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Parent(s):
Duplicate from rishi1985/text-embeding-14-a
Browse files- .gitattributes +34 -0
- Dockerfile +79 -0
- README.md +11 -0
- app.py +117 -0
- config.py +9 -0
- ml.ipynb +456 -0
- package-lock.json +975 -0
- package.json +14 -0
- pyngrok.yml +1 -0
- requirements.txt +115 -0
- temp.py +4 -0
- web.html +60 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.npy filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pkl filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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Dockerfile
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# Use the official Python 3.9 image as the base image
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FROM python:3.9
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# Expose the port
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EXPOSE 7860
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# Keeps Python from generating .pyc files in the container
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ENV PYTHONDONTWRITEBYTECODE=1
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# Turns off buffering for easier container logging
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ENV PYTHONUNBUFFERED=1
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# Set the PYNGROK_CONFIG environment variable
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ENV PYNGROK_CONFIG /tmp/pyngrok.yml
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# Set the NGROK_PATH environment variable to a writable location
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ENV NGROK_PATH /tmp/ngrok
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# Copy requirements.txt into the container
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COPY requirements.txt .
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# Upgrade pip and install the required packages
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RUN pip install --upgrade pip && \
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pip install -r requirements.txt
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# Install sudo and create the necessary directories before copying the files
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RUN apt-get update && \
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apt-get install -y sudo && \
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mkdir -p /code/image
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# Creates a non-root user with an explicit UID and adds permission to access the /code folder
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RUN adduser -u 5678 --disabled-password --gecos "" appuser && \
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usermod -aG sudo appuser && \
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usermod -aG root appuser && \
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chown -R appuser:appuser /code
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# Create the pyngrok bin directory and set the ownership and permissions for appuser
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RUN mkdir -p /usr/local/lib/python3.9/site-packages/pyngrok/bin && \
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chown -R appuser:appuser /usr/local/lib/python3.9/site-packages/pyngrok/bin && \
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chmod -R 777 /usr/local/lib/python3.9/site-packages/pyngrok/bin
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RUN mkdir -p /.ngrok2 && \
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chown -R appuser:appuser /.ngrok2 && \
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chmod -R 777 /.ngrok2
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RUN apt-get update && \
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apt-get install -y curl
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# Set the working directory and copy the files
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WORKDIR /code
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# Set the ownership and permissions for the /code directory and its contents
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RUN chown -R appuser:appuser /code && \
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chmod -R 777 /code
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COPY . /code
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# RUN chown -R appuser:appuser /code/data.csv && \
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# chmod -R 777 /code/data.csv
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# Copy the pyngrok.yml configuration file
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COPY pyngrok.yml /tmp/pyngrok.yml
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# Set the TRANSFORMERS_CACHE environment variable to a cache directory inside /tmp
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ENV TRANSFORMERS_CACHE /tmp/transformers_cache
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ENV TORCH_HOME /tmp/torch_cache
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USER appuser
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# Start the application using pyngrok
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# CMD python main.py
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# Get the public IP address and display it
