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 must end with two \r
Icon Icon
*.tar
# Thumbnails # Thumbnails
._* ._*

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@ -14,21 +14,21 @@ appearance, race, religion, or sexual identity and orientation.
Examples of behavior that contributes to creating a positive environment Examples of behavior that contributes to creating a positive environment
include: include:
* Using welcoming and inclusive language - Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences - Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism - Gracefully accepting constructive criticism
* Focusing on what is best for the community - Focusing on what is best for the community
* Showing empathy towards other community members - Showing empathy towards other community members
Examples of unacceptable behavior by participants include: Examples of unacceptable behavior by participants include:
* The use of sexualized language or imagery and unwelcome sexual attention or - The use of sexualized language or imagery and unwelcome sexual attention or
advances advances
* Trolling, insulting/derogatory comments, and personal or political attacks - Trolling, insulting/derogatory comments, and personal or political attacks
* Public or private harassment - Public or private harassment
* Publishing others' private information, such as a physical or electronic - Publishing others' private information, such as a physical or electronic
address, without explicit permission address, without explicit permission
* Other conduct which could reasonably be considered inappropriate in a - Other conduct which could reasonably be considered inappropriate in a
professional setting professional setting
## Our Responsibilities ## Our Responsibilities

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

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@ -1,7 +1,7 @@
--- ---
name: 🚀 Feature request name: 🚀 Feature request
about: Suggest an idea for this project 🏖 about: Suggest an idea for this project 🏖
title: '' title: ""
labels: enhancement labels: enhancement
assignees: assignees:
--- ---

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@ -1,7 +1,7 @@
--- ---
name: ❓ Question name: ❓ Question
about: Ask a question about this project 🎓 about: Ask a question about this project 🎓
title: '' title: ""
labels: question labels: question
assignees: assignees:
--- ---

4
.github/TODO.md vendored
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@ -1,6 +1,4 @@
- [ ] make frontend -> look better plus clean up code - [ ] use different ml models
- [ ] find better way of using .env from django (possibly)
- [ ] use gpt-neox-20b for qa
- [ ] deploy to caprover or etc. - [ ] deploy to caprover or etc.
- [ ] dockerize / containerize - [ ] dockerize / containerize
- [x] Get package to use .env from django - [x] Get package to use .env from django

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 from pathlib import Path
import dotenv import dotenv
from transformers import pipeline import openai
from transformers import list_models, pipeline
logging.basicConfig( logging.basicConfig(
filename="QA.log", filename="QA.log",
@ -20,23 +21,63 @@ sys.path.append(str(Path(__file__).parent.parent.parent) + "/utils")
import config import config
import internet import internet
dotenv.load_dotenv()
def answer( 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]]: ) -> tuple[Any, list[str]]:
QA_MODEL: Any = pipeline("question-answering") # if environment keys are not given, assume it is in env
results: tuple[list[str], list[str]] = internet.Google( if GOOGLE_SEARCH_API_KEY == "":
query, GOOGLE_SEARCH_API_KEY, GOOGLE_SEARCH_ENGINE_ID GOOGLE_SEARCH_API_KEY = str(os.environ.get("GOOGLE_SEARCH_API_KEY"))
).google() if GOOGLE_SEARCH_ENGINE_ID == "":
answer: tuple[Any, list[str]] = ( GOOGLE_SEARCH_ENGINE_ID = str(os.environ.get("GOOGLE_SEARCH_ENGINE_ID"))
QA_MODEL(question=query, context=str(results[0])), if OPENAI_API_KEY == "":
results[1], 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: if config.CONF_DEBUG:
logging.info(f"Answer: {answer}") logging.info(f"Answer: {answer_result}")
return answer return answer_result
# print(os.environ) # print(os.environ)
# print(answer("Who is Elon Musk?")) print(answer("What is the newest Pokemon Game?"))
# def custom_answer # def custom_answer

