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>

Author: 4 people

notebooks/multimodal/multimodal_dataframe.ipynb

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