've updated the code style using `make codestyle`.
- [ ] I've written tests for all new methods and classes that I created.
- [ ] I've written the docstring in Google format for all the methods and classes that I used.
- [ ] I've read the [`CODE_OF_CONDUCT.md`](https://github.com/internet-ml/internet-ml/blob/master/CODE_OF_CONDUCT.md) document.
- [ ] I've read the [`CONTRIBUTING.md`](https://github.com/internet-ml/internet-ml/blob/master/CONTRIBUTING.md) guide.
- [ ] I've updated the code style using `make codestyle`.
- [ ] I've written tests for all new methods and classes that I created.
- [ ] I've written the docstring in Google format for all the methods and classes that I used.

18
.github/SECURITY.md vendored
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@ -7,18 +7,18 @@
Vulnerabilities can be reported by emailing core members:
- internet-ml [contact@thamognya.com](mailto:contact@thamognya.com)
- internet-ml [contact@thamognya.com](mailto:contact@thamognya.com)
Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:
- Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
- Full paths of source file(s) related to the manifestation of the issue
- The location of the affected source code (tag/branch/commit or direct URL)
- Any special configuration required to reproduce the issue
- Environment (e.g. Linux / Windows / macOS)
- Step-by-step instructions to reproduce the issue
- Proof-of-concept or exploit code (if possible)
- Impact of the issue, including how an attacker might exploit the issue
- Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
- Full paths of source file(s) related to the manifestation of the issue
- The location of the affected source code (tag/branch/commit or direct URL)
- Any special configuration required to reproduce the issue
- Environment (e.g. Linux / Windows / macOS)
- Step-by-step instructions to reproduce the issue
- Proof-of-concept or exploit code (if possible)
- Impact of the issue, including how an attacker might exploit the issue
This information will help us triage your report more quickly.

12
.github/TODO.md vendored
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@ -1,7 +1,5 @@
- [ ] make frontend -> look better plus clean up code
- [ ] find better way of using .env from django (possibly)
- [ ] use gpt-neox-20b for qa
- [ ] deploy to caprover or etc.
- [ ] dockerize / containerize
- [x] Get package to use .env from django
- [x] IMP IMP work on API_KEY fixing config or use .env
- [ ] use different ml models
- [ ] deploy to caprover or etc.
- [ ] dockerize / containerize
- [x] Get package to use .env from django
- [x] IMP IMP work on API_KEY fixing config or use .env

783
.prettierignore 100644
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@ -0,0 +1,783 @@
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@ -6,7 +6,8 @@ import sys
from pathlib import Path
import dotenv
from transformers import pipeline
import openai
from transformers import list_models, pipeline
logging.basicConfig(
filename="QA.log",
@ -20,23 +21,63 @@ sys.path.append(str(Path(__file__).parent.parent.parent) + "/utils")
import config
import internet
dotenv.load_dotenv()
def answer(
query: str, GOOGLE_SEARCH_API_KEY: str, GOOGLE_SEARCH_ENGINE_ID: str
query: str,
model: str = "openai-ChatGPT",
GOOGLE_SEARCH_API_KEY: str = "",
GOOGLE_SEARCH_ENGINE_ID: str = "",
OPENAI_API_KEY: str = "",
CHATGPT_SESSION_TOKEN: str = "",
) -> tuple[Any, list[str]]:
QA_MODEL: Any = pipeline("question-answering")
results: tuple[list[str], list[str]] = internet.Google(
query, GOOGLE_SEARCH_API_KEY, GOOGLE_SEARCH_ENGINE_ID
).google()
answer: tuple[Any, list[str]] = (
QA_MODEL(question=query, context=str(results[0])),
results[1],
)
# if environment keys are not given, assume it is in env
if GOOGLE_SEARCH_API_KEY == "":
GOOGLE_SEARCH_API_KEY = str(os.environ.get("GOOGLE_SEARCH_API_KEY"))
if GOOGLE_SEARCH_ENGINE_ID == "":
GOOGLE_SEARCH_ENGINE_ID = str(os.environ.get("GOOGLE_SEARCH_ENGINE_ID"))
if OPENAI_API_KEY == "":
OPENAI_API_KEY = str(os.environ.get("OPENAI_API_KEY"))
openai.api_key = OPENAI_API_KEY
if CHATGPT_SESSION_TOKEN == "":
CHATGPT_SESSION_TOKEN = str(os.environ.get("CHATGPT_SESSION_TOKEN"))
"""
model naming convention
# Open-AI models:
include prefix openai-*
# HuggingFace
include prefix hf-*
#
"""
if not (model.startswith("openai-") == 0 or model.startswith("hf-") == 0):
model = "openai-ChatGPT" # Default
answer: str = ""
if model.startswith("openai-") == 0:
# results: tuple[list[str], list[str]] = internet.Google(
# query, GOOGLE_SEARCH_API_KEY, GOOGLE_SEARCH_ENGINE_ID
# ).google(filter_irrelevant=True)
print("hi")
else:
models = [
model
for model in list_models()
if "qa" in model or "question-answering" in model
]
model = model.replace("hf-", "", 1)
if not model in models:
model = "hf-"
# results: tuple[list[str], list[str]] = internet.Google(
# query, GOOGLE_SEARCH_API_KEY, GOOGLE_SEARCH_ENGINE_ID
# ).google(filter_irrelevant=False)
answer_result: tuple[Any, list[str]] = (answer, ["hi"]) # results[1])
if config.CONF_DEBUG:
logging.info(f"Answer: {answer}")
return answer
logging.info(f"Answer: {answer_result}")
return answer_result
# print(os.environ)
# print(answer("Who is Elon Musk?"))
print(answer("What is the newest Pokemon Game?"))
# def custom_answer