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

@ -1,125 +1,27 @@
from typing import Any from typing import List
import concurrent.futures import concurrent.futures
import logging import re
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
def jaccard(u: Any, v: Any) -> Any: def filter_relevant(sentences: list[str], question: str) -> list[str]:
# Pad the shorter array with zeros at the end def is_relevant(sentence: str) -> bool:
u = np.pad(u, (0, max(u.shape[0], v.shape[0]) - u.shape[0]), "constant") # Use regular expression to check if the sentence contains any of the words in the question
v = np.pad(v, (0, max(u.shape[0], v.shape[0]) - v.shape[0]), "constant") return bool(
# Calculate the Jaccard similarity re.search(r"\b" + "\\b|\\b".join(question.split()) + r"\b", sentence)
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)
) )
# 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))
def is_answer(sentence: str, question: str, threshold: float = 0.3) -> bool: # Return only the sentences where is_relevant returned True
# Tokenize the sentence and the question return [sentence for sentence, relevant in zip(sentences, relevant) if relevant]
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}"
)
return answer
def filter_irrelevant(sentences: list[str], question: str) -> list[str]: # # Example usage
# Create a list to store the relevant sentences # context = ["The quick brown fox jumps over the lazy dog.", "The slow green snake slithers under the rock."]
relevant_sentences = [] # question = "quick brown fox"
for sentence in sentences: # relevant_context = filter_relevant_context(context, question)
if is_answer(sentence, question): # print(relevant_context) # Output: ["The quick brown fox jumps over the lazy dog."]
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?",
)
)
"""

741
poetry.lock generated
View File

@ -167,23 +167,36 @@ files = [
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name = "astroid" name = "astroid"
version = "2.12.13" version = "2.13.2"
description = "An abstract syntax tree for Python with inference support." description = "An abstract syntax tree for Python with inference support."
category = "dev" category = "dev"
optional = false optional = false
python-versions = ">=3.7.2" python-versions = ">=3.7.2"
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[package.dependencies] [package.dependencies]
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typing-extensions = ">=4.0.0"
wrapt = [ wrapt = [
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version = "4.0.2" version = "4.0.2"
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torch = ">=1.7" torch = ">=1.7"
torchvision = "*" torchvision = "*"
[[package]]
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slack = ["slack-sdk"] slack = ["slack-sdk"]
telegram = ["requests"] telegram = ["requests"]
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{file = "websockets-10.4-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:09a1814bb15eff7069e51fed0826df0bc0702652b5cb8f87697d469d79c23576"},
{file = "websockets-10.4-pp38-pypy38_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ff64a1d38d156d429404aaa84b27305e957fd10c30e5880d1765c9480bea490f"},
{file = "websockets-10.4-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:b343f521b047493dc4022dd338fc6db9d9282658862756b4f6fd0e996c1380e1"},
{file = "websockets-10.4-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:932af322458da7e4e35df32f050389e13d3d96b09d274b22a7aa1808f292fee4"},
{file = "websockets-10.4-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d6a4162139374a49eb18ef5b2f4da1dd95c994588f5033d64e0bbfda4b6b6fcf"},
{file = "websockets-10.4-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c57e4c1349fbe0e446c9fa7b19ed2f8a4417233b6984277cce392819123142d3"},
{file = "websockets-10.4-pp39-pypy39_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b627c266f295de9dea86bd1112ed3d5fafb69a348af30a2422e16590a8ecba13"},
{file = "websockets-10.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:05a7233089f8bd355e8cbe127c2e8ca0b4ea55467861906b80d2ebc7db4d6b72"},
{file = "websockets-10.4.tar.gz", hash = "sha256:eef610b23933c54d5d921c92578ae5f89813438fded840c2e9809d378dc765d3"},
]
[[package]] [[package]]
name = "wheel" name = "wheel"
version = "0.38.4" version = "0.38.4"
@ -3516,6 +3910,21 @@ files = [
{file = "wrapt-1.14.1.tar.gz", hash = "sha256:380a85cf89e0e69b7cfbe2ea9f765f004ff419f34194018a6827ac0e3edfed4d"}, {file = "wrapt-1.14.1.tar.gz", hash = "sha256:380a85cf89e0e69b7cfbe2ea9f765f004ff419f34194018a6827ac0e3edfed4d"},
] ]
[[package]]
name = "wsproto"
version = "1.2.0"
description = "WebSockets state-machine based protocol implementation"
category = "main"
optional = false
python-versions = ">=3.7.0"
files = [
{file = "wsproto-1.2.0-py3-none-any.whl", hash = "sha256:b9acddd652b585d75b20477888c56642fdade28bdfd3579aa24a4d2c037dd736"},
{file = "wsproto-1.2.0.tar.gz", hash = "sha256:ad565f26ecb92588a3e43bc3d96164de84cd9902482b130d0ddbaa9664a85065"},
]
[package.dependencies]
h11 = ">=0.9.0,<1"
[[package]] [[package]]
name = "xxhash" name = "xxhash"
version = "3.2.0" version = "3.2.0"
@ -3731,4 +4140,4 @@ testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools"
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = "^3.10" python-versions = "^3.10"
content-hash = "c9dff93b24ef36e3d334731d34e80372b2c3b2c4f19b12d8bee10772e079217d" content-hash = "d5757348a31dce3db191455bfba230c11859b968d8bfe76c44ec825e3c1d7738"

