Duplicate from rishi1985/text-embeding-11-a

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+# Use the official Python 3.9 image as the base image
+FROM python:3.9
+
+# Expose the port
+EXPOSE 7860
+
+# Keeps Python from generating .pyc files in the container
+ENV PYTHONDONTWRITEBYTECODE=1
+
+# Turns off buffering for easier container logging
+ENV PYTHONUNBUFFERED=1
+
+# Set the PYNGROK_CONFIG environment variable
+ENV PYNGROK_CONFIG /tmp/pyngrok.yml
+
+# Set the NGROK_PATH environment variable to a writable location
+ENV NGROK_PATH /tmp/ngrok
+
+# Copy requirements.txt into the container
+COPY requirements.txt .
+
+# Upgrade pip and install the required packages
+RUN pip install --upgrade pip && \
+    pip install -r requirements.txt
+
+# Install sudo and create the necessary directories before copying the files
+RUN apt-get update && \
+    apt-get install -y sudo && \
+    mkdir -p /code/image
+
+# Creates a non-root user with an explicit UID and adds permission to access the /code folder
+RUN adduser -u 5678 --disabled-password --gecos "" appuser && \
+    usermod -aG sudo appuser && \
+    usermod -aG root appuser && \
+    chown -R appuser:appuser /code
+
+# Create the pyngrok bin directory and set the ownership and permissions for appuser
+RUN mkdir -p /usr/local/lib/python3.9/site-packages/pyngrok/bin && \
+    chown -R appuser:appuser /usr/local/lib/python3.9/site-packages/pyngrok/bin && \
+    chmod -R 777 /usr/local/lib/python3.9/site-packages/pyngrok/bin
+
+RUN mkdir -p /.ngrok2 && \
+    chown -R appuser:appuser /.ngrok2 && \
+    chmod -R 777 /.ngrok2
+
+RUN apt-get update && \
+    apt-get install -y curl
+
+# Set the working directory and copy the files
+WORKDIR /code
+
+# Set the ownership and permissions for the /code directory and its contents
+RUN chown -R appuser:appuser /code && \
+    chmod -R 777 /code
+
+COPY . /code
+
+# RUN chown -R appuser:appuser /code/data.csv && \
+#     chmod -R 777 /code/data.csv
+
+# Copy the pyngrok.yml configuration file
+COPY pyngrok.yml /tmp/pyngrok.yml
+
+# Set the TRANSFORMERS_CACHE environment variable to a cache directory inside /tmp
+ENV TRANSFORMERS_CACHE /tmp/transformers_cache
+ENV TORCH_HOME /tmp/torch_cache
+
+USER appuser
+
+# Start the application using pyngrok
+# CMD python main.py
+# Get the public IP address and display it
+RUN curl -s https://api.ipify.org | xargs echo "Public IP:"
+RUN pip install gunicorn
+
+# Start the Uvicorn server
+# ENTRYPOINT ["python", "main.py"]
+# CMD ["sh", "-c", "python main.py & sleep infinity"]
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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+---
+title: text embedding -1
+emoji: 🦀
+colorFrom: indigo
+colorTo: yellow
+sdk: docker
+pinned: false
+duplicated_from: rishi1985/text-embeding-11-a
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# import firebase_admin
+# from firebase_admin import credentials
+# from firebase_admin import firestore
+import io
+from fastapi import FastAPI, File, UploadFile
+from werkzeug.utils import secure_filename
+# import speech_recognition as sr
+import subprocess
+import os
+import requests
+import random
+import pandas as pd
+from pydub import AudioSegment
+from datetime import datetime
+from datetime import date
+import numpy as np
+# from sklearn.ensemble import RandomForestRegressor
+import shutil
+import json
+# from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+from pydantic import BaseModel
+from typing import Annotated
+# from transformers import BertTokenizerFast, EncoderDecoderModel
+import torch
+import re
+# from transformers import AutoTokenizer, T5ForConditionalGeneration
+from fastapi import Form
+# from transformers import AutoModelForSequenceClassification
+# from transformers import TFAutoModelForSequenceClassification
+# from transformers import AutoTokenizer, AutoConfig
+import numpy as np
+# from scipy.special import softmax
+from sentence_transformers import SentenceTransformer
+
+
+
+
+# model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v4_MiniLM-L6')
+model = SentenceTransformer("sentence-transformers/all-roberta-large-v1")
+
+
+
+
+
+class Query(BaseModel):
+    text: str
+
+   
+
+
+from fastapi import FastAPI, Request, Depends, UploadFile, File
+from fastapi.exceptions import HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+
+
+# now = datetime.now()
+
+
+# UPLOAD_FOLDER = '/files'
+# ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png',
+#                       'jpg', 'jpeg', 'gif', 'ogg', 'mp3', 'wav'}
+
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=['*'],
+    allow_credentials=True,
+    allow_methods=['*'],
+    allow_headers=['*'],
+)
+
+
+# cred = credentials.Certificate('key.json')
+# app1 = firebase_admin.initialize_app(cred)
+# db = firestore.client()
+# data_frame = pd.read_csv('data.csv')
+
+
+
+@app.on_event("startup")
+async def startup_event():
+   print("on startup")
+
+   
+
+
+@app.post("/")
+async def get_answer(q: Query ):
+    
+    text = q.text
+    text_e = model.encode(text)
+    
+    
+    dict={ }
+
+    c=0
+    text_e= text_e.tolist()
+
+    for num in text_e:
+        dict[c]= num
+        c= c+1
+
+    
+    
+    return dict
+
+    
+    
+   
+   
+    return "hello"
+    
+
+

