id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
168,277 | import json
import os
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Type, TypeVar, Union
from huggingface_hub import ModelHubMixin, hf_hub_download
The provided code snippet includes necessary dependencies for implementing the `prepare_dialogue` functi... | Format example to single- or multi-turn dialogue. |
168,278 | import json
import os
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Type, TypeVar, Union
from huggingface_hub import ModelHubMixin, hf_hub_download
IGNORE_INDEX = -100
The provided code snippet includes necessary dependencies for implementing the `mask... | Masks the user turns of a dialogue from the loss |
168,279 | import os
import re, json
from typing import Dict, List, Any
import requests
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.llms import BaseLLM
from pydantic import BaseModel, Field
from langchain.chains.base import Chain
from langchain.chat_models import ChatOpenAI
f... | null |
168,280 | import streamlit as st
import requests
def initialize_session_state():
if "is_dark_theme" not in st.session_state:
st.session_state.is_dark_theme = False | null |
168,281 | import streamlit as st
import requests
def get_walmart_bot_response(user_input):
endpoint = "http://localhost:5000/walmartbot"
data = {"messages": [user_input]}
response = requests.post(endpoint, json=data)
return response.json() | null |
168,282 | import streamlit as st
import requests
def get_searchgpt_response(user_input):
endpoint = "http://localhost:5000/searchgpt"
reply = requests.post(endpoint, json={"text": user_input})
response = {"messages": [reply.json()]}
return response | null |
168,283 | import streamlit as st
import requests
def display_response(response, user_input, prev_messages):
if "messages" in response:
for message in response["messages"]:
st.text(f"Bot: 💬 {message}")
if "sources" in response:
for source in response["sources"]:
st.text(f"Source:... | null |
168,284 | import streamlit as st
import requests
def apply_theme():
# Check if dark theme is enabled
is_dark_theme = st.session_state.get("is_dark_theme", False)
# Apply the theme based on the user's choice
if is_dark_theme:
st.markdown(
"""
<style>
body {
... | null |
168,285 | import streamlit as st
import requests
def toggle_theme():
st.session_state.is_dark_theme = not st.session_state.is_dark_theme
# Use this to rerun the script when the button is clicked
st.rerun() | null |
168,286 | from search_capabilities import *
from walmart_functions import *
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List
from fastapi.middleware.cors import CORSMiddleware
class ConversationRequest(BaseModel):
messages: List[str]
sales_agent = SalesGPT.from_llm(llm, verbos... | null |
168,287 | from search_capabilities import *
from walmart_functions import *
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List
from fastapi.middleware.cors import CORSMiddleware
class ChatResponse(BaseModel):
text: str
def handle_chat(request: ChatResponse):
try:
# ... | null |
168,288 | import requests
import os
from dotenv import load_dotenv
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from langchain.prompts import MessagesPlaceholder
from langchain.chains.conversation.memory import ConversationBufferWindowMemo... | null |
168,289 | import requests
import os
from dotenv import load_dotenv
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from langchain.prompts import MessagesPlaceholder
from langchain.chains.conversation.memory import ConversationBufferWindowMemo... | null |
168,290 | from fastapi import FastAPI, Depends, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from fastapi import Request
from config import settings
import typing as t
import uvicorn
import os
from qdrant_engine import QdrantIndex
async def root(request: Reque... | null |
168,291 | from fastapi import FastAPI, Depends, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from fastapi import Request
from config import settings
import typing as t
import uvicorn
import os
from qdrant_engine import QdrantIndex
qdrant_index = QdrantIndex(set... | null |
168,292 | from fastapi import FastAPI, Depends, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from fastapi import Request
from config import settings
import typing as t
import uvicorn
import os
from qdrant_engine import QdrantIndex
qdrant_index = QdrantIndex(set... | null |
168,293 | import os
import numpy as np
from llama_index import ServiceContext, VectorStoreIndex, StorageContext, load_index_from_storage
from llama_index.node_parser import SentenceWindowNodeParser, HierarchicalNodeParser, get_leaf_nodes
from llama_index.indices.postprocessor import MetadataReplacementPostProcessor, SentenceTran... | null |
168,294 | import os
import numpy as np
from llama_index import ServiceContext, VectorStoreIndex, StorageContext, load_index_from_storage
