feat: Deprecate blob.exif and replace with sample notebook

Author: shuoweil

bigframes/blob/_functions.py

@@ -126,57 +126,7 @@ def udf(self):
         return self._session.read_gbq_function(udf_name)
 
 
-def exif_func(src_obj_ref_rt: str, verbose: bool) -> str:
-    try:
-        import io
-        import json
-
-        from PIL import ExifTags, Image
-        import requests
-        from requests import adapters
-
-        session = requests.Session()
-        session.mount("https://", adapters.HTTPAdapter(max_retries=3))
-
-        src_obj_ref_rt_json = json.loads(src_obj_ref_rt)
-        src_url = src_obj_ref_rt_json["access_urls"]["read_url"]
-
-        response = session.get(src_url, timeout=30)
-        response.raise_for_status()
-        bts = response.content
-
-        image = Image.open(io.BytesIO(bts))
-        exif_data = image.getexif()
-        exif_dict = {}
-
-        if exif_data:
-            for tag, value in exif_data.items():
-                tag_name = ExifTags.TAGS.get(tag, tag)
-                # Convert non-serializable types to strings
-                try:
-                    json.dumps(value)
-                    exif_dict[tag_name] = value
-                except (TypeError, ValueError):
-                    exif_dict[tag_name] = str(value)
-
-        if verbose:
-            return json.dumps({"status": "", "content": json.dumps(exif_dict)})
-        else:
-            return json.dumps(exif_dict)
-
-    except Exception as e:
-        # Return error as JSON with error field
-        error_result = {"status": f"{type(e).__name__}: {str(e)}", "content": "{}"}
-        if verbose:
-            return json.dumps(error_result)
-        else:
-            return "{}"
-
-
-exif_func_def = FunctionDef(exif_func, ["pillow", "requests"])
-
-
-# Blur images. Takes ObjectRefRuntime as JSON string. Outputs ObjectRefRuntime JSON string.
+# Blur images. Takes ObjectRefRuntime as JSON string. Outputs ObjectRefRuntime JSON string.                             │
 def image_blur_func(
     src_obj_ref_rt: str,
     dst_obj_ref_rt: str,

bigframes/operations/blob.py

@@ -336,76 +336,6 @@ def get_runtime_json_str(
         runtime = self._get_runtime(mode=mode, with_metadata=with_metadata)
         return runtime._apply_unary_op(ops.ToJSONString())
 
-    def exif(
-        self,
-        *,
-        engine: Literal[None, "pillow"] = None,
-        connection: Optional[str] = None,
-        max_batching_rows: int = 8192,
-        container_cpu: Union[float, int] = 0.33,
-        container_memory: str = "512Mi",
-        verbose: bool = False,
-    ) -> bigframes.series.Series:
-        """Extract EXIF data. Now only support image types.
-
-        Args:
-            engine ('pillow' or None, default None): The engine (bigquery or third party library) used for the function. The value must be specified.
-            connection (str or None, default None): BQ connection used for function internet transactions, and the output blob if "dst" is str. If None, uses default connection of the session.
-            max_batching_rows (int, default 8,192): Max number of rows per batch send to cloud run to execute the function.
-            container_cpu (int or float, default 0.33): number of container CPUs. Possible values are [0.33, 8]. Floats larger than 1 are cast to intergers.
-            container_memory (str, default "512Mi"): container memory size. String of the format <number><unit>. Possible values are from 512Mi to 32Gi.
-            verbose (bool, default False): If True, returns a struct with status and content fields. If False, returns only the content.
-
-        Returns:
-            bigframes.series.Series: JSON series of key-value pairs if verbose=False, or struct with status and content if verbose=True.
-
-        Raises:
-            ValueError: If engine is not 'pillow'.
-            RuntimeError: If EXIF extraction fails or returns invalid structure.
-        """
-        if engine is None or engine.casefold() != "pillow":
-            raise ValueError("Must specify the engine, supported value is 'pillow'.")
-
-        import bigframes.bigquery as bbq
-        import bigframes.blob._functions as blob_func
-        import bigframes.pandas as bpd
-
-        connection = self._resolve_connection(connection)
-        df = self.get_runtime_json_str(mode="R").to_frame()
-        df["verbose"] = verbose
-
-        exif_udf = blob_func.TransformFunction(
-            blob_func.exif_func_def,
-            session=self._data._block.session,
-            connection=connection,
-            max_batching_rows=max_batching_rows,
-            container_cpu=container_cpu,
-            container_memory=container_memory,
-        ).udf()
-
-        res = self._apply_udf_or_raise_error(df, exif_udf, "EXIF extraction")
-
-        if verbose:
-            try:
-                exif_content_series = bbq.parse_json(
-                    res._apply_unary_op(ops.JSONValue(json_path="$.content"))
-                ).rename("exif_content")
-                exif_status_series = res._apply_unary_op(
-                    ops.JSONValue(json_path="$.status")
-                )
-            except Exception as e:
-                raise RuntimeError(f"Failed to parse EXIF JSON result: {e}") from e
-            results_df = bpd.DataFrame(
-                {"status": exif_status_series, "content": exif_content_series}
-            )
-            results_struct = bbq.struct(results_df).rename("exif_results")
-            return results_struct
-        else:
-            try:
-                return bbq.parse_json(res)
-            except Exception as e:
-                raise RuntimeError(f"Failed to parse EXIF JSON result: {e}") from e
-
     def image_blur(
         self,
         ksize: tuple[int, int],