config.py

@@ -0,0 +1,9 @@
+from dotenv import load_dotenv
+import os
+
+load_dotenv(dotenv_path=".env")  # Load environment variables from .env file
+
+# DATABASE_URL = os.getenv("DATABASE_URL")
+# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+# OCR_API_KEY = os.getenv("OCR_API_KEY")
+NGROK_AUTH_TOKEN = os.getenv("NGROK_AUTH_TOKEN")

ml.ipynb

@@ -0,0 +1,456 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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",
+      "  df['hour'] = pd.to_datetime(df['time']).dt.hour\n"
+     ]
+    }
+   ],
+   "source": [
+    "df= pd.read_csv('data.csv')\n",
+    "df['hour'] = pd.to_datetime(df['time']).dt.hour\n",
+    "df['weekday'] = pd.to_datetime(df['date']).dt.weekday\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>address</th>\n",
+       "      <th>car_num</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>time</th>\n",
+       "      <th>date</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>weekday</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
+       "      <td>jj</td>\n",
+       "      <td>88.372527</td>\n",
+       "      <td>22.580441</td>\n",
+       "      <td>18:48:39</td>\n",
+       "      <td>2023-05-02</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
+       "      <td>jj</td>\n",
+       "      <td>88.372527</td>\n",
+       "      <td>22.580441</td>\n",
+       "      <td>18:48:39</td>\n",
+       "      <td>2023-05-02</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
+       "      <td>jj</td>\n",
+       "      <td>88.372527</td>\n",
+       "      <td>22.580441</td>\n",
+       "      <td>18:50:15</td>\n",
+       "      <td>2023-05-02</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
+       "      <td>jj</td>\n",
+       "      <td>88.372526</td>\n",
+       "      <td>22.580458</td>\n",
+       "      <td>19:16:08</td>\n",
+       "      <td>2023-05-02</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
+       "      <td>jj</td>\n",
+       "      <td>88.372552</td>\n",
+       "      <td>22.580432</td>\n",
+       "      <td>19:17:15</td>\n",
+       "      <td>2023-05-02</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             address car_num        lat   \n",
+       "0  40, Vidyasagar St, Machuabazar, Kolkata, West ...      jj  88.372527  \\\n",
+       "1  40, Vidyasagar St, Machuabazar, Kolkata, West ...      jj  88.372527   \n",
+       "2  40, Vidyasagar St, Machuabazar, Kolkata, West ...      jj  88.372527   \n",
+       "3  40, Vidyasagar St, Machuabazar, Kolkata, West ...      jj  88.372526   \n",
+       "4  15, Vidyasagar St, Machuabazar, Kolkata, West ...      jj  88.372552   \n",
+       "\n",
+       "        long      time        date  hour  weekday  \n",
+       "0  22.580441  18:48:39  2023-05-02    18        1  \n",
+       "1  22.580441  18:48:39  2023-05-02    18        1  \n",
+       "2  22.580441  18:50:15  2023-05-02    18        1  \n",
+       "3  22.580458  19:16:08  2023-05-02    19        1  \n",
+       "4  22.580432  19:17:15  2023-05-02    19        1  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df.drop(['time','date','address','car_num'],axis=1,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>address</th>\n",
+       "      <th>car_num</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>time</th>\n",
+       "      <th>date</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>weekday</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
+       "      <td>jj</td>\n",
+       "      <td>88.372527</td>\n",
+       "      <td>22.580441</td>\n",
+       "      <td>18:48:39</td>\n",
+       "      <td>2023-05-02</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
+       "      <td>jj</td>\n",
+       "      <td>88.372527</td>\n",
+       "      <td>22.580441</td>\n",
+       "      <td>18:48:39</td>\n",
+       "      <td>2023-05-02</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
+       "      <td>jj</td>\n",
+       "      <td>88.372527</td>\n",
+       "      <td>22.580441</td>\n",