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RUN curl -s https://api.ipify.org | xargs echo "Public IP:"
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RUN pip install gunicorn
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# Start the Uvicorn server
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# ENTRYPOINT ["python", "main.py"]
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# CMD ["sh", "-c", "python main.py & sleep infinity"]
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
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README.md
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---
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title: text embedding -1
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emoji: 🦀
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colorFrom: indigo
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colorTo: yellow
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sdk: docker
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pinned: false
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duplicated_from: rishi1985/text-embeding-14-a
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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# import firebase_admin
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# from firebase_admin import credentials
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# from firebase_admin import firestore
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import io
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from fastapi import FastAPI, File, UploadFile
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from werkzeug.utils import secure_filename
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# import speech_recognition as sr
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import subprocess
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import os
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import requests
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import random
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import pandas as pd
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from pydub import AudioSegment
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from datetime import datetime
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from datetime import date
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import numpy as np
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# from sklearn.ensemble import RandomForestRegressor
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import shutil
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import json
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# from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
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from pydantic import BaseModel
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from typing import Annotated
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# from transformers import BertTokenizerFast, EncoderDecoderModel
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import torch
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import re
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# from transformers import AutoTokenizer, T5ForConditionalGeneration
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from fastapi import Form
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# from transformers import AutoModelForSequenceClassification
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# from transformers import TFAutoModelForSequenceClassification
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# from transformers import AutoTokenizer, AutoConfig
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import numpy as np
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# from scipy.special import softmax
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from sentence_transformers import SentenceTransformer
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# model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v4_MiniLM-L6')
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model = SentenceTransformer("sentence-transformers/all-roberta-large-v1")
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class Query(BaseModel):
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text: str
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from fastapi import FastAPI, Request, Depends, UploadFile, File
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from fastapi.exceptions import HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import JSONResponse
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# now = datetime.now()
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# UPLOAD_FOLDER = '/files'
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# ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png',
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# 'jpg', 'jpeg', 'gif', 'ogg', 'mp3', 'wav'}
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=['*'],
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allow_credentials=True,
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allow_methods=['*'],
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allow_headers=['*'],
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)
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# cred = credentials.Certificate('key.json')