from llama_index.node_parser import SentenceWindowNodeParser, HierarchicalNodeParser, get_leaf_nodes
from llama_index.indices.postprocessor import MetadataReplacementPostProcessor, SentenceTran... | null |
168,295 | import os
import numpy as np
from llama_index import ServiceContext, VectorStoreIndex, StorageContext, load_index_from_storage
from llama_index.node_parser import SentenceWindowNodeParser, HierarchicalNodeParser, get_leaf_nodes
from llama_index.indices.postprocessor import MetadataReplacementPostProcessor, SentenceTran... | null |
168,296 | import os
import numpy as np
from llama_index import ServiceContext, VectorStoreIndex, StorageContext, load_index_from_storage
from llama_index.node_parser import SentenceWindowNodeParser, HierarchicalNodeParser, get_leaf_nodes
from llama_index.indices.postprocessor import MetadataReplacementPostProcessor, SentenceTran... | null |
168,297 | import os
import numpy as np
from llama_index import ServiceContext, VectorStoreIndex, StorageContext, load_index_from_storage
from llama_index.node_parser import SentenceWindowNodeParser, HierarchicalNodeParser, get_leaf_nodes
from llama_index.indices.postprocessor import MetadataReplacementPostProcessor, SentenceTran... | null |
168,298 | from setuptools import setup, find_packages
from typing import List
REQUIREMENTS_FILE_NAME = "requirements.txt"
HYPHEN_E_DOT = "-e ."
List = _Alias()
def get_requirements_list()->List[str]:
with open(REQUIREMENTS_FILE_NAME) as requirement_file:
requirement_list = requirement_file.readlines()
requi... | null |
168,299 | from flask import Flask
from video_summarizer.logger import logging
from video_summarizer.exception import CustomException
import os, sys
import logging
logging.basicConfig(filename=log_file_path,
filemode='w',
format='[%(asctime)s] %(name)s - %(levelname)s - %(message)s'... | null |
168,300 | import os
from flask import Flask, render_template, request, send_from_directory
from video_summarizer.components.video_to_subtitle import SubtitleGenerator, Video2SubConfig
from video_summarizer.components.summarize import Summarizer
from video_summarizer.pipeline import run_training_pipeline
import threading
from gtt... | It renders the index.html file in the templates folder Returns: The index.html file is being returned. |
168,301 | import os
from flask import Flask, render_template, request, send_from_directory
from video_summarizer.components.video_to_subtitle import SubtitleGenerator, Video2SubConfig
from video_summarizer.components.summarize import Summarizer
from video_summarizer.pipeline import run_training_pipeline
import threading
from gtt... | The function upload_file() takes in a video file or a video link, transcribes the video, and summarizes the transcript Returns: the transcript and summary text. |
168,302 | import os
from flask import Flask, render_template, request, send_from_directory
from video_summarizer.components.video_to_subtitle import SubtitleGenerator, Video2SubConfig
from video_summarizer.components.summarize import Summarizer
from video_summarizer.pipeline import run_training_pipeline
import threading
from gtt... | null |
168,303 | import os
from flask import Flask, render_template, request, send_from_directory
from video_summarizer.components.video_to_subtitle import SubtitleGenerator, Video2SubConfig
from video_summarizer.components.summarize import Summarizer
from video_summarizer.pipeline import run_training_pipeline
import threading
from gtt... | null |
168,304 | import sys
from datetime import timedelta
from typing import Iterator, TextIO
from video_summarizer.exception import CustomException
def format_timestamp(seconds: float, always_include_hours: bool = False):
"""
It takes a float representing a number of seconds, and returns a string representing the same number
... | It takes a transcript and a file object, and writes the transcript to the file in SRT format -> SubRip subTitle Args: transcript (Iterator[dict]): Iterator[dict] file (TextIO): The file to write the transcript to. |
168,305 | from flask import Flask
from video_summarizer.logger import logging
import logging
logging.basicConfig(filename=log_file_path,
filemode='w',
format='[%(asctime)s] %(name)s - %(levelname)s - %(message)s',
level=logging.INFO)
def index():
logging.in... | null |
168,306 | from fastapi import FastAPI, APIRouter
async def root():
return {"message": "Hello World"} | null |
168,307 | import secrets
import pandas as pd
from typing import Callable, Annotated, Union
from fastapi import FastAPI, APIRouter, Depends, HTTPException, status, Request, Body, Response
from fastapi.responses import JSONResponse
from fastapi.security import HTTPBasic, HTTPBasicCredentials, OAuth2PasswordBearer, OAuth2PasswordRe... | null |
168,308 | import secrets
import pandas as pd
from typing import Callable, Annotated, Union
from fastapi import FastAPI, APIRouter, Depends, HTTPException, status, Request, Body, Response
