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This repository was archived by the owner on May 7, 2026. It is now read-only.

Commit 84c6f88

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shuoweilGarrettWuchelsea-linsycai
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docs: Add EXIF metadata extraction example to multimodal notebook (#2429)
This PR updates the notebooks/multimodal/multimodal_dataframe.ipynb notebook to include a comprehensive example of extracting EXIF metadata from images. Key Changes: * Added a new section "7. Extract EXIF metadata from images". * Implemented a custom remote function (UDF) using pillow and requests to retrieve and parse EXIF tags from image URLs. * Demonstrated how to apply this function efficiently within a BigFrames workflow to analyze image metadata. This addition provides users with a practical pattern for handling image metadata and using custom libraries within BigQuery DataFrames. Fixes #<478952827> 🦕 --------- Co-authored-by: Garrett Wu <6505921+GarrettWu@users.noreply.github.com> Co-authored-by: Chelsea Lin <chelsealin@google.com> Co-authored-by: Shenyang Cai <sycai@users.noreply.github.com>
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‎notebooks/multimodal/multimodal_dataframe.ipynb‎

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+92-2Lines changed: 92 additions & 2 deletions
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@@ -61,7 +61,8 @@
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"3. Conduct image transformations\n",
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"4. Use LLM models to ask questions and generate embeddings on images\n",
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"5. PDF chunking function\n",
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"6. Transcribe audio"
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"6. Transcribe audio\n",
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"7. Extract EXIF metadata from images"
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]
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},
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{
@@ -104,6 +105,11 @@
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"PROJECT = \"bigframes-dev\" # replace with your project. \n",
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"# Refer to https://cloud.google.com/bigquery/docs/multimodal-data-dataframes-tutorial#required_roles for your required permissions\n",
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"\n",
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"LOCATION = \"us\" # replace with your location.\n",
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"\n",
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"# Dataset where the UDF will be created.\n",
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"DATASET_ID = \"bigframes_samples\" # replace with your dataset ID.\n",
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"\n",
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"OUTPUT_BUCKET = \"bigframes_blob_test\" # replace with your GCS bucket. \n",
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"# The connection (or bigframes-default-connection of the project) must have read/write permission to the bucket. \n",
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"# Refer to https://cloud.google.com/bigquery/docs/multimodal-data-dataframes-tutorial#grant-permissions for setting up connection service account permissions.\n",
@@ -112,12 +118,14 @@
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"import bigframes\n",
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"# Setup project\n",
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"bigframes.options.bigquery.project = PROJECT\n",
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"bigframes.options.bigquery.location = LOCATION\n",
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"\n",
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"# Display options\n",
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"bigframes.options.display.blob_display_width = 300\n",
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"bigframes.options.display.progress_bar = None\n",
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"\n",
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"import bigframes.pandas as bpd"
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"import bigframes.pandas as bpd\n",
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"import bigframes.bigquery as bbq"
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]
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},
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{
@@ -1546,6 +1554,88 @@
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"transcribed_series_verbose = df['audio'].blob.audio_transcribe(model_name=\"gemini-2.0-flash-001\", verbose=True)\n",
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"transcribed_series_verbose"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 7. Extract EXIF metadata from images"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This section demonstrates how to extract EXIF metadata from images using a custom BigQuery Python UDF and the `Pillow` library."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Construct the canonical connection ID\n",
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"FULL_CONNECTION_ID = f\"{PROJECT}.{LOCATION}.bigframes-default-connection\"\n",
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"\n",
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"@bpd.udf(\n",
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" input_types=[str],\n",
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" output_type=str,\n",
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" dataset=DATASET_ID,\n",
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" name=\"extract_exif\",\n",
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" bigquery_connection=FULL_CONNECTION_ID,\n",
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" packages=[\"pillow\", \"requests\"],\n",
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" max_batching_rows=8192,\n",
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" container_cpu=0.33,\n",
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" container_memory=\"512Mi\"\n",
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")\n",
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"def extract_exif(src_obj_ref_rt: str) -> str:\n",
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" import io\n",
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" import json\n",
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" from PIL import ExifTags, Image\n",
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" import requests\n",
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" from requests import adapters\n",
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" session = requests.Session()\n",
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" session.mount(\"https://\", adapters.HTTPAdapter(max_retries=3))\n",
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" src_obj_ref_rt_json = json.loads(src_obj_ref_rt)\n",
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" src_url = src_obj_ref_rt_json[\"access_urls\"][\"read_url\"]\n",
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" response = session.get(src_url, timeout=30)\n",
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" bts = response.content\n",
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" image = Image.open(io.BytesIO(bts))\n",
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" exif_data = image.getexif()\n",
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" exif_dict = {}\n",
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" if exif_data:\n",
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" for tag, value in exif_data.items():\n",
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" tag_name = ExifTags.TAGS.get(tag, tag)\n",
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" exif_dict[tag_name] = value\n",
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" return json.dumps(exif_dict)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a Multimodal DataFrame from the sample image URIs\n",
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"exif_image_df = bpd.from_glob_path(\n",
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" \"gs://bigframes_blob_test/images_exif/*\",\n",
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" name=\"blob_col\",\n",
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")\n",
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"\n",
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"# Generate a JSON string containing the runtime information (including signed read URLs)\n",
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"# This allows the UDF to download the images from Google Cloud Storage\n",
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"access_urls = exif_image_df[\"blob_col\"].blob.get_runtime_json_str(mode=\"R\")\n",
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"\n",
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"# Apply the BigQuery Python UDF to the runtime JSON strings\n",
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"# We cast to string to ensure the input matches the UDF's signature\n",
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"exif_json = access_urls.astype(str).apply(extract_exif)\n",
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"\n",
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"# Parse the resulting JSON strings back into a structured JSON type for easier access\n",
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"exif_data = bbq.parse_json(exif_json)\n",
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"\n",
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"exif_data"
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]
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}
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],
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"metadata": {

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