View File

@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry] [tool.poetry]
name = "internet-ml" name = "internet-ml"
version = "2.0.0" version = "2.0.2"
description = "Internet-ML: Allowing ML to connect to the internet" description = "Internet-ML: Allowing ML to connect to the internet"
readme = "./.github/README.md" readme = "./.github/README.md"
authors = ["Thamognya Kodi <contact@thamognya.com>"] authors = ["Thamognya Kodi <contact@thamognya.com>"]
@ -53,6 +53,8 @@ spacy = "^3.4.4"
nltk = "^3.8" nltk = "^3.8"
beautifulsoup4 = "^4.11.1" beautifulsoup4 = "^4.11.1"
diffusers = "^0.11.1" diffusers = "^0.11.1"
openai = "^0.26.0"
revchatgpt = "^0.1.1"
[tool.poetry.group.dev.dependencies] [tool.poetry.group.dev.dependencies]
bandit = "^1.7.4" bandit = "^1.7.4"

View File

@ -31,16 +31,17 @@ markupsafe==2.1.1 ; python_version >= "3.10" and python_version < "4.0"
multidict==6.0.4 ; python_version >= "3.10" and python_version < "4.0" multidict==6.0.4 ; python_version >= "3.10" and python_version < "4.0"
multiprocess==0.70.14 ; python_version >= "3.10" and python_version < "4.0" multiprocess==0.70.14 ; python_version >= "3.10" and python_version < "4.0"
murmurhash==1.0.9 ; python_version >= "3.10" and python_version < "4.0" murmurhash==1.0.9 ; python_version >= "3.10" and python_version < "4.0"
nltk==3.8 ; python_version >= "3.10" and python_version < "4.0" nltk==3.8.1 ; python_version >= "3.10" and python_version < "4.0"
numpy==1.24.1 ; python_version < "4.0" and python_version >= "3.10" numpy==1.24.1 ; python_version < "4.0" and python_version >= "3.10"
nvidia-cublas-cu11==11.10.3.66 ; python_version >= "3.10" and python_version < "4.0" and platform_system == "Linux" nvidia-cublas-cu11==11.10.3.66 ; python_version >= "3.10" and python_version < "4.0" and platform_system == "Linux"
nvidia-cuda-nvrtc-cu11==11.7.99 ; python_version >= "3.10" and python_version < "4.0" and platform_system == "Linux" nvidia-cuda-nvrtc-cu11==11.7.99 ; python_version >= "3.10" and python_version < "4.0" and platform_system == "Linux"
nvidia-cuda-runtime-cu11==11.7.99 ; python_version >= "3.10" and python_version < "4.0" and platform_system == "Linux" nvidia-cuda-runtime-cu11==11.7.99 ; python_version >= "3.10" and python_version < "4.0" and platform_system == "Linux"
nvidia-cudnn-cu11==8.5.0.96 ; python_version >= "3.10" and python_version < "4.0" and platform_system == "Linux" nvidia-cudnn-cu11==8.5.0.96 ; python_version >= "3.10" and python_version < "4.0" and platform_system == "Linux"
openai==0.26.0 ; python_version >= "3.10" and python_version < "4.0"
packaging==21.3 ; python_version >= "3.10" and python_version < "4.0" packaging==21.3 ; python_version >= "3.10" and python_version < "4.0"
pandas==1.5.2 ; python_version >= "3.10" and python_version < "4.0" pandas==1.5.2 ; python_version >= "3.10" and python_version < "4.0"