from fastapi.responses import JSONResponse
from fastapi.security import HTTPBasic, HTTPBasicCredentials, OAuth2PasswordBearer, OAuth2PasswordRe... | null |
168,309 | import secrets
import pandas as pd
from typing import Callable, Annotated, Union
from fastapi import FastAPI, APIRouter, Depends, HTTPException, status, Request, Body, Response
from fastapi.responses import JSONResponse
from fastapi.security import HTTPBasic, HTTPBasicCredentials, OAuth2PasswordBearer, OAuth2PasswordRe... | null |
168,310 | from datetime import datetime, timedelta
from typing import Any, Union, Annotated
from fastapi import Depends, HTTPException, status
from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
from jose import jwt, ExpiredSignatureError, JWTError
from passlib.context import CryptContext
from app.schema... | null |
168,311 | from datetime import datetime, timedelta
from typing import Any, Union, Annotated
from fastapi import Depends, HTTPException, status
from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
from jose import jwt, ExpiredSignatureError, JWTError
from passlib.context import CryptContext
from app.schema... | null |
168,312 | from typing import Callable, Any, List, Optional
import json
from fastapi import FastAPI, APIRouter, Depends, HTTPException, status, Request, Body, Response
from fastapi.routing import APIRoute
from fastapi.exceptions import RequestValidationError
from app.handlers import log_database_handler
import logging
def parse_e... | Translates list of raw errors (instances) into list of dicts with name/msg :param raw_errors: List with instances of raw error :return: List of dicts (1 dict for every raw error) |
168,313 | import pyodbc
import pandas as pd
from app.core.config import settings
def convert_result2dict(cursor):
try:
result = []
columns = [column[0] for column in cursor.description]
for row in cursor.fetchall():
result.append(dict(zip(columns,row)))
#print(result)
#... | null |
168,314 | import streamlit as st
from stmol import showmol
import py3Dmol
import requests
import biotite.structure.io as bsio
st.sidebar.title('🎈 ESMFold')
st.sidebar.write('[*ESMFold*](https://esmatlas.com/about) is an end-to-end single sequence protein structure predictor based on the ESM-2 language model. For more informatio... | null |
168,315 | import langchain
import os
import streamlit as st
import requests
import sounddevice as sd
import wavio
import openai
from openai import OpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.prompts import HumanMessagePromptTemplate
from langchain.schema.me... | null |
168,316 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,317 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,318 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,319 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,320 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,321 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,322 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,323 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,324 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,325 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,326 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,327 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,328 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,329 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,330 | from os import listdir
from numpy import array
from keras.models import Model
from pickle import dump
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad... | null |
168,331 | import streamlit as st
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.applications.vgg16 import VGG16
from te... | null |
168,332 | import streamlit as st
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.applications.vgg16 import VGG16
from te... | null |
168,333 | import streamlit as st
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.applications.vgg16 import VGG16
from te... | null |
168,334 | from flask import Flask, render_template, request
import pandas as pd
import numpy as np
import sklearn
import os
import pickle
import warnings
def home():
return render_template('home.html') | null |
168,335 | from flask import Flask, render_template, request
import pandas as pd
import numpy as np
import sklearn
import os
import pickle
import warnings
loaded_model = pickle.load(open("model.pkl", 'rb'))
def predict():
N = int(request.form['Nitrogen'])
P = int(request.form['Phosporus'])
K = int(request.form['Potas... | null |
168,336 | from src.ingest_data import ingest_data
from src.preprocessing import data_preprocessing
from src.hyperparameters import search_hyperparameters
from catboost import CatBoostClassifier
import pickle
def ingest_data()-> pd.DataFrame:
data = pd.read_csv("E:\dl\Breast-Cancer-Survival-Prediction\data\data.csv")
ret... | null |
168,337 | import logging
from typing import Optional
Optional: _SpecialForm = ...
