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Duplicate from rishi1985/text-embeding-11-a

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Files changed (12) hide show
  1. .gitattributes +34 -0
  2. Dockerfile +79 -0
  3. README.md +11 -0
  4. app.py +117 -0
  5. config.py +9 -0
  6. ml.ipynb +456 -0
  7. package-lock.json +975 -0
  8. package.json +14 -0
  9. pyngrok.yml +1 -0
  10. requirements.txt +115 -0
  11. temp.py +4 -0
  12. web.html +60 -0
.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ckpt filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.npy filter=lfs diff=lfs merge=lfs -text
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+ *.npz filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet 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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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.wasm filter=lfs diff=lfs merge=lfs -text
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+ *.xz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zst filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
Dockerfile ADDED
@@ -0,0 +1,79 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Use the official Python 3.9 image as the base image
2
+ FROM python:3.9
3
+
4
+ # Expose the port
5
+ EXPOSE 7860
6
+
7
+ # Keeps Python from generating .pyc files in the container
8
+ ENV PYTHONDONTWRITEBYTECODE=1
9
+
10
+ # Turns off buffering for easier container logging
11
+ ENV PYTHONUNBUFFERED=1
12
+
13
+ # Set the PYNGROK_CONFIG environment variable
14
+ ENV PYNGROK_CONFIG /tmp/pyngrok.yml
15
+
16
+ # Set the NGROK_PATH environment variable to a writable location
17
+ ENV NGROK_PATH /tmp/ngrok
18
+
19
+ # Copy requirements.txt into the container
20
+ COPY requirements.txt .
21
+
22
+ # Upgrade pip and install the required packages
23
+ RUN pip install --upgrade pip && \
24
+ pip install -r requirements.txt
25
+
26
+ # Install sudo and create the necessary directories before copying the files
27
+ RUN apt-get update && \
28
+ apt-get install -y sudo && \
29
+ mkdir -p /code/image
30
+
31
+ # Creates a non-root user with an explicit UID and adds permission to access the /code folder
32
+ RUN adduser -u 5678 --disabled-password --gecos "" appuser && \
33
+ usermod -aG sudo appuser && \
34
+ usermod -aG root appuser && \
35
+ chown -R appuser:appuser /code
36
+
37
+ # Create the pyngrok bin directory and set the ownership and permissions for appuser
38
+ RUN mkdir -p /usr/local/lib/python3.9/site-packages/pyngrok/bin && \
39
+ chown -R appuser:appuser /usr/local/lib/python3.9/site-packages/pyngrok/bin && \
40
+ chmod -R 777 /usr/local/lib/python3.9/site-packages/pyngrok/bin
41
+
42
+ RUN mkdir -p /.ngrok2 && \
43
+ chown -R appuser:appuser /.ngrok2 && \
44
+ chmod -R 777 /.ngrok2
45
+
46
+ RUN apt-get update && \
47
+ apt-get install -y curl
48
+
49
+ # Set the working directory and copy the files
50
+ WORKDIR /code
51
+
52
+ # Set the ownership and permissions for the /code directory and its contents
53
+ RUN chown -R appuser:appuser /code && \
54
+ chmod -R 777 /code
55
+
56
+ COPY . /code
57
+
58
+ # RUN chown -R appuser:appuser /code/data.csv && \
59
+ # chmod -R 777 /code/data.csv
60
+
61
+ # Copy the pyngrok.yml configuration file
62
+ COPY pyngrok.yml /tmp/pyngrok.yml
63
+
64
+ # Set the TRANSFORMERS_CACHE environment variable to a cache directory inside /tmp
65
+ ENV TRANSFORMERS_CACHE /tmp/transformers_cache
66
+ ENV TORCH_HOME /tmp/torch_cache
67
+
68
+ USER appuser
69
+
70
+ # Start the application using pyngrok
71
+ # CMD python main.py
72
+ # Get the public IP address and display it
73
+ RUN curl -s https://api.ipify.org | xargs echo "Public IP:"
74
+ RUN pip install gunicorn
75
+
76
+ # Start the Uvicorn server
77
+ # ENTRYPOINT ["python", "main.py"]
78
+ # CMD ["sh", "-c", "python main.py & sleep infinity"]
79
+ CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
