The resources.Classify class provides methods to classify a set of videos or all the videos within an index.

Related quickstart notebook:

Methods

Classify a set of videos

Description: This method classifies a set of videos based on the provided parameters and returns the first page of results.

Function signature and example:

def videos(
    self,
    video_ids: List[str],
    options: List[Union[str, Literal["visual", "conversation", "text_in_video"]]],
    classes: List[models.ClassifyClassParams],
    *,
    conversation_option: Optional[Union[str, Literal["semantic", "exact_match"]]] = "semantic",
    page_limit: Optional[int] = None,
    include_clips: Optional[bool] = None,
    threshold: Optional[Dict[str, Any]] = None,
    show_detailed_score: Optional[bool] = None,
    **kwargs
) -> models.ClassifyPageResult

CLASSES = [
    {
        "name": "DanceTok",
        "prompts": [
            "Dance tutorial",
            "Dance group",
            "Dance competition"
        ]
    },
    {
        "name": "CookTok",
        "prompts": [
            "Cooking tutorial",
            "Cooking ustensils review"
        ]
    }
]

def print_classify_page_result(result):
    print("Classification results:")
    for video_data in result.data:
        print_classify_video_data(video_data)

    print("\nPage information:")
    print_token_page_info(result.page_info)
    
def print_classify_video_data(video_data):
    print(f"Video ID: {video_data.video_id}")
    for class_data in video_data.classes:
        print_classify_class(class_data)
    print()  # Add a blank line between videos

def print_classify_class(class_data):
    print(f"  Class name: {class_data.name}")
    print(f"  Score: {class_data.score}")
    print(f"  Duration ratio: {class_data.duration_ratio}")
    if class_data.clips:
        print("  Clips:")
        for clip in class_data.clips:
            print_classify_clip(clip)

def print_classify_clip(clip):
    print(f"    Start: {clip.start}")
    print(f"    End: {clip.end}")
    print(f"    Score: {clip.score}")
    print(f"    Option: {clip.option}")
    print(f"    Prompt: {clip.prompt}")
    print(f"    Thumbnail URL: {clip.thumbnail_url or 'N/A'}")
    if clip.detailed_scores:
        print_classify_detailed_score(clip.detailed_scores)

def print_classify_detailed_score(detailed_score):
    print(f"      Max score: {detailed_score.max_score}")
    print(f"      Avg score: {detailed_score.avg_score}")
    print(f"      Normalized score: {detailed_score.normalized_score}")

def print_token_page_info(page_info):
    print(f"Limit per page: {page_info.limit_per_page}")
    print(f"Total results: {page_info.total_results}")
    print(f"Page expired at: {page_info.page_expired_at}")
    print(f"Next page token: {page_info.next_page_token or 'N/A'}")
    print(f"Previous page token: {page_info.prev_page_token or 'N/A'}")

VIDEO_IDS = ["VIDEO_ID_1", "VIDEO_ID_2", "VIDEO_ID_3"]   
result = client.classify.videos(
    video_ids=VIDEO_IDS,
    options=["visual", "conversation"],
    classes=CLASSES,
    include_clips=True,
    show_detailed_score=True,
    page_limit=1
)

print_classify_page_result(result)

Parameters:

Name	Type	Required	Description
`video_ids`	`List[str]`	Yes	A list containing the unique identifiers of the videos that you want to classify
`options`	`List[Union[str, Literal["visual", "conversation", "text_in_video"]]]`	Yes	A list that specifies the sources of information the platform uses when it categorizes a video.
`classes`	`List[models.ClassifyClassParams]`	Yes	An array of `ClassifyClassParams` objects containing the names and the definitions of entities or actions that the platform must identify.
`conversation_option`	`Optional[Union[str, Literal["semantic", "exact_match"]]]`	No	Specifies the type of match the platform will perform.
`page_limit`	`Optional[int]`	No	The maximum number of results per page.
`include_clips`	`Optional[bool]`	No	Set this parameter to `true` to retrieve detailed information about each matching video clip.
`threshold`	`Optional[Dict[str, Any]]`	No	Thresholds for different classification options.
`show_detailed_score`	`Optional[bool]`	No	Whether to show detailed scores in the result.
`**kwargs`	`dict`	No	Additional keyword arguments for the request.

Return value: Returns a models.ClassifyPageResult object containing the classification results.

API Reference: For a description of each field in the request and response, see the Classify a set of videos page.

Related guides:

Classify all the videos within an index

Description: This method classifies all the videos within a specific index based on the provided parameters and returns the first page of results.