View File

@ -1,7 +1,8 @@
from typing import Any, List, Tuple
from typing import Any, Dict, List, Tuple
import logging
import os
import pickle
import sys
from importlib import reload
from pathlib import Path
@ -27,18 +28,21 @@ sys.path.append(str(Path(__file__).parent.parent.parent.parent) + "/utils")
sys.path.append(str(Path(__file__).parent.parent))
import asyncio
import concurrent.futures
import itertools
import re
import aiohttp
import config
from bs4 import BeautifulSoup
from keywords import get_keywords
from normalize import normalizer
# from relevancy import filter_irrelevant
from relevancy import filter_relevant
from sentencize import sentencizer
from urlextract import URLExtract
dotenv.load_dotenv()
class Google:
def __init__(
@ -49,20 +53,6 @@ class Google:
) -> None:
self.__GOOGLE_SEARCH_API_KEY: str = GOOGLE_SEARCH_API_KEY
self.__GOOGLE_SEARCH_ENGINE_ID: str = GOOGLE_SEARCH_ENGINE_ID
# dotenv_path = os.path.join(os.path.dirname(__file__), '.env')
# dotenv.load_dotenv(dotenv_path)
# self.__GOOGLE_SEARCH_API_KEY: str = ""
# self.__GOOGLE_SEARCH_ENGINE_ID: str = ""
# if (
# "INTERNET_ML_GOOGLE_API" in os.environ
# and "INTERNET_ML_GOOGLE_SEARCH_ENGINE_ID" in os.environ
# ):
# self.__GOOGLE_SEARCH_API_KEY = str(os.environ.get("INTERNET_ML_GOOGLE_API"))
# self.__GOOGLE_SEARCH_ENGINE_ID = str(
# os.environ.get("INTERNET_ML_GOOGLE_SEARCH_ENGINE_ID")
# )
# else:
# exit("API KEYS")
self.__num_res: int = (
5
if config.NLP_CONF_MODE == "speed"
@ -78,6 +68,8 @@ class Google:
str(self.__query),
)
)
self.__cache_file: str = "google_internet_cache.pkl"
self.__content: list[str] = []
def __get_urls(self: "Google") -> None:
# Send the request to the Google Search API
@ -139,30 +131,42 @@ class Google:
loop.close()
self.__content = self.__flatten(contents)
def google(self: "Google") -> tuple[list[str], list[str]]:
# Hard coded exceptions - START
if "Thamognya" in self.__query or "thamognya" in self.__query:
return (["The smartest person in the world"], ["I decided it"])
if "modi" in self.__query or "Modi" in self.__query:
return (
["Prime Minister of India"],
[
"https://www.narendramodi.in/",
"https://en.wikipedia.org/wiki/Narendra_Modi",
"https://twitter.com/narendramodi?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor",
"https://www.instagram.com/narendramodi/?hl=en",
"https://www.facebook.com/narendramodi/",
"http://www.pmindia.gov.in/en/",
"https://timesofindia.indiatimes.com/topic/Narendra-Modi",
"https://www.britannica.com/biography/Narendra-Modi",
"https://indianexpress.com/article/india/zelenskky-dials-pm-modi-wishes-new-delhi-successful-g20-presidency-8345365/",
"https://economictimes.indiatimes.com/news/narendra-modi",
],
)
# Hard coded exceptions - End
def __filter_irrelevant_processing(self: "Google") -> None:
# Create a ThreadPoolExecutor with 4 worker threads
with concurrent.futures.ThreadPoolExecutor(max_workers=500) as executor:
# Create a list of futures for the filtering tasks
futures = [executor.submit(filter_relevant, self.__content, self.__query)]
# Wait for the tasks to complete
concurrent.futures.wait(futures)
# Get the results of the tasks
content: list[str] = []
for future in futures:
content.append(future.result())
self.__content = content
def google(
self: "Google", filter_irrelevant: bool = True
) -> tuple[list[str], list[str]]:
# Check the cache file first
try:
with open(self.__cache_file, "rb") as f:
cache = pickle.load(f)
except FileNotFoundError:
cache = {}
# Check if query are in the cache
if self.__query in cache:
results_cache: tuple[list[str], list[str]] = cache[self.__query]
return results_cache
# If none of the keywords are in the cache, get the results and update the cache
self.__get_urls()
self.__get_urls_contents()
return (self.__content, self.__urls)
if filter_irrelevant:
self.__filter_irrelevant_processing()
results: tuple[list[str], list[str]] = (self.__content, self.__urls)
cache[self.__query] = results
with open(self.__cache_file, "wb") as f:
pickle.dump(cache, f)
return results
"""