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# app1 = firebase_admin.initialize_app(cred)
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# db = firestore.client()
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# data_frame = pd.read_csv('data.csv')
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@app.on_event("startup")
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async def startup_event():
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print("on startup")
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@app.post("/")
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async def get_answer(q: Query ):
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text = q.text
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text_e = model.encode(text)
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dict={ }
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c=0
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text_e= text_e.tolist()
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for num in text_e:
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dict[c]= num
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c= c+1
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return dict
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return "hello"
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config.py
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from dotenv import load_dotenv
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import os
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load_dotenv(dotenv_path=".env") # Load environment variables from .env file
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# DATABASE_URL = os.getenv("DATABASE_URL")
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# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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# OCR_API_KEY = os.getenv("OCR_API_KEY")
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NGROK_AUTH_TOKEN = os.getenv("NGROK_AUTH_TOKEN")
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ml.ipynb
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1 |
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8 |
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|
9 |
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|
10 |
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|
11 |
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|
12 |
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|
13 |
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{
|
14 |
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"cell_type": "code",
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16 |
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|
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|
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|
20 |
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|
21 |
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|
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"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_20744\\3250454216.py:2: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
|
23 |
+
" df['hour'] = pd.to_datetime(df['time']).dt.hour\n"
|
24 |
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]
|
25 |
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|
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],
|
27 |
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"source": [
|
28 |
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|
29 |
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|
30 |
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"df['weekday'] = pd.to_datetime(df['date']).dt.weekday\n",
|
31 |
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"\n"
|
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|
33 |
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|
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|
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37 |
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|
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"<div>\n",
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|
57 |
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|
58 |
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|
59 |
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" <th></th>\n",
|
60 |
+
" <th>address</th>\n",
|
61 |
+
" <th>car_num</th>\n",
|
62 |
+
" <th>lat</th>\n",
|
63 |
+
" <th>long</th>\n",
|
64 |
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" <th>time</th>\n",
|
65 |
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" <th>date</th>\n",
|
66 |
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" <th>hour</th>\n",
|
67 |
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" <th>weekday</th>\n",
|
68 |
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" </tr>\n",
|
69 |
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|
70 |
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|
71 |
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|
72 |
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" <th>0</th>\n",
|
73 |
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" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
|
74 |
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" <td>jj</td>\n",
|
75 |
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" <td>88.372527</td>\n",
|
76 |
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" <td>22.580441</td>\n",