Function signature and example:

def index(
    self,
    index_id: str,
    options: List[Union[str, Literal["visual", "conversation", "text_in_video"]]],
    classes: List[models.ClassifyClassParams],
    *,
    conversation_option: Optional[Union[str, Literal["semantic", "exact_match"]]] = "semantic",
    page_limit: Optional[int] = None,
    include_clips: Optional[bool] = None,
    threshold: Optional[Dict[str, Any]] = None,
    show_detailed_score: Optional[bool] = None,
    **kwargs
) -> models.ClassifyPageResult

CLASSES = [
    {
        "name": "DanceTok",
        "prompts": [
            "Dance tutorial",
            "Dance group",
            "Dance competition"
        ]
    },
    {
        "name": "CookTok",
        "prompts": [
            "Cooking tutorial",
            "Cooking ustensils review"
        ]
    }
]

def print_classify_page_result(result):
    print("Classification results:")
    for video_data in result.data:
        print_classify_video_data(video_data)

    print("\nPage information:")
    print_token_page_info(result.page_info)

def print_classify_video_data(video_data):
    print(f"Video ID: {video_data.video_id}")
    for class_data in video_data.classes:
        print_classify_class(class_data)
    print()  # Add a blank line between videos

def print_classify_class(class_data):
    print(f"  Class name: {class_data.name}")
    print(f"  Score: {class_data.score}")
    print(f"  Duration ratio: {class_data.duration_ratio}")
    if class_data.clips:
        print("  Clips:")
        for clip in class_data.clips:
            print_classify_clip(clip)

def print_classify_clip(clip):
    print(f"    Start: {clip.start}")
    print(f"    End: {clip.end}")
    print(f"    Score: {clip.score}")
    print(f"    Option: {clip.option}")
    print(f"    Prompt: {clip.prompt}")
    print(f"    Thumbnail URL: {clip.thumbnail_url or 'N/A'}")
    if clip.detailed_scores:
        print_classify_detailed_score(clip.detailed_scores)

def print_classify_detailed_score(detailed_score):
    print(f"      Max score: {detailed_score.max_score}")
    print(f"      Avg score: {detailed_score.avg_score}")
    print(f"      Normalized score: {detailed_score.normalized_score}")

def print_token_page_info(page_info):
    print(f"Limit per page: {page_info.limit_per_page}")
    print(f"Total results: {page_info.total_results}")
    print(f"Page expired at: {page_info.page_expired_at}")
    print(f"Next page token: {page_info.next_page_token or 'N/A'}")
    print(f"Previous page token: {page_info.prev_page_token or 'N/A'}")

result = client.classify.index(
    index_id="<YOUR_INDEX_ID>",
    options=["visual", "conversation"],
    classes=CLASSES,
    include_clips=True,
    show_detailed_score=True,
    page_limit=1
)

print_classify_page_result(result)

Name	Type	Required	Description
`index_id`	`str`	Yes	The unique identifier of the index containing the videos you want to classify.
`options`	`List[Union[str, Literal["visual", "conversation", "text_in_video"]]]`	Yes	An array that specifies the sources of information the platform uses when it categorizes a video.
`classes`	`List[models.ClassifyClassParams]`	Yes	An array of `ClassifyClassParams` objects containing the names and the definitions of entities or actions that the platform must identify.
`conversation_option`	`Optional[Union[str, Literal["semantic", "exact_match"]]]`	No	Specifies the type of match the platform will perform.
`page_limit`	`Optional[int]`	No	The maximum number of results per page.
`include_clips`	`Optional[bool]`	No	Set this parameter to `true` to retrieve detailed information about each matching video clip.
`threshold`	`Optional[Dict[str, Any]]`	No	Thresholds for different classification options.
`show_detailed_score`	`Optional[bool]`	No	Whether to show detailed scores in the result.
`**kwargs`	`dict`	No	Additional keyword arguments for the request.

Return value: Returns a models.ClassifyPageResult object containing the classification results.

API Reference: For a description of each field in the request and response, see the Classify all the videos within an index page.

Related guides:

Retrieve a specific page of results

Description: This method retrieves a specific page of results. This method provides direct pagination. Choose it mainly when the total number of items is manageable, or you must fetch a single page of results.