README.md ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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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
7
+ pinned: false
8
+ duplicated_from: rishi1985/text-embeding-11-a
9
+ ---
10
+
11
+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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1
+ # import firebase_admin
2
+ # from firebase_admin import credentials
3
+ # from firebase_admin import firestore
4
+ import io
5
+ from fastapi import FastAPI, File, UploadFile
6
+ from werkzeug.utils import secure_filename
7
+ # import speech_recognition as sr
8
+ import subprocess
9
+ import os
10
+ import requests
11
+ import random
12
+ import pandas as pd
13
+ from pydub import AudioSegment
14
+ from datetime import datetime
15
+ from datetime import date
16
+ import numpy as np
17
+ # from sklearn.ensemble import RandomForestRegressor
18
+ import shutil
19
+ import json
20
+ # from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
21
+ from pydantic import BaseModel
22
+ from typing import Annotated
23
+ # from transformers import BertTokenizerFast, EncoderDecoderModel
24
+ import torch
25
+ import re
26
+ # from transformers import AutoTokenizer, T5ForConditionalGeneration
27
+ from fastapi import Form
28
+ # from transformers import AutoModelForSequenceClassification
29
+ # from transformers import TFAutoModelForSequenceClassification
30
+ # from transformers import AutoTokenizer, AutoConfig
31
+ import numpy as np
32
+ # from scipy.special import softmax
33
+ from sentence_transformers import SentenceTransformer
34
+
35
+
36
+
37
+
38
+ # model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v4_MiniLM-L6')
39
+ model = SentenceTransformer("sentence-transformers/all-roberta-large-v1")
40
+
41
+
42
+
43
+
44
+
45
+ class Query(BaseModel):
46
+ text: str
47
+
48
+
49
+
50
+
51
+ from fastapi import FastAPI, Request, Depends, UploadFile, File
52
+ from fastapi.exceptions import HTTPException
53
+ from fastapi.middleware.cors import CORSMiddleware
54
+ from fastapi.responses import JSONResponse
55
+
56
+
57
+ # now = datetime.now()
58
+
59
+
60
+ # UPLOAD_FOLDER = '/files'
61
+ # ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png',
62
+ # 'jpg', 'jpeg', 'gif', 'ogg', 'mp3', 'wav'}
63
+
64
+
65
+ app = FastAPI()
66
+
67
+ app.add_middleware(
68
+ CORSMiddleware,
69
+ allow_origins=['*'],
70
+ allow_credentials=True,
71
+ allow_methods=['*'],
72
+ allow_headers=['*'],
73
+ )
74
+
75
+
76
+ # cred = credentials.Certificate('key.json')
77
+ # app1 = firebase_admin.initialize_app(cred)
78
+ # db = firestore.client()
79
+ # data_frame = pd.read_csv('data.csv')
80
+
81
+
82
+
83
+ @app.on_event("startup")
84
+ async def startup_event():
85
+ print("on startup")
86
+
87
+
88
+
89
+
90
+ @app.post("/")
91
+ async def get_answer(q: Query ):
92
+
93
+ text = q.text
94
+ text_e = model.encode(text)
95
+
96
+
97
+ dict={ }
98
+
99
+ c=0
100
+ text_e= text_e.tolist()
101
+
102
+ for num in text_e:
103
+ dict[c]= num
104
+ c= c+1
105
+
106
+
107
+
108
+ return dict
109
+
110
+
111
+
112
+
113
+
114
+ return "hello"
115
+
116
+
117
+
config.py ADDED
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1
+ from dotenv import load_dotenv
2
+ import os
3
+
4
+ load_dotenv(dotenv_path=".env") # Load environment variables from .env file
5
+
6
+ # DATABASE_URL = os.getenv("DATABASE_URL")
7
+ # OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
8
+ # OCR_API_KEY = os.getenv("OCR_API_KEY")
9
+ NGROK_AUTH_TOKEN = os.getenv("NGROK_AUTH_TOKEN")
ml.ipynb ADDED
@@ -0,0 +1,456 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 2,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "import pandas as pd \n",
10
+ "import numpy as np\n"
11
+ ]
12
+ },
13
+ {
14
+ "cell_type": "code",
15
+ "execution_count": 30,
16
+ "metadata": {},
17
+ "outputs": [
18
+ {
19
+ "name": "stderr",
20
+ "output_type": "stream",
21
+ "text": [
22
+ "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
+ ]
25
+ }
26
+ ],
27