View File

@ -0,0 +1,13 @@
from typing import List
import spacy
def get_keywords(query: str) -> list[str]:
# Load the NLP model
nlp = spacy.load("en_core_web_sm")
# Process the query
doc = nlp(query)
# Extract the nouns and adjectives from the query
keywords = [token.text for token in doc if token.pos_ in ["NOUN", "ADJ"]]
return keywords

View File

@ -2,5 +2,5 @@
I will be using a combination of
- CoQA (unmodified)
- Natural Questions (modified to make context from answer)
- CoQA (unmodified)
- Natural Questions (modified to make context from answer)

View File

@ -1,125 +1,27 @@
from typing import Any
from typing import List
import concurrent.futures
import logging
import sys
from pathlib import Path
import nltk
import numpy as np
import spacy
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
# from scipy.spatial.distance import jaccard
from sklearn.feature_extraction.text import TfidfVectorizer
sys.path.append(str(Path(__file__).parent.parent.parent.parent) + "/utils/NLP")
sys.path.append(str(Path(__file__).parent.parent.parent.parent) + "/utils")
import config
logging.basicConfig(
filename="relevancy.log",
filemode="w",
level=logging.INFO,
format="%(name)s - %(levelname)s - %(message)s",
)
nltk.download("punkt", quiet=True)
nltk.download("stopwords", quiet=True)
nltk.download("wordnet", quiet=True)
nltk.download("omw-1.4", quiet=True)
nlp = spacy.load("en_core_web_sm") # Load the English language model
lemmatizer = WordNetLemmatizer() # Initialize the WordNet lemmatizer
stop_words = set(stopwords.words("english")) # Get the English stop words
import re
def jaccard(u: Any, v: Any) -> Any:
# Pad the shorter array with zeros at the end
u = np.pad(u, (0, max(u.shape[0], v.shape[0]) - u.shape[0]), "constant")
v = np.pad(v, (0, max(u.shape[0], v.shape[0]) - v.shape[0]), "constant")
# Calculate the Jaccard similarity
nonzero = np.bitwise_or(u != 0, v != 0)
intersection = np.bitwise_and(u != 0, v != 0)
return 1.0 - float(np.count_nonzero(intersection)) / float(
np.count_nonzero(nonzero)
)
def is_answer(sentence: str, question: str, threshold: float = 0.3) -> bool:
# Tokenize the sentence and the question
sentence_tokens = word_tokenize(sentence)
question_tokens = word_tokenize(question)
# Remove stop words from the sentence and the question
sentence_tokens = [
token for token in sentence_tokens if token.lower() not in stop_words
]
question_tokens = [
token for token in question_tokens if token.lower() not in stop_words
]
# Perform lemmatization on the sentence and the question
sentence_tokens = [lemmatizer.lemmatize(token.lower()) for token in sentence_tokens]
question_tokens = [lemmatizer.lemmatize(token.lower()) for token in question_tokens]
# Extract the main verb from the question
main_verb = None
for token in question_tokens:
if nlp(token)[0].pos_ == "VERB":
main_verb = token
break
# Generate numerical representations of the sentence and the question using TF-IDF
vectorizer = TfidfVectorizer()
sentence_vector = vectorizer.fit_transform([sentence]).toarray()[0]
question_vector = vectorizer.fit_transform([question]).toarray()[0]
# Calculate the similarity between the sentence and the question
similarity = 1 - jaccard(sentence_vector, question_vector)
# Check if the sentence answers the question
answer: bool
if main_verb is None:
answer = similarity >= threshold
else:
answer = main_verb in sentence_tokens and similarity >= threshold
if config.CONF_DEBUG:
logging.info(
f"Is Relevant -> Sentence: {sentence}, Question: {question} -> Relevancy: {answer}"
def filter_relevant(sentences: list[str], question: str) -> list[str]:
def is_relevant(sentence: str) -> bool:
# Use regular expression to check if the sentence contains any of the words in the question
return bool(
re.search(r"\b" + "\\b|\\b".join(question.split()) + r"\b", sentence)
)
return answer
# Create a thread pool with 4 worker threads
with concurrent.futures.ThreadPoolExecutor(max_workers=500) as executor:
# Map the is_relevant function to each sentence in the list and return the resulting list of booleans
relevant = list(executor.map(is_relevant, sentences))
# Return only the sentences where is_relevant returned True
return [sentence for sentence, relevant in zip(sentences, relevant) if relevant]
def filter_irrelevant(sentences: list[str], question: str) -> list[str]:
# Create a list to store the relevant sentences
relevant_sentences = []
for sentence in sentences:
if is_answer(sentence, question):
relevant_sentences.append(sentence)
print(sentence)
if config.CONF_DEBUG:
logging.info(f"Relevant Sentences: {relevant_sentences}")
return relevant_sentences
# # Test the is_answer function
# sentence = "Neil Armstrong was the first person to walk on the Moon."
# question = "Who was the first person to walk on the Moon?"
# if is_answer(sentence, question):
# print("The sentence answers the question.")
# else:
# print("The sentence does not answer the question.")
# from concurrent.futures import ThreadPoolExecutor
# import concurrent.futures
"""
print(
filter_irrelevant(
[
"Neil Armstrong is an American Astronaut",
"Neil Armstrong is dead",
"Neil Armstrng is fake",
],
"Who is Neil Armstrong?",
)
)
"""
# # Example usage
# context = ["The quick brown fox jumps over the lazy dog.", "The slow green snake slithers under the rock."]
# question = "quick brown fox"
# relevant_context = filter_relevant_context(context, question)
# print(relevant_context) # Output: ["The quick brown fox jumps over the lazy dog."]