def get_console_logger(name:Optional[str]='project') -> logging.Logger:
logger = logging.getLogger(name)
if not logger.handlers:
logger.setLevel(logging.DEBUG)
console_handler = logging.StreamHandler()
console_hand... | null |
168,338 | from flask import Flask, render_template, request
import openai
def index():
return render_template("index.html") | null |
168,339 | from flask import Flask, render_template, request
import openai
openai.api_key = 'YOUR_API_KEY'
def api():
# Get the message from the POST request
message = request.json.get("message")
# Send the message to OpenAI's API and receive the response
completion = openai.ChatCompletion.create(
m... | null |
168,340 | from fastapi import FastAPI, HTTPException
from fastapi.encoders import jsonable_encoder
from fastapi.responses import JSONResponse
import torch
from transformers import pipeline
from utils_sum import preprocess_transcript, divide_chunks, Item, clean_transcript
import datetime
import logging
import sys
import nltk
impo... | null |
168,341 | import requests
import time
HOST_URL = "localhost"
API_VERSION = "v1.0"
PREDICTION_PORT = 8001
def client_script():
# Prediction Module
try:
st = time.time()
result = requests.post(f'http://{HOST_URL}:{PREDICTION_PORT}/{API_VERSION}/prediction',
headers={'Conten... | null |
168,342 | from fastapi import FastAPI, HTTPException
from fastapi.encoders import jsonable_encoder
from fastapi.responses import JSONResponse
from utils_key import Item, final_processing, over_all_key, divide_chunks, NER_transcript, nounKey_nerKey_summary_chunk, clean_transcript, camel
from keybert import KeyBERT
from keyphrase_... | null |
168,343 | import requests
import time
HOST_URL = "localhost"
API_VERSION = "v1.0"
PREDICTION_PORT = 8001
def client_script():
# Prediction Module
try:
st = time.time()
result = requests.post(f'http://{HOST_URL}:{PREDICTION_PORT}/{API_VERSION}/prediction',
headers={'Conte... | null |
168,344 | from fastapi import FastAPI, HTTPException
from fastapi.encoders import jsonable_encoder
from fastapi.responses import JSONResponse
from utils_topic import Item, get_similarities_model1
from sentence_transformers import SentenceTransformer
import datetime
import logging
import sys
import numpy as np
import ast
import c... | null |
168,345 | import requests
import time
HOST_URL = "localhost"
API_VERSION = "v1.0"
PREDICTION_PORT = 8001
def client_script():
# Prediction Module
try:
st = time.time()
result = requests.post(f'http://{HOST_URL}:{PREDICTION_PORT}/{API_VERSION}/prediction',
headers={'Conten... | null |
168,346 | from fastapi import FastAPI, HTTPException
from fastapi.encoders import jsonable_encoder
from fastapi.responses import JSONResponse
from utils_zero import Item_zeroshot
from transformers import pipeline
import torch
import datetime
import logging
import sys
import ast
import configparser
logging.basicConfig(level = log... | null |
168,347 | import requests
import time
ZERO_URL = "localhost"
API_VERSION = "v1.0"
ZEROSHOT_PORT = 8001
def client_script():
# Prediction Module
try:
st = time.time()
result = requests.post(f'http://{ZERO_URL}:{ZEROSHOT_PORT}/{API_VERSION}/prediction',
headers={'Content-type... | null |
168,348 | import os
import requests
from bs4 import BeautifulSoup
from datetime import datetime
if not os.path.exists(main_directory):
os.makedirs(main_directory, exist_ok=True)
if not os.path.exists(subdirectory_path):
os.makedirs(subdirectory_path, exist_ok=True)
response = requests.get(url)
class BeautifulSoup(Tag):
... | null |
168,349 | import streamlit as st
import requests
import sounddevice as sd
import wavio
from langchain import OpenAI
import os
from openai import OpenAI
def record_audio(filename, duration, fs):
print("Recording audio...")
recording = sd.rec(int(duration * fs), samplerate=fs, channels=2)
sd.wait()
wavio.write(fi... | null |
168,350 | from setuptools import find_packages, setup
from typing import List
HYPEN_E_DOT = "-e ."