+       "      <td>18:50:15</td>\n",
+       "      <td>2023-05-02</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
+       "      <td>jj</td>\n",
+       "      <td>88.372526</td>\n",
+       "      <td>22.580458</td>\n",
+       "      <td>19:16:08</td>\n",
+       "      <td>2023-05-02</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
+       "      <td>jj</td>\n",
+       "      <td>88.372552</td>\n",
+       "      <td>22.580432</td>\n",
+       "      <td>19:17:15</td>\n",
+       "      <td>2023-05-02</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             address car_num        lat   \n",
+       "0  40, Vidyasagar St, Machuabazar, Kolkata, West ...      jj  88.372527  \\\n",
+       "1  40, Vidyasagar St, Machuabazar, Kolkata, West ...      jj  88.372527   \n",
+       "2  40, Vidyasagar St, Machuabazar, Kolkata, West ...      jj  88.372527   \n",
+       "3  40, Vidyasagar St, Machuabazar, Kolkata, West ...      jj  88.372526   \n",
+       "4  15, Vidyasagar St, Machuabazar, Kolkata, West ...      jj  88.372552   \n",
+       "\n",
+       "        long      time        date  hour  weekday  \n",
+       "0  22.580441  18:48:39  2023-05-02    18        1  \n",
+       "1  22.580441  18:48:39  2023-05-02    18        1  \n",
+       "2  22.580441  18:50:15  2023-05-02    18        1  \n",
+       "3  22.580458  19:16:08  2023-05-02    19        1  \n",
+       "4  22.580432  19:17:15  2023-05-02    19        1  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=df[['lat','long','weekday']]\n",
+    "y= df['hour']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# adress_map={}\n",
+    "# temp=[]\n",
+    "# count=0;\n",
+    "# for i in x['address']:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<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>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor(random_state=0)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    " \n",
+    "# create regressor object\n",
+    "regressor = RandomForestRegressor(n_estimators=100, random_state=0)\n",
+    "\n",
+    "\n",
+    " \n",
+    "# fit the regressor with x and y data\n",
+    "regressor.fit(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[18.08 18.08 18.08 18.64 18.97 18.97 18.97 19.   19.   19.   19.   19.\n",
+      " 19.   19.   19.   19.   19.   19.   19.   19.89 19.89 16.99]\n",
+      "0     18\n",
+      "1     18\n",
+      "2     18\n",
+      "3     19\n",
+      "4     19\n",
+      "5     19\n",
+      "6     19\n",
+      "7     19\n",
+      "8     19\n",
+      "9     19\n",
+      "10    19\n",
+      "11    19\n",
+      "12    19\n",
+      "13    19\n",
+      "14    19\n",
+      "15    19\n",
+      "16    19\n",
+      "17    19\n",
+      "18    19\n",
+      "19    20\n",
+      "20    20\n",
+      "21    16\n",
+      "Name: hour, dtype: int32\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(regressor.predict(x))\n",
+    "print(y)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adress= df['address']\n",
+    "lat= df['lat']\n",
+    "long= df['long']\n",
+    "weekday= df['weekday']\n",
+    "\n",
+    "a1=[]\n",
+    "lat1=[]\n",
+    "long1=[]\n",
+    "weekday1=[]\n",
+    "\n",
+    "\n",
+    "\n",
+    "i =0\n",
+    "\n",
+    "while i<len(adress):\n",
+    "    if adress[i] == '40, Vidyasagar St, Machuabazar, Kolkata, West Bengal 700009, India':\n",
+    "        a1.append(adress[i])\n",
+    "        lat1.append(lat[i])\n",
+    "        long1.append(long[i])\n",
+    "        weekday1.append(weekday[i])\n",
+    "        break\n",
+    "    i=i+1\n",
+    "\n",
+    "df2= pd.DataFrame({'lat':lat1,'long':long1,'weekday':weekday1})\n",
+    "\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[18.08]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(regressor.predict(df2))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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+      "engines": {
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+}