Function signature and example:

def by_page_token(
    self,
    page_token: str,
    **kwargs
) -> models.ClassifyPageResult

def print_classify_page_result(result):
    print("Classification results:")
    for video_data in result.data:
        print_classify_video_data(video_data)

    print("\nPage information:")
    print_token_page_info(result.page_info)

def print_classify_video_data(video_data):
    print(f"Video ID: {video_data.video_id}")
    for class_data in video_data.classes:
        print_classify_class(class_data)
    print()  # Add a blank line between videos

def print_classify_class(class_data):
    print(f"  Class name: {class_data.name}")
    print(f"  Score: {class_data.score}")
    print(f"  Duration ratio: {class_data.duration_ratio}")
    if class_data.clips:
        print("  Clips:")
        for clip in class_data.clips:
            print_classify_clip(clip)

def print_classify_clip(clip):
    print(f"    Start: {clip.start}")
    print(f"    End: {clip.end}")
    print(f"    Score: {clip.score}")
    print(f"    Option: {clip.option}")
    print(f"    Prompt: {clip.prompt}")
    print(f"    Thumbnail URL: {clip.thumbnail_url or 'N/A'}")
    if clip.detailed_scores:
        print_classify_detailed_score(clip.detailed_scores)

def print_classify_detailed_score(detailed_score):
    print(f"      Max score: {detailed_score.max_score}")
    print(f"      Avg score: {detailed_score.avg_score}")
    print(f"      Normalized score: {detailed_score.normalized_score}")

def print_token_page_info(page_info):
    print(f"Limit per page: {page_info.limit_per_page}")
    print(f"Total results: {page_info.total_results}")
    print(f"Page expired at: {page_info.page_expired_at}")
    print(f"Next page token: {page_info.next_page_token or 'N/A'}")
    print(f"Previous page token: {page_info.prev_page_token or 'N/A'}")

result = client.classify.by_page_token("<YOUR_PAGE_TOKEN>")

print_classify_page_result(result)

Parameters:

Name	Type	Required	Description
`pageToken`	`str`	Yes	A token that identifies the page to retrieve.
`**kwargs`	`dict`	No	Additional keyword arguments for the request.

Return value: Returns a models.ClassifyPageResult object containing the classification results for the specified page.

API Reference: For a description of each field in the request and response, see the Retrieve a specific page of results page.

Related guides:

Iterative pagination

If your application must retrieve a large number of items, use iterative pagination. To retrieve the first page of results, invoke the index or videos methods of the classify object. To retrieve subsequent pages of results, use the iterator protocol.

CLASSES = [
    {
        "name": "DanceTok",
        "prompts": [
            "Dance tutorial",
            "Dance group",
            "Dance competition"
        ]
    },
    {
        "name": "CookTok",
        "prompts": [
            "Cooking tutorial",
            "Cooking ustensils review"
        ]
    }
]

def print_classify_page_result(result):
    print("Classification results:")
    data = getattr(result, 'data', result)
    if isinstance(data, list):
        for video_data in data:
            print_classify_video_data(video_data)
    else:
        print_classify_video_data(data)

def print_classify_video_data(video_data):
    print(f"Video ID: {video_data.video_id}")
    for class_data in video_data.classes:
        print_classify_class(class_data)
    print()  # Add a blank line between videos

def print_classify_class(class_data):
    print(f"  Class name: {class_data.name}")
    print(f"  Score: {class_data.score}")
    print(f"  Duration ratio: {class_data.duration_ratio}")
    if class_data.clips:
        print("  Clips:")
        for clip in class_data.clips:
            print_classify_clip(clip)

def print_classify_clip(clip):
    print(f"    Start: {clip.start}")
    print(f"    End: {clip.end}")
    print(f"    Score: {clip.score}")
    print(f"    Option: {clip.option}")
    print(f"    Prompt: {clip.prompt}")
    print(f"    Thumbnail URL: {clip.thumbnail_url or 'N/A'}")
    if clip.detailed_scores:
        print_classify_detailed_score(clip.detailed_scores)

def print_classify_detailed_score(detailed_score):
    print(f"      Max score: {detailed_score.max_score}")
    print(f"      Avg score: {detailed_score.avg_score}")
    print(f"      Normalized score: {detailed_score.normalized_score}")


result = client.classify.index(
    index_id="<YOUR_INDEX_ID>",
    options=["visual", "conversation"],
    classes=CLASSES,
    include_clips=True,
    show_detailed_score=True,
    page_limit=1
)

print_classify_page_result(result)

page_number = 2
while True:
    try:
        print(f"Page {page_number}")
        print_classify_page_result(next(result))
        page_number += 1
    except StopIteration:
        break

print("No more results.")