parquet_explorer.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/parquet_explorer.ipynb","timestamp":1754497194668},{"file_id":"https://huggingface.co/datasets/codeShare/chroma_prompts/blob/main/parquet_explorer.ipynb","timestamp":1754475181338},{"file_id":"https://huggingface.co/datasets/codeShare/chroma_prompts/blob/main/parquet_explorer.ipynb","timestamp":1754312448728},{"file_id":"https://huggingface.co/datasets/codeShare/chroma_prompts/blob/main/parquet_explorer.ipynb","timestamp":1754310418707},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1754223895158},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1747490904984},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1740037333374},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1736477078136},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1725365086834}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["Download a parquet file to your Google drive and load it from there into this notebook.\n","\n","Parquet files: https://huggingface.co/datasets/codeShare/chroma_prompts/tree/main"],"metadata":{"id":"LeCfcqgiQvCP"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"HFy5aDxM3G7O","executionInfo":{"status":"ok","timestamp":1754473310268,"user_tz":-120,"elapsed":15144,"user":{"displayName":"","userId":""}},"outputId":"30ed1675-3125-4c5b-b001-bbe71714703b","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["# Step 1: Mount Google Drive\n","#from google.colab import drive\n","#drive.mount('/content/drive')\n","\n","#@markdown paste .parquet file stored on your Google Drive folder to see its characteristics\n","\n","# Step 2: Import required libraries\n","import pandas as pd\n","\n","# Step 3: Define the path to the Parquet file\n","file_path = '/content/drive/MyDrive/Chroma prompts/redcaps_1.parquet' #@param {type:'string'}\n","\n","# Step 4: Read the Parquet file\n","df = pd.read_parquet(file_path)\n","\n","# Step 5: Basic exploration of the Parquet file\n","print(\"First 5 rows of the dataset:\")\n","print(df.head())\n","\n","print(\"\\nDataset Info:\")\n","print(df.info())\n","\n","print(\"\\nBasic Statistics:\")\n","print(df.describe())\n","\n","print(\"\\nColumn Names:\")\n","print(df.columns.tolist())\n","\n","print(\"\\nMissing Values:\")\n","print(df.isnull().sum())\n","\n","# Optional: Display number of rows and columns\n","print(f\"\\nShape of the dataset: {df.shape}\")"],"metadata":{"id":"So-PKtbo5AVA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","#@markdown Build a dataset for training using a .parquet file\n","\n","num_dataset_items = 200 #@param {type:'slider',max:1000}\n","\n","\n","\n","# Step 1: Install required libraries (if not already installed)\n","# !pip install datasets pandas pillow requests\n","\n","# Step 2: Import required libraries\n","import pandas as pd\n","from datasets import Dataset\n","from PIL import Image\n","import requests\n","from io import BytesIO\n","import numpy as np\n","\n","# Step 3: Define the path to the Parquet file\n","file_path = '/content/drive/MyDrive/Chroma prompts/redcaps_1.parquet' #@param {type:'string'}\n","\n","# Step 4: Read the Parquet file\n","df = pd.read_parquet(file_path)\n","\n","# Step 5: Randomly select 300 rows to account for potential image loading failures\n","df_sample = df.sample(n=math.floor(num_dataset_items*1.2), random_state=42).reset_index(drop=True)\n","\n","# Step 6: Function to download, resize, and process images\n","def load_and_resize_image_from_url(url, max_size=(1024, 1024)):\n","    try:\n","        response = requests.get(url, timeout=10)\n","        response.raise_for_status()  # Raise an error for bad status codes\n","        img = Image.open(BytesIO(response.content)).convert('RGB')\n","        # Resize image to fit within 1024x1024 while maintaining aspect ratio\n","        img.thumbnail(max_size, Image.Resampling.LANCZOS)\n","        return img\n","    except Exception as e:\n","        print(f\"Error loading image from {url}: {e}\")\n","        return None\n","\n","# Step 7: Create lists for images and captions\n","images = []\n","texts = []\n","\n","for index, row in df_sample.iterrows():\n","    if len(images) >= num_dataset_items:  # Stop once we have 200 valid images\n","        break\n","    url = row['url']\n","    caption = row['original_caption'] + ', ' + row['vlm_caption'].replace('This image displays:','').replace('This image displays','')\n","\n","    # Load and resize image\n","    img = load_and_resize_image_from_url(url)\n","    if img is not None:\n","        images.append(img)\n","        texts.append(caption)\n","    else:\n","        print(f\"Skipping row {index} due to image loading failure.