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77 |
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" <td>18:48:39</td>\n",
|
78 |
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|
79 |
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|
80 |
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|
81 |
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|
82 |
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|
83 |
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" <th>1</th>\n",
|
84 |
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" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
|
85 |
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" <td>jj</td>\n",
|
86 |
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|
87 |
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|
88 |
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|
89 |
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|
90 |
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|
91 |
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|
92 |
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|
93 |
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|
94 |
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|
95 |
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" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
|
96 |
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" <td>jj</td>\n",
|
97 |
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" <td>88.372527</td>\n",
|
98 |
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|
99 |
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|
100 |
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|
101 |
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|
102 |
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" <td>1</td>\n",
|
103 |
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|
104 |
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" <tr>\n",
|
105 |
+
" <th>3</th>\n",
|
106 |
+
" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
|
107 |
+
" <td>jj</td>\n",
|
108 |
+
" <td>88.372526</td>\n",
|
109 |
+
" <td>22.580458</td>\n",
|
110 |
+
" <td>19:16:08</td>\n",
|
111 |
+
" <td>2023-05-02</td>\n",
|
112 |
+
" <td>19</td>\n",
|
113 |
+
" <td>1</td>\n",
|
114 |
+
" </tr>\n",
|
115 |
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" <tr>\n",
|
116 |
+
" <th>4</th>\n",
|
117 |
+
" <td>15, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
|
118 |
+
" <td>jj</td>\n",
|
119 |
+
" <td>88.372552</td>\n",
|
120 |
+
" <td>22.580432</td>\n",
|
121 |
+
" <td>19:17:15</td>\n",
|
122 |
+
" <td>2023-05-02</td>\n",
|
123 |
+
" <td>19</td>\n",
|
124 |
+
" <td>1</td>\n",
|
125 |
+
" </tr>\n",
|
126 |
+
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|
127 |
+
"</table>\n",
|
128 |
+
"</div>"
|
129 |
+
],
|
130 |
+
"text/plain": [
|
131 |
+
" address car_num lat \n",
|
132 |
+
"0 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372527 \\\n",
|
133 |
+
"1 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372527 \n",
|
134 |
+
"2 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372527 \n",
|
135 |
+
"3 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372526 \n",
|
136 |
+
"4 15, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372552 \n",
|
137 |
+
"\n",
|
138 |
+
" long time date hour weekday \n",
|
139 |
+
"0 22.580441 18:48:39 2023-05-02 18 1 \n",
|
140 |
+
"1 22.580441 18:48:39 2023-05-02 18 1 \n",
|
141 |
+
"2 22.580441 18:50:15 2023-05-02 18 1 \n",
|
142 |
+
"3 22.580458 19:16:08 2023-05-02 19 1 \n",
|
143 |
+
"4 22.580432 19:17:15 2023-05-02 19 1 "
|
144 |
+
]
|
145 |
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},
|
146 |
+
"execution_count": 31,
|
147 |
+
"metadata": {},
|
148 |
+
"output_type": "execute_result"
|
149 |
+
}
|
150 |
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],
|
151 |
+
"source": [
|
152 |
+
"df.head()"
|
153 |
+
]
|
154 |
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},
|
155 |
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{
|
156 |
+
"cell_type": "code",
|
157 |
+
"execution_count": 29,
|
158 |
+
"metadata": {},
|
159 |
+
"outputs": [],
|
160 |
+
"source": [
|
161 |
+
"# df.drop(['time','date','address','car_num'],axis=1,inplace=True)"
|
162 |
+
]
|
163 |
+
},
|
164 |
+
{
|
165 |
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"cell_type": "code",
|
166 |
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"execution_count": 32,
|
167 |
+
"metadata": {},
|
168 |
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169 |
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{
|
170 |
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|
171 |
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|
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|
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|
186 |
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|
187 |
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|
188 |
+
" <tr style=\"text-align: right;\">\n",
|
189 |
+
" <th></th>\n",
|
190 |
+
" <th>address</th>\n",
|
191 |
+
" <th>car_num</th>\n",
|
192 |
+
" <th>lat</th>\n",
|
193 |
+
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|
194 |
+
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|
195 |
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|
196 |
+
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|
197 |
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|