List = _Alias()
The provided code snippet includes necessary dependencies for implementing the `get_requirements` function. Write a Python function `def get_requirements(file_path: str) -> List[str]` to solve the following proble... | this function will return the list of requirements |
168,351 | import os
import sys
import datetime
import numpy as np
import pandas as pd
import dill
import pickle
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from src.exception import CustomExcep... | null |
168,352 | import os
import sys
import datetime
import numpy as np
import pandas as pd
import dill
import pickle
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from src.exception import CustomExcep... | null |
168,353 | import os
import sys
import datetime
import numpy as np
import pandas as pd
import dill
import pickle
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from src.exception import CustomExcep... | null |
168,354 | import os
import sys
import datetime
import numpy as np
import pandas as pd
import dill
import pickle
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from src.exception import CustomExcep... | null |
168,355 | import os
import sys
import datetime
import numpy as np
import pandas as pd
import dill
import pickle
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from src.exception import CustomExcep... | null |
168,356 | import os
import sys
import datetime
import numpy as np
import pandas as pd
import dill
import pickle
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from src.exception import CustomExcep... | null |
168,357 | import sys
from src.logger import logging
def error_message_detail(error, error_detail: sys):
_, _, exc_tb = error_detail.exc_info()
file_name = exc_tb.tb_frame.f_code.co_filename
error_message = "Error ocurred in python script name [{0}] line number [{1}] error message [{2}]".format(
file_name, ex... | null |
168,358 | import numpy as np
from flask import Flask, jsonify, request, render_template
from src.pipeline.predict_pipeline import CustomData, PredictRecommendPipeline
from src.pipeline.scraping_pipeline import ImageScrappingPipeline
from math import trunc
from src.logger import logging
import os
def city_arr():
city_set = se... | null |
168,359 | import numpy as np
from flask import Flask, jsonify, request, render_template
from src.pipeline.predict_pipeline import CustomData, PredictRecommendPipeline
from src.pipeline.scraping_pipeline import ImageScrappingPipeline
from math import trunc
from src.logger import logging
import os
def main_arr(city):
loc_set =... | null |
168,360 | import numpy as np
from flask import Flask, jsonify, request, render_template
from src.pipeline.predict_pipeline import CustomData, PredictRecommendPipeline
from src.pipeline.scraping_pipeline import ImageScrappingPipeline
from math import trunc
from src.logger import logging
import os
propType = ['Multistorey Apartmen... | null |
168,361 | import pandas as pd
import numpy as np
from pyscript import Element
from js import document, window
import pickle
import warnings
def get_predictions():
data = {
"ApplicantIncome": document.querySelector("#ApplicantIncome").value,
"CoapplicantIncome": document.querySelector("#CoapplicantInc... | null |
168,362 | import os, sys
from os.path import dirname as up
from utils.common_libraries import *
from utils.constants import *
The provided code snippet includes necessary dependencies for implementing the `load_image_from_url` function. Write a Python function `def load_image_from_url(url: str, new_size: tuple = None)` to solve... | Loads an image from a given URL and optionally resizes it. :param url: The URL of the image to load. :type url: str :param new_size: The new size of the image, if resizing is desired. Defaults to None. :type new_size: tuple, optional :return: The loaded image, possibly resized. :rtype: PIL.Image.Image |
168,363 | import os, sys
from os.path import dirname as up
from utils.common_libraries import *
from utils.constants import *
The provided code snippet includes necessary dependencies for implementing the `authenticate_google_service_account_credentials` function. Write a Python function `def authenticate_google_service_account... | Authenticate with Google using a service account. Reads the Google application credentials from an environment variable and creates a service account credential object. Returns: service_account.Credentials: The service account credentials. Raises: RuntimeError: If authentication or logging of the authentication fails. |
168,364 | import os, sys
