Update signals.py

signals.py

@@ -4,7 +4,9 @@ import os
 from typing import List
 import datasets
 import logging
-
+from datetime import datetime, timedelta
+import pandas as pd
+import requests
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
@@ -31,13 +33,13 @@ _LICENSE = ""
 # TODO: Add link to the official dataset URLs here
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URL = "https://github.com/Sayali-pingle/HuggingFace--Traffic-Image-Dataset/blob/main/camera_data.csv"
+# _URL = "https://raw.githubusercontent.com/Sayali-pingle/HuggingFace--Traffic-Image-Dataset/main/camera_data.csv"
 
 # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
 class TrafficImages(datasets.GeneratorBasedBuilder):
     """TODO: Short description of my dataset."""
 
-    #_URLS = _URLS
+    # _URLS = _URLS
     VERSION = datasets.Version("1.1.0")
 
     def _info(self):
@@ -49,7 +51,7 @@ class TrafficImages(datasets.GeneratorBasedBuilder):
                     "camera_id": datasets.Value("string"),
                     "latitude": datasets.Value("float"),
                     "longitude": datasets.Value("float"),
-                    "image_url": datasets.Value("string"),
+                    "image_url": datasets.Image(),
                     "image_metadata": datasets.Value("string")
                 }
             ),
@@ -57,52 +59,70 @@ class TrafficImages(datasets.GeneratorBasedBuilder):
             citation=_CITATION,
         )
     
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-      urls_to_download = self._URL
-      downloaded_files = dl_manager.download_and_extract(urls_to_download)
+    # def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+    #   urls_to_download = self._URL
+    #   downloaded_files = dl_manager.download_and_extract(urls_to_download)
 
-      return [
-          datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-          datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
-      ]
+    #   return [
+    #       datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+    #       datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+    #   ]
       
-    def _generate_examples(self, file_path):
-        # This method will yield examples from your dataset
-        start_date = datetime(2024, 1, 1, 18, 0, 0)
-        end_date = datetime(2024, 1, 31, 19, 0, 0)
-        interval_seconds = 240
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        # The URLs should be the paths to the raw files in the Hugging Face dataset repository
+        urls_to_download = {
+            "csv_file": "https://raw.githubusercontent.com/Sayali-pingle/HuggingFace--Traffic-Image-Dataset/main/camera_data.csv"
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download['csv_file'])
 
-        date_time_strings = [
-            (current_date + timedelta(seconds=seconds)).strftime('%Y-%m-%dT%H:%M:%S+08:00')
-            for current_date in pd.date_range(start=start_date, end=end_date, freq='D')
-            for seconds in range(0, 3600, interval_seconds)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "csv_file_path": downloaded_files,
+                },
+            ),
         ]
+  
+    def _generate_examples(self, csv_file_path):
+        # This method will yield examples from your dataset
+        # start_date = datetime(2024, 1, 1, 18, 0, 0)
+        # end_date = datetime(2024, 1, 2, 19, 0, 0)
+        # interval_seconds = 240
 
-        url = 'https://api.data.gov.sg/v1/transport/traffic-images'
-        camera_data = []
+        # date_time_strings = [
+        #     (current_date + timedelta(seconds=seconds)).strftime('%Y-%m-%dT%H:%M:%S+08:00')
+        #     for current_date in pd.date_range(start=start_date, end=end_date, freq='D')
+        #     for seconds in range(0, 3600, interval_seconds)
+        # ]
 
-        for date_time in date_time_strings:
-            params = {'date_time': date_time}
-            response = requests.get(url, params=params)
+        # url = 'https://api.data.gov.sg/v1/transport/traffic-images'
+        # camera_data = []
 
-            if response.status_code == 200:
-                data = response.json()
-                camera_data.extend([
-                    {
-                        'timestamp': item['timestamp'],
-                        'camera_id': camera['camera_id'],
-                        'latitude': camera['location']['latitude'],
-                        'longitude': camera['location']['longitude'],
-                        'image_url': camera['image'],
-                        'image_metadata': camera['image_metadata']
-                    }
-                    for item in data['items']
-                    for camera in item['cameras']
-                ])
-            else:
-                print(f"Error: {response.status_code}")
+        # for date_time in date_time_strings:
+        #     params = {'date_time': date_time}
+        #     response = requests.get(url, params=params)
 
-        for idx, example in enumerate(camera_data):
+        #     if response.status_code == 200:
+        #         data = response.json()
+        #         camera_data.extend([
+        #             {
+        #                 'timestamp': item['timestamp'],
+        #                 'camera_id': camera['camera_id'],
+        #                 'latitude': camera['location']['latitude'],
+        #                 'longitude': camera['location']['longitude'],
+        #                 'image_url': camera['image'],
+        #                 'image_metadata': camera['image_metadata']
+        #             }
+        #             for item in data['items']
+        #             for camera in item['cameras']
+        #         ])
+        #     else:
+        #         print(f"Error: {response.status_code}")
+
+        camera_data= pd.read_csv(csv_file_path)
+
+        for idx, example in camera_data.iterrows():
             yield idx, {
                 "timestamp": example["timestamp"],
                 "camera_id": example["camera_id"],
@@ -111,4 +131,3 @@ class TrafficImages(datasets.GeneratorBasedBuilder):
                 "image_url": example["image_url"],
                 "image_metadata": example["image_metadata"]
             }
-