+ "source": [
28
+ "df= pd.read_csv('data.csv')\n",
29
+ "df['hour'] = pd.to_datetime(df['time']).dt.hour\n",
30
+ "df['weekday'] = pd.to_datetime(df['date']).dt.weekday\n",
31
+ "\n"
32
+ ]
33
+ },
34
+ {
35
+ "cell_type": "code",
36
+ "execution_count": 31,
37
+ "metadata": {},
38
+ "outputs": [
39
+ {
40
+ "data": {
41
+ "text/html": [
42
+ "<div>\n",
43
+ "<style scoped>\n",
44
+ " .dataframe tbody tr th:only-of-type {\n",
45
+ " vertical-align: middle;\n",
46
+ " }\n",
47
+ "\n",
48
+ " .dataframe tbody tr th {\n",
49
+ " vertical-align: top;\n",
50
+ " }\n",
51
+ "\n",
52
+ " .dataframe thead th {\n",
53
+ " text-align: right;\n",
54
+ " }\n",
55
+ "</style>\n",
56
+ "<table border=\"1\" class=\"dataframe\">\n",
57
+ " <thead>\n",
58
+ " <tr style=\"text-align: right;\">\n",
59
+ " <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
+ " <th>time</th>\n",
65
+ " <th>date</th>\n",
66
+ " <th>hour</th>\n",
67
+ " <th>weekday</th>\n",
68
+ " </tr>\n",
69
+ " </thead>\n",
70
+ " <tbody>\n",
71
+ " <tr>\n",
72
+ " <th>0</th>\n",
73
+ " <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
74
+ " <td>jj</td>\n",
75
+ " <td>88.372527</td>\n",
76
+ " <td>22.580441</td>\n",
77
+ " <td>18:48:39</td>\n",
78
+ " <td>2023-05-02</td>\n",
79
+ " <td>18</td>\n",
80
+ " <td>1</td>\n",
81
+ " </tr>\n",
82
+ " <tr>\n",
83
+ " <th>1</th>\n",
84
+ " <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
85
+ " <td>jj</td>\n",
86
+ " <td>88.372527</td>\n",
87
+ " <td>22.580441</td>\n",
88
+ " <td>18:48:39</td>\n",
89
+ " <td>2023-05-02</td>\n",
90
+ " <td>18</td>\n",
91
+ " <td>1</td>\n",
92
+ " </tr>\n",
93
+ " <tr>\n",
94
+ " <th>2</th>\n",
95
+ " <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
96
+ " <td>jj</td>\n",
97
+ " <td>88.372527</td>\n",
98
+ " <td>22.580441</td>\n",
99
+ " <td>18:50:15</td>\n",
100
+ " <td>2023-05-02</td>\n",
101
+ " <td>18</td>\n",
102
+ " <td>1</td>\n",
103
+ " </tr>\n",
104
+ " <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
+ " <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
+ " </tbody>\n",
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
+ },
146
+ "execution_count": 31,
147
+ "metadata": {},
148
+ "output_type": "execute_result"
149
+ }
150
+ ],
151
+ "source": [
152
+ "df.head()"
153
+ ]
154
+ },
155
+ {
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
+ "cell_type": "code",
166
+ "execution_count": 32,
167
+ "metadata": {},
168
+ "outputs": [
169
+ {
170
+ "data": {
171
+ "text/html": [
172
+ "<div>\n",
173
+ "<style scoped>\n",
174
+ " .dataframe tbody tr th:only-of-type {\n",
175
+ " vertical-align: middle;\n",
176
+ " }\n",
177
+ "\n",
178
+ " .dataframe tbody tr th {\n",
179
+ " vertical-align: top;\n",
180
+ " }\n",
181
+ "\n",
182
+ " .dataframe thead th {\n",
183
+ " text-align: right;\n",
184
+ " }\n",
185
+ "</style>\n",
186
+ "<table border=\"1\" class=\"dataframe\">\n",
187
+ " <thead>\n",
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
+ " <th>long</th>\n",
194
+ " <th>time</th>\n",
195
+ " <th>date</th>\n",
196
+ " <th>hour</th>\n",
197
+ " <th>weekday</th>\n",
198
+ " </tr>\n",
199
+ " </thead>\n",
200
+ " <tbody>\n",
201
+ " <tr>\n",
202
+ " <th>0</th>\n",
203
+ " <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
204
+ " <td>jj</td>\n",
205
+ " <td>88.372527</td>\n",
206
+ " <td>22.580441</td>\n",
207
+ " <td>18:48:39</td>\n",
208
+ " <td>2023-05-02</td>\n",
209
+ " <td>18</td>\n",
210
+ " <td>1</td>\n",
211
+ " </tr>\n",
212
+ " <tr>\n",
213
+ " <th>1</th>\n",
214
+ " <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
215
+ " <td>jj</td>\n",
216
+ " <td>88.372527</td>\n",
217
+ " <td>22.580441</td>\n",
218
+ " <td>18:48:39</td>\n",
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
+ " <td>2023-05-02</td>\n",
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",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.8.0"
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+ },
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+ "orig_nbformat": 4