741
poetry.lock generated
View File

@ -167,23 +167,36 @@ files = [
[[package]]
name = "astroid"
version = "2.12.13"
version = "2.13.2"
description = "An abstract syntax tree for Python with inference support."
category = "dev"
optional = false
python-versions = ">=3.7.2"
files = [
{file = "astroid-2.12.13-py3-none-any.whl", hash = "sha256:10e0ad5f7b79c435179d0d0f0df69998c4eef4597534aae44910db060baeb907"},
{file = "astroid-2.12.13.tar.gz", hash = "sha256:1493fe8bd3dfd73dc35bd53c9d5b6e49ead98497c47b2307662556a5692d29d7"},
{file = "astroid-2.13.2-py3-none-any.whl", hash = "sha256:8f6a8d40c4ad161d6fc419545ae4b2f275ed86d1c989c97825772120842ee0d2"},
{file = "astroid-2.13.2.tar.gz", hash = "sha256:3bc7834720e1a24ca797fd785d77efb14f7a28ee8e635ef040b6e2d80ccb3303"},
]
[package.dependencies]
lazy-object-proxy = ">=1.4.0"
typing-extensions = ">=4.0.0"
wrapt = [
{version = ">=1.11,<2", markers = "python_version < \"3.11\""},
{version = ">=1.14,<2", markers = "python_version >= \"3.11\""},
]
[[package]]
name = "async-generator"
version = "1.10"
description = "Async generators and context managers for Python 3.5+"
category = "main"
optional = false
python-versions = ">=3.5"
files = [
{file = "async_generator-1.10-py3-none-any.whl", hash = "sha256:01c7bf666359b4967d2cda0000cc2e4af16a0ae098cbffcb8472fb9e8ad6585b"},
{file = "async_generator-1.10.tar.gz", hash = "sha256:6ebb3d106c12920aaae42ccb6f787ef5eefdcdd166ea3d628fa8476abe712144"},
]
[[package]]
name = "async-timeout"
version = "4.0.2"
@ -357,6 +370,83 @@ files = [
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]
[[package]]
name = "cffi"
version = "1.15.1"
description = "Foreign Function Interface for Python calling C code."
category = "main"
optional = false
python-versions = "*"
files = [
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category = "dev"
category = "main"
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python-versions = ">=3.7"
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