198 |
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|
199 |
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|
200 |
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|
201 |
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|
202 |
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|
203 |
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" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
|
204 |
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" <td>jj</td>\n",
|
205 |
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" <td>88.372527</td>\n",
|
206 |
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|
207 |
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|
208 |
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|
209 |
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|
210 |
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|
211 |
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|
212 |
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|
213 |
+
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|
214 |
+
" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
|
215 |
+
" <td>jj</td>\n",
|
216 |
+
" <td>88.372527</td>\n",
|
217 |
+
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|
218 |
+
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|
219 |
+
" <td>2023-05-02</td>\n",
|
220 |
+
" <td>18</td>\n",
|
221 |
+
" <td>1</td>\n",
|
222 |
+
" </tr>\n",
|
223 |
+
" <tr>\n",
|
224 |
+
" <th>2</th>\n",
|
225 |
+
" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
|
226 |
+
" <td>jj</td>\n",
|
227 |
+
" <td>88.372527</td>\n",
|
228 |
+
" <td>22.580441</td>\n",
|
229 |
+
" <td>18:50:15</td>\n",
|
230 |
+
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|
231 |
+
" <td>18</td>\n",
|
232 |
+
" <td>1</td>\n",
|
233 |
+
" </tr>\n",
|
234 |
+
" <tr>\n",
|
235 |
+
" <th>3</th>\n",
|
236 |
+
" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
|
237 |
+
" <td>jj</td>\n",
|
238 |
+
" <td>88.372526</td>\n",
|
239 |
+
" <td>22.580458</td>\n",
|
240 |
+
" <td>19:16:08</td>\n",
|
241 |
+
" <td>2023-05-02</td>\n",
|
242 |
+
" <td>19</td>\n",
|
243 |
+
" <td>1</td>\n",
|
244 |
+
" </tr>\n",
|
245 |
+
" <tr>\n",
|
246 |
+
" <th>4</th>\n",
|
247 |
+
" <td>15, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
|
248 |
+
" <td>jj</td>\n",
|
249 |
+
" <td>88.372552</td>\n",
|
250 |
+
" <td>22.580432</td>\n",
|
251 |
+
" <td>19:17:15</td>\n",
|
252 |
+
" <td>2023-05-02</td>\n",
|
253 |
+
" <td>19</td>\n",
|
254 |
+
" <td>1</td>\n",
|
255 |
+
" </tr>\n",
|
256 |
+
" </tbody>\n",
|
257 |
+
"</table>\n",
|
258 |
+
"</div>"
|
259 |
+
],
|
260 |
+
"text/plain": [
|
261 |
+
" address car_num lat \n",
|
262 |
+
"0 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372527 \\\n",
|
263 |
+
"1 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372527 \n",
|
264 |
+
"2 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372527 \n",
|
265 |
+
"3 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372526 \n",
|
266 |
+
"4 15, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372552 \n",
|
267 |
+
"\n",
|
268 |
+
" long time date hour weekday \n",
|
269 |
+
"0 22.580441 18:48:39 2023-05-02 18 1 \n",
|
270 |
+
"1 22.580441 18:48:39 2023-05-02 18 1 \n",
|
271 |
+
"2 22.580441 18:50:15 2023-05-02 18 1 \n",
|
272 |
+
"3 22.580458 19:16:08 2023-05-02 19 1 \n",
|
273 |
+
"4 22.580432 19:17:15 2023-05-02 19 1 "
|
274 |
+
]
|
275 |
+
},
|
276 |
+
"execution_count": 32,
|
277 |
+
"metadata": {},
|
278 |
+
"output_type": "execute_result"
|
279 |
+
}
|
280 |
+
],
|
281 |
+
"source": [
|
282 |
+
"df.head()"
|
283 |
+
]
|
284 |
+
},
|
285 |
+
{
|
286 |
+
"cell_type": "code",
|
287 |
+
"execution_count": 34,
|
288 |
+
"metadata": {},
|
289 |
+
"outputs": [],
|
290 |
+
"source": [
|
291 |
+
"x=df[['lat','long','weekday']]\n",
|
292 |
+
"y= df['hour']"
|
293 |
+
]
|
294 |
+
},
|
295 |
+
{
|
296 |
+
"cell_type": "code",
|
297 |
+
"execution_count": 35,
|
298 |
+
"metadata": {},
|
299 |
+
"outputs": [],
|
300 |
+
"source": [
|
301 |
+
"# adress_map={}\n",
|
302 |
+
"# temp=[]\n",
|
303 |
+
"# count=0;\n",
|
304 |
+
"# for i in x['address']:\n",
|
305 |
+
"\n"
|
306 |
+
]
|
307 |
+
},
|
308 |
+
{
|
309 |
+
"cell_type": "code",
|
310 |
+
"execution_count": 36,
|
311 |
+
"metadata": {},
|
312 |
+
"outputs": [
|
313 |
+
{
|
314 |
+
"data": {
|
315 |
+
"text/html": [
|
316 |
+
"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=0)</pre></div></div></div></div></div>"
|
317 |
+
],
|
318 |
+
"text/plain": [
|
319 |
+
"RandomForestRegressor(random_state=0)"
|
320 |
+
]
|
321 |
+
},
|
322 |
+
"execution_count": 36,
|
323 |
+
"metadata": {},
|
324 |
+
"output_type": "execute_result"
|
325 |
+
}
|
326 |
+
],
|
327 |
+
"source": [
|
328 |
+
"from sklearn.ensemble import RandomForestRegressor\n",
|
329 |
+
" \n",
|
330 |
+
"# create regressor object\n",
|
331 |
+
"regressor = RandomForestRegressor(n_estimators=100, random_state=0)\n",
|
332 |
+
"\n",
|
333 |
+
"\n",
|
334 |
+
" \n",
|
335 |
+
"# fit the regressor with x and y data\n",
|
336 |
+
"regressor.fit(x, y)"
|
337 |
+
]
|
338 |
+
},
|
339 |
+
{
|
340 |
+
"cell_type": "code",
|
341 |
+
"execution_count": 37,
|
342 |
+
"metadata": {},
|
343 |
+
"outputs": [
|
344 |
+
{
|
345 |
+
"name": "stdout",
|
346 |
+
"output_type": "stream",
|
347 |
+
"text": [
|
348 |
+
"[18.08 18.08 18.08 18.64 18.97 18.97 18.97 19. 19. 19. 19. 19.\n",
|
349 |
+
" 19. 19. 19. 19. 19. 19. 19. 19.89 19.89 16.99]\n",
|
350 |
+
"0 18\n",
|
351 |
+