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+ },
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+ "nbformat": 4,
455
+ "nbformat_minor": 2
456
+ }
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package.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "name": "alcohol",
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+ "version": "1.0.0",
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+ "main": "firebase.js",
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+ "scripts": {
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+ "test": "echo \"Error: no test specified\" && exit 1"
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+ },
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+ "author": "",
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+ "license": "ISC",
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+ "dependencies": {
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+ "firebase": "^9.21.0"
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+ },
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+ "description": ""
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+ }
pyngrok.yml ADDED
@@ -0,0 +1 @@
 
 
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requirements.txt ADDED
@@ -0,0 +1,115 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ click
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+ comm
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+ cryptography
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+ decorator
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+ executing
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112
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+
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temp.py ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
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
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+ <!DOCTYPE html>
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+ <html>
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+ <head>
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+ <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>
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+ <label for="data">Select data:</label>
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+ <select id="data" name="data"></select>
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+ <br><br>
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+ <input type="button" value="Submit" onclick="fetchData()">
22
+
23
+ <!-- <br><br>
24
+ <button onclick="fetchAPI()">Submit</button>
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+ <br><br> -->
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+ <div id="result"></div>
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+ <script>
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+ function fetchData() {
29
+ const day = document.getElementById("day").value;
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+ 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}`)
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+ .then(response => response.json())
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+ .then(data => {
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+ // Populate data dropdown
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+ dataSelect.innerHTML = "";
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+ data.forEach(item => {
43
+ const option = document.createElement("option");
44
+ option.value = item.value;
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+ option.textContent = item.label;
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+ dataSelect.appendChild(option);
47
+ });
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+
49
+ // Show fetched data
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+ 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>