\")\n","\n","# Step 8: Check if we have enough images\n","if len(images) < num_dataset_items:\n","    print(f\"Warning: Only {len(images)} images were successfully loaded.\")\n","else:\n","    # Truncate to exactly 200 if we have more\n","    images = images[:num_dataset_items]\n","    texts = texts[:num_dataset_items]\n","\n","# Step 9: Create a Hugging Face Dataset\n","dataset = Dataset.from_dict({\n","    'image': images,\n","    'text': texts\n","})\n","\n","# Step 10: Verify the dataset\n","print(dataset)\n","\n","# Step 11: Example of accessing an image and text\n","print(\"\\nExample of accessing first item:\")\n","print(\"Text:\", dataset['text'][0])\n","print(\"Image type:\", type(dataset['image'][0]))\n","print(\"Image size:\", dataset['image'][0].size)\n","\n","# Optional: Save the dataset to disk (if needed)\n","dataset.save_to_disk('/content/drive/MyDrive/Chroma prompts/custom_dataset')"],"metadata":{"id":"XZvpJ5zw0fzR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset.save_to_disk('/content/drive/MyDrive/redcap_dataset')"],"metadata":{"id":"iTyxazlM1OAn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset['image'][10]"],"metadata":{"id":"jFnWBQHa142R"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Import pandas\n","import pandas as pd\n","\n","# Define the path to the Parquet file\n","file_path = '/content/drive/MyDrive/Chroma prompts/e621_2024_p10.parquet' #@param {type:'string'}\n","\n","parquet_column = 'descriptions' #@param {type:'string'}\n","# Read the Parquet file\n","df = pd.read_parquet(file_path)\n","\n","# Set pandas display options to show full text without truncation\n","pd.set_option('display.max_colwidth', None)  # Show full content of columns\n","pd.set_option('display.width', None)         # Use full display width\n","\n","# Create sliders for selecting the range of captions\n","#@markdown Caption Range { run: \"auto\", display_mode: \"form\" }\n","start_at = 16814 #@param {type:\"slider\", min:0, max:33147, step:1}\n","range = 247 #@param {type:'slider',min:1,max:1000,step:1}\n","start_index = start_at\n","end_index = start_at + range\n","###@param {type:\"slider\", min:1, max:33148, step:1}\n","\n","include_either_words = '' #@param {type:'string', placeholder:'item1,item2...'}\n","#display_only = True #@param {type:'boolean'}\n","\n","_include_either_words = ''\n","for include_word in include_either_words.split(','):\n","  if include_word.strip()=='':continue\n","  _include_either_words= include_either_words + include_word.lower()+','+include_word.title() +','\n","#-----#\n","_include_either_words = _include_either_words[:len(_include_either_words)-1]\n","\n","\n","# Ensure end_index is greater than start_index and within bounds\n","if end_index <= start_index:\n","    print(\"Error: End index must be greater than start index.\")\n","elif end_index > len(df):\n","    print(f\"Error: End index cannot exceed {len(df)}. Setting to maximum value.\")\n","    end_index = len(df)\n","elif start_index < 0:\n","    print(\"Error: Start index cannot be negative. Setting to 0.\")\n","    start_index = 0\n","\n","# Display the selected range of captions\n","tmp =''\n","\n","categories= ['regular_summary:',';midjourney_style_summary:', 'individual_parts:']\n","\n","print(f\"\\nDisplaying captions from index {start_index} to {end_index-1}:\")\n","for index, caption in df[f'{parquet_column}'][start_index:end_index].items():\n","  for include_word in _include_either_words.split(','):\n","    found = True\n","    if (include_word.strip() in caption) or include_word.strip()=='':\n","      #----#\n","      if not found: continue\n","      tmp= caption + '\\n\\n'\n","      for category in categories:\n","        tmp = tmp.replace(f'{category}',f'\\n\\n{category}\\n')\n","      #----#\n","      print(f'Index {index}: {tmp}')\n"],"metadata":{"id":"wDhyb8M_7pkD"},"execution_count":null,"outputs":[]}]}