from os.path import dirname as up
from utils.common_libraries import *
from utils.constants import *
The provided code snippet includes necessary dependencies for implementing the `configure_google_ai_api` function. Write a Python function `def configure_google_ai_api()` to solve the following problem:
... | Configures the Google AI API with the provided API key. Args: api_key (str): The API key for Google AI API. Returns: The configuration object for Google AI API. |
168,365 | from spacy.lang.en import English
from spacy import displacy
import re
import pandas as pd
import spacy
import random
nlp = English()
nlp.add_pipe(nlp.create_pipe('sentencizer'))
TRAIN_DATA = outer_list
print(outer_list)
for ent in doc.ents:
print(ent.text, ent.start_char, ent.end_char, ent.label_)
spacy.displac... | null |
168,366 | from setuptools import find_packages, setup
def get_requirements(file_path):
# requirements = []
with open(file_path) as requirements_obj:
requirements = requirements_obj.readlines()
requirements = [req.replace("\n","") for req in requirements]
if "-e ." in requirements:
re... | null |
168,367 | import os
import sys
import pickle
import dill
import numpy as np
import pandas as pd
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from src.exception import CustomException
class CustomException(Exception):
def __init__(self, error_message, error_detail: sys):
super... | null |
168,368 | import os
import sys
import pickle
import dill
import numpy as np
import pandas as pd
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from src.exception import CustomException
class CustomException(Exception):
def __init__(self, error_message, error_detail: sys):
super... | null |
168,369 | import os
import sys
import pickle
import dill
import numpy as np
import pandas as pd
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from src.exception import CustomException
class CustomException(Exception):
def __init__(self, error_message, error_detail: sys):
super... | null |
168,370 | import sys
def error_message_details(error, error_details:sys):
# exc_info() returns the information about the error
_,_,exc_tb = error_details.exc_info()
# get file name from the exc_info() function
file_name = exc_tb.tb_frame.f_code.co_filename
# construct the error message to return
error_... | null |
168,371 | from flask import Flask, request, render_template
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from src.pipeline.prediction_pipeline import InputData, PreditctPipeline
def index():
return render_template('index.html') | null |
168,372 | from flask import Flask, request, render_template
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from src.pipeline.prediction_pipeline import InputData, PreditctPipeline
class PreditctPipeline:
def __init__(self):
pass
def predict(self, features):
try:
... | null |
168,373 | import os
from wsgiref import simple_server
from flask import Flask, request, render_template, jsonify
from flask import Response
from flask_cors import CORS, cross_origin
from src.ML_pipelines.stage_03_prediction import prediction
import pickle
from src.training_Validation_Insertion import train_validation
def home()... | null |
168,374 | import os
from wsgiref import simple_server
from flask import Flask, request, render_template, jsonify
from flask import Response
from flask_cors import CORS, cross_origin
from src.ML_pipelines.stage_03_prediction import prediction
import pickle
from src.training_Validation_Insertion import train_validation
model = pic... | null |
168,375 | import os
from wsgiref import simple_server
from flask import Flask, request, render_template, jsonify
from flask import Response
from flask_cors import CORS, cross_origin
from src.ML_pipelines.stage_03_prediction import prediction
import pickle
from src.training_Validation_Insertion import train_validation
class trai... | null |
168,376 | import argparse
from src.application_logging.logger import App_Logger
from src.utils.common_utils import read_params, save_model,find_best_model
import pandas as pd
from pathlib import Path
from sklearn import linear_model
from sklearn import ensemble
import sklearn.svm
from sklearn.tree import DecisionTreeClassifier
i... | Method Name: model_selection_and_tuning Description: Selects a best model from all classification model having best accuracy and auc_roc_score and does hyperparameter tuning Output: Best model selected for each cluster On Failure: Raise Exception Written By: Saurabh Naik Version: 1.0 Revisions: None |
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