"1 18\n",
|
352 |
+
"2 18\n",
|
353 |
+
"3 19\n",
|
354 |
+
"4 19\n",
|
355 |
+
"5 19\n",
|
356 |
+
"6 19\n",
|
357 |
+
"7 19\n",
|
358 |
+
"8 19\n",
|
359 |
+
"9 19\n",
|
360 |
+
"10 19\n",
|
361 |
+
"11 19\n",
|
362 |
+
"12 19\n",
|
363 |
+
"13 19\n",
|
364 |
+
"14 19\n",
|
365 |
+
"15 19\n",
|
366 |
+
"16 19\n",
|
367 |
+
"17 19\n",
|
368 |
+
"18 19\n",
|
369 |
+
"19 20\n",
|
370 |
+
"20 20\n",
|
371 |
+
"21 16\n",
|
372 |
+
"Name: hour, dtype: int32\n"
|
373 |
+
]
|
374 |
+
}
|
375 |
+
],
|
376 |
+
"source": [
|
377 |
+
"print(regressor.predict(x))\n",
|
378 |
+
"print(y)\n"
|
379 |
+
]
|
380 |
+
},
|
381 |
+
{
|
382 |
+
"cell_type": "code",
|
383 |
+
"execution_count": 43,
|
384 |
+
"metadata": {},
|
385 |
+
"outputs": [],
|
386 |
+
"source": [
|
387 |
+
"adress= df['address']\n",
|
388 |
+
"lat= df['lat']\n",
|
389 |
+
"long= df['long']\n",
|
390 |
+
"weekday= df['weekday']\n",
|
391 |
+
"\n",
|
392 |
+
"a1=[]\n",
|
393 |
+
"lat1=[]\n",
|
394 |
+
"long1=[]\n",
|
395 |
+
"weekday1=[]\n",
|
396 |
+
"\n",
|
397 |
+
"\n",
|
398 |
+
"\n",
|
399 |
+
"i =0\n",
|
400 |
+
"\n",
|
401 |
+
"while i<len(adress):\n",
|
402 |
+
" if adress[i] == '40, Vidyasagar St, Machuabazar, Kolkata, West Bengal 700009, India':\n",
|
403 |
+
" a1.append(adress[i])\n",
|
404 |
+
" lat1.append(lat[i])\n",
|
405 |
+
" long1.append(long[i])\n",
|
406 |
+
" weekday1.append(weekday[i])\n",
|
407 |
+
" break\n",
|
408 |
+
" i=i+1\n",
|
409 |
+
"\n",
|
410 |
+
"df2= pd.DataFrame({'lat':lat1,'long':long1,'weekday':weekday1})\n",
|
411 |
+
"\n",
|
412 |
+
"\n",
|
413 |
+
" "
|
414 |
+
]
|
415 |
+
},
|
416 |
+
{
|
417 |
+
"cell_type": "code",
|
418 |
+
"execution_count": 44,
|
419 |
+
"metadata": {},
|
420 |
+
"outputs": [
|
421 |
+
{
|
422 |
+
"name": "stdout",
|
423 |
+
"output_type": "stream",
|
424 |
+
"text": [
|
425 |
+
"[18.08]\n"
|
426 |
+
]
|
427 |
+
}
|
428 |
+
],
|
429 |
+
"source": [
|
430 |
+
"print(regressor.predict(df2))"
|
431 |
+
]
|
432 |
+
}
|
433 |
+
],
|
434 |
+
"metadata": {
|
435 |
+
"kernelspec": {
|
436 |
+
"display_name": "Python 3",
|
437 |
+
"language": "python",
|
438 |
+
"name": "python3"
|
439 |
+
},
|
440 |
+
"language_info": {
|
441 |
+
"codemirror_mode": {
|
442 |
+
"name": "ipython",
|
443 |
+
"version": 3
|
444 |
+
},
|
445 |
+
"file_extension": ".py",
|
446 |
+
"mimetype": "text/x-python",
|
447 |
+
"name": "python",
|
448 |
+
"nbconvert_exporter": "python",
|
449 |
+
"pygments_lexer": "ipython3",
|
450 |
+
"version": "3.8.0"
|
451 |
+
},
|
452 |
+
"orig_nbformat": 4
|
453 |
+
},
|
454 |
+
"nbformat": 4,
|
455 |
+
"nbformat_minor": 2
|
456 |
+
}
|
package-lock.json
ADDED
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package.json
ADDED
@@ -0,0 +1,14 @@
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|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"name": "alcohol",
|
3 |
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"version": "1.0.0",
|
4 |
+
"main": "firebase.js",
|
5 |
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"scripts": {
|
6 |
+
"test": "echo \"Error: no test specified\" && exit 1"
|
7 |
+
},
|
8 |
+
"author": "",
|
9 |
+
"license": "ISC",
|
10 |
+
"dependencies": {
|
11 |
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"firebase": "^9.21.0"
|
12 |
+
},
|
13 |
+
"description": ""
|
14 |
+
}
|
pyngrok.yml
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
ngrok_path: /tmp/ngrok
|
requirements.txt
ADDED
@@ -0,0 +1,115 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
aniso8601
|
2 |
+
asttokens
|
3 |
+
backcall
|
4 |
+
bidict
|
5 |
+
blinker
|
6 |
+
CacheControl
|
7 |
+
cachetools
|
8 |
+
certifi
|
9 |
+
cffi
|
10 |
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charset-normalizer
|
11 |
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click
|
12 |
+
colorama
|
13 |
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comm
|
14 |
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cryptography
|
15 |
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debugpy
|
16 |
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decorator
|
17 |
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dnspython
|
18 |
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docopt
|
19 |
+
email-validator
|
20 |
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executing
|
21 |
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firebase-admin
|
22 |
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Flask
|
23 |
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flask-mongoengine
|
24 |
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Flask-RESTful
|
25 |
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Flask-SocketIO
|
26 |
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Flask-WTF
|
27 |
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google-api-core
|
28 |
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google-api-python-client
|
29 |
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google-auth
|
30 |
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google-auth-httplib2
|
31 |
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google-cloud-core
|
32 |
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google-cloud-firestore
|
33 |
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google-cloud-storage
|
34 |
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google-crc32c
|
35 |
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google-resumable-media
|
36 |
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googleapis-common-protos
