update

output_dataset.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:dac518fe3d179e85c74aea8d6a6285047e1679485e8efcaafb06ac0d13e25955
-size 22461062
+oid sha256:ca47ac46bfc9643c362f1e7ff017d807e587320b45e66c147a257421c20cbad6
+size 22455970

par.py

@@ -0,0 +1,71 @@
+import os
+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+import argparse
+import re
+
+def encode_file(file_path):
+    """Read file content as string."""
+    with open(file_path, 'r', encoding='utf-8') as file:
+        return file.read()
+
+def extract_images(markdown_content):
+    """Extract PHOTO_IDs from markdown files and return as a list."""
+    return re.findall(r'\{\{PHOTO_ID:(\d+)\|WIDTH:\d+\}\}', markdown_content)
+
+def collect_data(directory):
+    data = []
+    # Debugging: Print directory content
+    print(f"Directory contents: {os.listdir(directory)}")
+
+    # Create a dictionary to map PHOTO_ID to actual image filenames
+    image_files = {}
+    for filename in os.listdir(directory):
+        if filename.endswith('.jpg'):
+            photo_id_match = re.search(r'(\d+)', filename)
+            if photo_id_match:
+                photo_id = photo_id_match.group(1)
+                image_files[photo_id] = filename
+
+    # Debugging: Print image file mappings
+    print(f"Image Files: {image_files}")
+
+    for filename in os.listdir(directory):
+        problem_id = filename.split('.')[0]
+        row = {'Problem ID': problem_id, 'Images': []}
+        for file in os.listdir(directory):
+            if file.startswith(problem_id):
+                file_type = file.split('.')[-1]
+                file_path = os.path.join(directory, file)
+                if file_type in ['in', 'out', 'sol.md' 'cpp', 'md']:
+                    content = encode_file(file_path)
+                    row[file_type] = content
+                    if file_type in ['md', 'sol.md']:
+                        image_ids = extract_images(content)
+                        # Debugging: Print extracted image IDs
+                        print(f"Extracted Image IDs from {file_path}: {image_ids}")
+                        row['Images'] = [image_files[id] for id in image_ids if id in image_files]
+        data.append(row)
+
+    # Debugging: Print final data before conversion to verify
+    # print(f"Final Data Collected: {data}")
+
+    return data
+
+def create_parquet_file(data, output_file):
+    df = pd.DataFrame(data)
+    table = pa.Table.from_pandas(df)
+    pq.write_table(table, output_file)
+
+def main():
+    parser = argparse.ArgumentParser(description='Convert dataset to Parquet format.')
+    parser.add_argument('directory', type=str, help='Directory containing the dataset files.')
+    parser.add_argument('-o', '--output', type=str, default='output_dataset.parquet', help='Output Parquet file name.')
+    args = parser.parse_args()
+
+    data = collect_data(args.directory)
+    create_parquet_file(data, args.output)
+
+if __name__ == "__main__":
+    main()