pathy==0.10.1 ; python_version >= "3.10" and python_version < "4.0" pathy==0.10.1 ; python_version >= "3.10" and python_version < "4.0"
pillow==9.3.0 ; python_version >= "3.10" and python_version < "4.0" pillow==9.4.0 ; python_version >= "3.10" and python_version < "4.0"
platformdirs==2.6.2 ; python_version >= "3.10" and python_version < "4.0" platformdirs==2.6.2 ; python_version >= "3.10" and python_version < "4.0"
preshed==3.0.8 ; python_version >= "3.10" and python_version < "4.0" preshed==3.0.8 ; python_version >= "3.10" and python_version < "4.0"
psutil==5.9.4 ; python_version >= "3.10" and python_version < "4.0" psutil==5.9.4 ; python_version >= "3.10" and python_version < "4.0"
@ -60,7 +61,7 @@ rich==10.16.2 ; python_version >= "3.10" and python_version < "4.0"
scikit-learn==1.2.0 ; python_version >= "3.10" and python_version < "4.0" scikit-learn==1.2.0 ; python_version >= "3.10" and python_version < "4.0"
scipy==1.9.3 ; python_version >= "3.10" and python_version < "4.0" scipy==1.9.3 ; python_version >= "3.10" and python_version < "4.0"
setuptools==65.6.3 ; python_version >= "3.10" and python_version < "4.0" setuptools==65.6.3 ; python_version >= "3.10" and python_version < "4.0"
shellingham==1.5.0 ; python_version >= "3.10" and python_version < "4.0" shellingham==1.5.0.post1 ; python_version >= "3.10" and python_version < "4.0"
six==1.16.0 ; python_version >= "3.10" and python_version < "4.0" six==1.16.0 ; python_version >= "3.10" and python_version < "4.0"
smart-open==6.3.0 ; python_version >= "3.10" and python_version < "4.0" smart-open==6.3.0 ; python_version >= "3.10" and python_version < "4.0"
soupsieve==2.3.2.post1 ; python_version >= "3.10" and python_version < "4.0" soupsieve==2.3.2.post1 ; python_version >= "3.10" and python_version < "4.0"
@ -82,7 +83,7 @@ typer[all]==0.4.2 ; python_version >= "3.10" and python_version < "4.0"
types-requests==2.28.11.7 ; python_version >= "3.10" and python_version < "4.0" types-requests==2.28.11.7 ; python_version >= "3.10" and python_version < "4.0"
types-urllib3==1.26.25.4 ; python_version >= "3.10" and python_version < "4.0" types-urllib3==1.26.25.4 ; python_version >= "3.10" and python_version < "4.0"
typing-extensions==4.4.0 ; python_version >= "3.10" and python_version < "4.0" typing-extensions==4.4.0 ; python_version >= "3.10" and python_version < "4.0"
uritools==4.0.0 ; python_version >= "3.10" and python_version < "4.0" uritools==4.0.1 ; python_version >= "3.10" and python_version < "4.0"
urlextract==1.8.0 ; python_version >= "3.10" and python_version < "4.0" urlextract==1.8.0 ; python_version >= "3.10" and python_version < "4.0"
urllib3==1.26.13 ; python_version >= "3.10" and python_version < "4" urllib3==1.26.13 ; python_version >= "3.10" and python_version < "4"
wasabi==0.10.1 ; python_version >= "3.10" and python_version < "4.0" wasabi==0.10.1 ; python_version >= "3.10" and python_version < "4.0"

