yolo

output_dataset.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4d75658b3f088a0b4a93eb5aedd74199e3b8428d132565088b9ed11aee069213
-size 22498564
+oid sha256:b81f2017ea6db55ced797c0fa4045bae4927347ea42dc041d02f1ea6b856616e
+size 22517556

process.py

@@ -34,7 +34,7 @@ def collect_data(directory):
         # Adjust the pattern if problem IDs include characters before "sol"
         problem_id = re.sub(r'sol$', '', mfile.split('.')[0])  # Strip "sol" from end
         if problem_id not in data:
-            data[problem_id] = {'Problem ID': problem_id, 'Images': [], 'in': None, 'out': None, 'cpp': None, 'md': None, 'sol.md': None}
+            data[problem_id] = {'Problem ID': problem_id, 'Images': [], 'in': None, 'out': None, 'cpp': None, 'Problem': None, 'Solution': None}
 
     # Now associate other files with these problem IDs
     for filename in os.listdir(directory):
@@ -44,12 +44,16 @@ def collect_data(directory):
             file_path = os.path.join(directory, filename)
             content = encode_file(file_path) if not filename.endswith('.jpg') else None
 
-            if file_type in ['in', 'out', 'cpp', 'md', 'sol.md']:
+            if file_type in ['in', 'out', 'cpp']:
                 data[problem_id][file_type] = content
-                if file_type in ['md', 'sol.md']:
-                    image_ids = extract_images(content)
-                    data[problem_id]['Images'] += [image_files[id] for id in image_ids if id in image_files]
-                    data[problem_id]['Images'] = list(set(data[problem_id]['Images']))  # Remove duplicates
+            if file_type == "md":
+                if "sol" in filename:
+                    data[problem_id]['Solution'] = content
+                else:
+                    data[problem_id]['Problem'] = content
+                image_ids = extract_images(content)
+                data[problem_id]['Images'] += [image_files[id] for id in image_ids if id in image_files]
+                data[problem_id]['Images'] = list(set(data[problem_id]['Images']))  # Remove duplicates
 
     return list(data.values())
 

viz.py

@@ -8,7 +8,7 @@ df = pd.read_parquet('output_dataset.parquet')
 print(df.columns)
 
 # Display the first few rows of the dataframe
-print(df.loc[0, "cpp"])
+print(df)
 
 
 # Basic statistics for numerical columns