|
37 |
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grpcio
|
38 |
+
grpcio-status
|
39 |
+
httplib2
|
40 |
+
idna
|
41 |
+
importlib-metadata
|
42 |
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ipykernel
|
43 |
+
ipython
|
44 |
+
itsdangerous
|
45 |
+
jedi
|
46 |
+
Jinja2
|
47 |
+
joblib
|
48 |
+
jupyter_client
|
49 |
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jupyter_core
|
50 |
+
MarkupSafe
|
51 |
+
matplotlib-inline
|
52 |
+
mongoengine
|
53 |
+
msgpack
|
54 |
+
nest-asyncio
|
55 |
+
numpy
|
56 |
+
packaging
|
57 |
+
pandas
|
58 |
+
parso
|
59 |
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pickleshare
|
60 |
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pipreqs
|
61 |
+
platformdirs
|
62 |
+
prompt-toolkit
|
63 |
+
proto-plus
|
64 |
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protobuf
|
65 |
+
psutil
|
66 |
+
pure-eval
|
67 |
+
pyasn1
|
68 |
+
pyasn1-modules
|
69 |
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pycparser
|
70 |
+
pydub
|
71 |
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Pygments
|
72 |
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PyJWT
|
73 |
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pymongo
|
74 |
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pyparsing
|
75 |
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python-dateutil
|
76 |
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python-engineio
|
77 |
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python-socketio
|
78 |
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pytz
|
79 |
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pyzmq
|
80 |
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requests
|
81 |
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rsa
|
82 |
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scikit-learn
|
83 |
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scipy
|
84 |
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six
|
85 |
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sockets
|
86 |
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SpeechRecognition
|
87 |
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stack-data
|
88 |
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threadpoolctl
|
89 |
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tornado
|
90 |
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traitlets
|
91 |
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typing_extensions
|
92 |
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tzdata
|
93 |
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uritemplate
|
94 |
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urllib3
|
95 |
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wcwidth
|
96 |
+
Werkzeug
|
97 |
+
WTForms
|
98 |
+
yarg
|
99 |
+
zipp
|
100 |
+
gunicorn
|
101 |
+
pyngrok
|
102 |
+
python-dotenv
|
103 |
+
uvicorn
|
104 |
+
fastapi
|
105 |
+
transformers
|
106 |
+
tensorflow
|
107 |
+
pydantic
|
108 |
+
typing
|
109 |
+
torch
|
110 |
+
python-multipart
|
111 |
+
sentence_transformers
|
112 |
+
|
113 |
+
|
114 |
+
|
115 |
+
|
temp.py
ADDED
@@ -0,0 +1,4 @@
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|
|
|
1 |
+
import pandas as pd
|
2 |
+
|
3 |
+
pd = pd.DataFrame({'address':[], 'car_num': [], 'lat': [], 'long': [], 'time': [], 'date': []})
|
4 |
+
pd.to_csv('data.csv', index=False)
|
web.html
ADDED
@@ -0,0 +1,60 @@
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
<!DOCTYPE html>
|
2 |
+
<html>
|
3 |
+
<head>
|
4 |
+
<title>API Demo</title>
|
5 |
+
</head>
|
6 |
+
<body>
|
7 |
+
<h1>API Demo</h1>
|
8 |
+
<label for="day">Select a day:</label>
|
9 |
+
<select id="day">
|
10 |
+
<option value="monday">Monday</option>
|
11 |
+
<option value="tuesday">Tuesday</option>
|
12 |
+
<option value="wednesday">Wednesday</option>
|
13 |
+
<option value="thursday">Thursday</option>
|
14 |
+
<option value="friday">Friday</option>
|
15 |
+
<option value="saturday">Saturday</option>
|
16 |
+
<option value="sunday">Sunday</option>
|
17 |
+
</select>
|
18 |
+
<label for="data">Select data:</label>
|
19 |
+
<select id="data" name="data"></select>
|
20 |
+
<br><br>
|
21 |
+
<input type="button" value="Submit" onclick="fetchData()">
|
22 |
+
|
23 |
+
<!-- <br><br>
|
24 |
+
<button onclick="fetchAPI()">Submit</button>
|
25 |
+
<br><br> -->
|
26 |
+
<div id="result"></div>
|
27 |
+
<script>
|
28 |
+
function fetchData() {
|
29 |
+
const day = document.getElementById("day").value;
|
30 |
+
const dataSelect = document.getElementById("data");
|
31 |
+
const resultDiv = document.getElementById("result");
|
32 |
+
|
33 |
+
// Clear previous results
|
34 |
+
resultDiv.innerHTML = "";
|
35 |
+
|
36 |
+
// Make API request
|
37 |
+
fetch(`https://api.example.com/data/${day}`)
|
38 |
+
.then(response => response.json())
|
39 |
+
.then(data => {
|
40 |
+
// Populate data dropdown
|
41 |
+
dataSelect.innerHTML = "";
|
42 |
+
data.forEach(item => {
|
43 |
+
const option = document.createElement("option");
|
44 |
+
option.value = item.value;
|
45 |
+
option.textContent = item.label;
|
46 |
+
dataSelect.appendChild(option);
|
47 |
+
});
|
48 |
+
|
49 |
+
// Show fetched data
|
50 |
+
resultDiv.innerHTML = `Data for ${day}: ${JSON.stringify(data)}`;
|
51 |
+
})
|
52 |
+
.catch(error => {
|
53 |
+
console.error(error);
|
54 |
+
resultDiv.innerHTML = "Error fetching data.";
|
55 |
+
});
|
56 |
+
}
|
57 |
+
|
58 |
+
</script>
|
59 |
+
</body>
|
60 |
+
</html>
|