package.json

@@ -0,0 +1,14 @@
+{
+  "name": "alcohol",
+  "version": "1.0.0",
+  "main": "firebase.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "author": "",
+  "license": "ISC",
+  "dependencies": {
+    "firebase": "^9.21.0"
+  },
+  "description": ""
+}

pyngrok.yml

@@ -0,0 +1 @@
+ngrok_path: /tmp/ngrok

requirements.txt

@@ -0,0 +1,115 @@
+aniso8601
+asttokens
+backcall
+bidict
+blinker
+CacheControl
+cachetools
+certifi
+cffi
+charset-normalizer
+click
+colorama
+comm
+cryptography
+debugpy
+decorator
+dnspython
+docopt
+email-validator
+executing
+firebase-admin
+Flask
+flask-mongoengine
+Flask-RESTful
+Flask-SocketIO
+Flask-WTF
+google-api-core
+google-api-python-client
+google-auth
+google-auth-httplib2
+google-cloud-core
+google-cloud-firestore
+google-cloud-storage
+google-crc32c
+google-resumable-media
+googleapis-common-protos
+grpcio
+grpcio-status
+httplib2
+idna
+importlib-metadata
+ipykernel
+ipython
+itsdangerous
+jedi
+Jinja2
+joblib
+jupyter_client
+jupyter_core
+MarkupSafe
+matplotlib-inline
+mongoengine
+msgpack
+nest-asyncio
+numpy
+packaging
+pandas
+parso
+pickleshare
+pipreqs
+platformdirs
+prompt-toolkit
+proto-plus
+protobuf
+psutil
+pure-eval
+pyasn1
+pyasn1-modules
+pycparser
+pydub
+Pygments
+PyJWT
+pymongo
+pyparsing
+python-dateutil
+python-engineio
+python-socketio
+pytz
+pyzmq
+requests
+rsa
+scikit-learn
+scipy
+six
+sockets
+SpeechRecognition
+stack-data
+threadpoolctl
+tornado
+traitlets
+typing_extensions
+tzdata
+uritemplate
+urllib3
+wcwidth
+Werkzeug
+WTForms
+yarg
+zipp
+gunicorn
+pyngrok
+python-dotenv
+uvicorn
+fastapi
+transformers
+tensorflow
+pydantic
+typing
+torch
+python-multipart
+sentence_transformers
+
+
+
+

temp.py

@@ -0,0 +1,4 @@
+import pandas as pd
+
+pd = pd.DataFrame({'address':[], 'car_num': [], 'lat': [], 'long': [], 'time': [], 'date': []})
+pd.to_csv('data.csv', index=False)

web.html

@@ -0,0 +1,60 @@
+<!DOCTYPE html>
+<html>
+  <head>
+    <title>API Demo</title>
+  </head>
+  <body>
+    <h1>API Demo</h1>
+    <label for="day">Select a day:</label>
+    <select id="day">
+      <option value="monday">Monday</option>
+      <option value="tuesday">Tuesday</option>
+      <option value="wednesday">Wednesday</option>
+      <option value="thursday">Thursday</option>
+      <option value="friday">Friday</option>
+      <option value="saturday">Saturday</option>
+      <option value="sunday">Sunday</option>
+    </select>
+    <label for="data">Select data:</label>
+    <select id="data" name="data"></select>
+    <br><br>
+    <input type="button" value="Submit" onclick="fetchData()">
+
+    <!-- <br><br>
+    <button onclick="fetchAPI()">Submit</button>
+    <br><br> -->
+    <div id="result"></div>
+    <script>
+     function fetchData() {
+    const day = document.getElementById("day").value;
+    const dataSelect = document.getElementById("data");
+    const resultDiv = document.getElementById("result");
+
+    // Clear previous results
+    resultDiv.innerHTML = "";
+
+    // Make API request
+    fetch(`https://api.example.com/data/${day}`)
+        .then(response => response.json())
+        .then(data => {
+            // Populate data dropdown
+            dataSelect.innerHTML = "";
+            data.forEach(item => {
+                const option = document.createElement("option");
+                option.value = item.value;
+                option.textContent = item.label;
+                dataSelect.appendChild(option);
+            });
+
+            // Show fetched data
+            resultDiv.innerHTML = `Data for ${day}: ${JSON.stringify(data)}`;
+        })
+        .catch(error => {
+            console.error(error);
+            resultDiv.innerHTML = "Error fetching data.";
+        });
+}
+
+    </script>
+  </body>
+</html>