View File

@ -4,6 +4,7 @@
href="https://bootswatch.com/5/flatly/bootstrap.css" href="https://bootswatch.com/5/flatly/bootstrap.css"
type="text/css" type="text/css"
/> />
{% endblock %} {% block bootstrap_navbar_variant %}{% endblock %} {% block branding %} {% endblock %} {% block bootstrap_navbar_variant %}{% endblock %} {% block
branding %}
<h2><a href="">Internet-ML's NLP Question Answering</a></h2> <h2><a href="">Internet-ML's NLP Question Answering</a></h2>
{% endblock %} {% endblock %}

View File

@ -37,8 +37,9 @@ class QAView(APIView):
""" """
answer = internet_ml.NLP.no_context.QA.answer( answer = internet_ml.NLP.no_context.QA.answer(
str(request.data["question"]), str(request.data["question"]),
str(os.environ.get("INTERNET_ML_GOOGLE_API")), str(os.environ.get("GOOGLE_SEARCH_API_KEY")),
str(os.environ.get("INTERNET_ML_GOOGLE_SEARCH_ENGINE_ID")), str(os.environ.get("GOOGLE_SEARCH_ENGINE_ID")),
str(os.environ.get("OPENAI_API_KEY")),
) )
content = { content = {
"error": "", "error": "",

View File

@ -789,14 +789,14 @@ testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packag
[[package]] [[package]]
name = "internet-ml" name = "internet-ml"
version = "2.0.0" version = "2.0.1"
description = "Internet-ML: Allowing ML to connect to the internet" description = "Internet-ML: Allowing ML to connect to the internet"
category = "main" category = "main"
optional = false optional = false
python-versions = ">=3.10,<4.0" python-versions = ">=3.10,<4.0"
files = [ files = [
{file = "internet_ml-2.0.0-py3-none-any.whl", hash = "sha256:4ee61fbe476a75194bfdd7f1a95b1e29f16d958e9e8a51074566a41a916a86a4"}, {file = "internet_ml-2.0.1-py3-none-any.whl", hash = "sha256:5cf68675a597e1bc30ae4efd2eb4932af95f140d565af2c23407fa5e022915af"},
{file = "internet_ml-2.0.0.tar.gz", hash = "sha256:619b10cd7e00b0068c6217a2fc5b1c80f7a57a2a56190d65483a895b4042df5b"}, {file = "internet_ml-2.0.1.tar.gz", hash = "sha256:51c0c8e77d2e392eb8cb0c9942084d7bf197235a1e8520650bd3a29a9d0f0831"},
] ]
[package.dependencies] [package.dependencies]
@ -807,6 +807,7 @@ datasets = ">=2.8.0,<3.0.0"
diffusers = ">=0.11.1,<0.12.0" diffusers = ">=0.11.1,<0.12.0"
nltk = ">=3.8,<4.0" nltk = ">=3.8,<4.0"
numpy = ">=1.24.0,<2.0.0" numpy = ">=1.24.0,<2.0.0"
openai = ">=0.26.0,<0.27.0"
pandas = ">=1.5.2,<2.0.0" pandas = ">=1.5.2,<2.0.0"
python-dotenv = ">=0.21.0,<0.22.0" python-dotenv = ">=0.21.0,<0.22.0"
requests = ">=2.28.1,<3.0.0" requests = ">=2.28.1,<3.0.0"
@ -1258,6 +1259,28 @@ files = [
setuptools = "*" setuptools = "*"
wheel = "*" wheel = "*"
[[package]]
name = "openai"
version = "0.26.0"
description = "Python client library for the OpenAI API"
category = "main"
optional = false
python-versions = ">=3.7.1"
files = [
{file = "openai-0.26.0.tar.gz", hash = "sha256:e5e8ef9a232be844ae9b2b443389ef4189ce56af70bfe4bee77fbaba4bbd504b"},
]
[package.dependencies]
aiohttp = "*"
requests = ">=2.20"
tqdm = "*"
[package.extras]
datalib = ["numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
dev = ["black (>=21.6b0,<22.0)", "pytest (>=6.0.0,<7.0.0)", "pytest-asyncio", "pytest-mock"]
embeddings = ["matplotlib", "numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)", "plotly", "scikit-learn (>=1.0.2)", "sklearn", "tenacity (>=8.0.1)"]
wandb = ["numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)", "wandb"]
[[package]] [[package]]
name = "packaging" name = "packaging"
version = "22.0" version = "22.0"

