Upload political_subs.py

political_subs.py

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+import pandas as pd
+import datasets
+from datasets import Features, Value, ClassLabel, Image, DownloadManager
+import logging
+import requests
+from PIL.Image import Image as PIL_Image
+import io
+
+_URLS = {
+    "my_data": "https://drive.google.com/uc?export=download&id=1t7qllYbonoCgNzzh7w9NhmnMZ4pmqERo",
+}
+
+class RedditDataset(datasets.GeneratorBasedBuilder):
+    """A Dataset builder for a DataFrame with Reddit data."""
+
+    VERSION = datasets.Version('1.0.0')
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=("This dataset contains Reddit posts with various attributes."),
+            features=Features({
+                "author": Value("string"),
+                "created_utc": Value("string"),
+                "domain": Value("string"),
+                "title": Value("string"),
+                "selftext": Value("string"),
+                "subreddit": Value("string"),
+                "score": Value("int32"),
+                "num_comments": Value("int32"),
+                "ups": Value("float32"),
+                "downs": Value("float32"),
+                "permalink": Value("string"),
+                "is_self": Value("bool"),
+                "url": Value("string"),
+                "subreddit_subscribers": Value("float32"),
+                "upvote_ratio": Value("float32"),
+                "is_original_content": Value("string"),
+                "media": Value("string"),
+                "selftext_html": Value("string"),
+                "author_flair_text": Value("string"),
+                "link_flair_text": Value("string"),
+                "image": Image(),
+                "image_text": Value("string"),
+            }),
+            supervised_keys=None,
+            homepage='https://www.reddit.com/',
+            citation="",
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+        downloaded_file = dl_manager.download_and_extract(_URLS["my_data"])
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"dataframe_path": downloaded_file}, # light config
+            ),
+        ]
+
+    def _generate_examples(self, dataframe_path): #light config
+        """Yields examples."""
+        df = pd.read_csv(dataframe_path)
+
+        for idx, row in df.iterrows():
+          image_data = None
+          if pd.notna(row['url']):
+              try:
+                  response = requests.get(row['url'])
+                  response.raise_for_status()
+                  image =  PIL_Image.open(io.BytesIO(response.content))
+                  image =  PIL_Image.convert('RGB')
+                  img_byte_arr = io.BytesIO()
+                  image.save(img_byte_arr, format='JPEG')
+                  image_data = img_byte_arr.getvalue()
+              except Exception as e:
+                  logging.exception(f"Could not download or process image from {row['url']}: {e}")
+                  image_data = None
+
+          yield idx, {
+                "author": row["author"],
+                "created_utc": row["created_utc"],
+                "domain": row["domain"] if pd.notna(row["domain"]) else "",
+                "title": row["title"],
+                "selftext": row["selftext"] if pd.notna(row["selftext"]) else "",
+                "subreddit": row["subreddit"],
+                "score": row["score"],
+                "num_comments": row["num_comments"],
+                "ups": row["ups"] if pd.notna(row["ups"]) else 0,
+                "downs": row["downs"] if pd.notna(row["downs"]) else 0,
+                "permalink": row["permalink"],
+                "is_self": row["is_self"],
+                "url": row["url"] if pd.notna(row["url"]) else "",
+                "subreddit_subscribers": row["subreddit_subscribers"] if pd.notna(row["subreddit_subscribers"]) else 0.0,
+                "upvote_ratio": row["upvote_ratio"] if pd.notna(row["upvote_ratio"]) else 0.0,
+                "is_original_content": row["is_original_content"] if pd.notna(row["is_original_content"]) else False,
+                "media": row["media"] if pd.notna(row["media"]) else "",
+                "selftext_html": row["selftext_html"] if pd.notna(row["selftext_html"]) else "",
+                "author_flair_text": row["author_flair_text"] if pd.notna(row["author_flair_text"]) else "",
+                "link_flair_text": row["link_flair_text"] if pd.notna(row["link_flair_text"]) else "",
+                "image": image_data,
+                "image_text": row['image_text'] if pd.notna(row['image_text']) else "",
+            }