View File

@ -1,5 +1,5 @@
<template lang="pug"> <template lang="pug">
div(class="lds-ring") .lds-ring
div div
div div
div div

View File

@ -2,5 +2,4 @@
// const mobileOpen = useState('mobileOpen', () => false) // const mobileOpen = useState('mobileOpen', () => false)
</script> </script>
<template lang="pug"> <template lang="pug"></template>
</template>

View File

@ -1,117 +1 @@
<template lang="pug"> <!-- TODO: Accutally finish this -->
div(v-if="loading")
.flex.items-center.justify-center.h-screen(v-if="loading")
LoadingWheel
div(v-else)
.grid.grid-cols-1.place-content-center.align-middle.gap-y-5.h-screen.text-center
.grid.grid-cols-1.place-self-center
h1.text-5xl
a.url(href="/NLP") Internet-NLP
h1.text-2xl
a.url(href="/NLP/no-context/question-answering") Question Answering No Context
h1.text-2xl
a.url(href="/") Part of Internet-ML
.container.mx-auto
form(v-if="!loading", @submit.prevent="submitForm")
label.block.font-bold.text-lg.mb-2(for="question") Question
textarea#question.border.rounded.w-full.py-2.px-3(
v-model="form.question",
rows="5"
)
button.bg-gray-500.text-white.font-bold.py-2.px-4.rounded(
class="hover:bg-gray-700",
type="submit",
v-if="form.submitCounter < 600"
)
| Submit
p.bg-red-500(v-if="form.submitCounter >= 600") Please wait for sometime as you have made 600 requests within one hour.
.mt-4.font-bold(v-if="answer !== null")
p Question: {{ tmpquestion }}
p.typed-text Answer: {{ typedAnswer }}
p Urls:
ul(v-if="typedAnswer.length === answer.length && urls !== null")
li(v-for="url in urls", :key="url")
span -
a.url(:href="url") {{ url }}
</template>
<script>
import LoadingWheel from '@/components/LoadingWheel.vue'
export default {
components: { LoadingWheel },
data() {
return {
form: {
question: '',
submitCounter: 0
},
answer: null,
loading: false,
typedAnswer: '',
urls: null
}
},
created() {
setInterval(() => {
this.form.submitCounter = 0
}, 3600000)
},
methods: {
resetForm() {
this.form.question = ''
this.answer = null
this.loading = false
this.typedAnswer = ''
this.urls = null
},
async submitForm() {
this.form.submitCounter++
this.loading = true
try {
const { data } = await this.$axios.post(
'https://internet-ml-backend.paas.thamognya.com/api/nlp/no-context/question-answering/',
{
question: this.form.question
}
)
this.tmpquestion = this.form.question
this.resetForm()
this.answer = data.response.answer
const typingInterval = setInterval(() => {
this.typedAnswer +=
this.answer[this.typedAnswer.length] || ''
if (this.typedAnswer.length === this.answer.length) {
clearInterval(typingInterval)
}
}, 150)
this.urls = data.resources
} catch (error) {
// console.error(error)
}
}
}
}
</script>
<style lang="scss">
.typed-text {
position: relative;
display: inline;
}
.typed-text::after {
content: '|';
animation: blink 0.7s infinite;
}
@keyframes blink {
0% {
opacity: 1;
}
100% {
opacity: 0;
}
}
</style>

View File

@ -1,3 +1,3 @@
#!/bin/sh #!/bin/sh
concurrently "cd backend && ./manage.py runserver localhost:8080" "cd frontend && yarn build && yarn start" concurrently "cd backend && ./manage.py runserver 0.0.0.0:8080" "cd frontend && yarn build && yarn start"