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| import warnings | |
| warnings.filterwarnings("ignore") | |
| import gradio as gr | |
| import pandas as pd | |
| from src.video_model import describe_video | |
| from src.utils import parse_string, parse_annotations | |
| import os | |
| # --- Function to construct the final query --- | |
| def process_video_and_questions(video, sitting, hands, location, screen): | |
| # Extract the video name (filename) | |
| video_name = os.path.basename(video) | |
| # Construct the query with the video name included | |
| query = f"Answer the questions from the video\n" | |
| additional_info = [] | |
| if sitting: | |
| additional_info.append("Is the subject in the video standing or sitting?") | |
| if hands: | |
| additional_info.append("Is the subject holding any object in their hands, if so the hands are not free else they are free?") | |
| if location: | |
| additional_info.append("Is the subject present indoors or outdoors?") | |
| if screen: | |
| additional_info.append("Is the subject interacting with a screen in the background by facing the screen?") | |
| end_query = """Provide the results in <annotation> tags, where 0 indicates False, 1 indicates True, and None indicates that no information is present. Follow the below examples\n: | |
| <annotation>indoors: 0</annotation> | |
| <annotation>standing: 1</annotation> | |
| <annotation>hands.free: 0</annotation> | |
| <annotation>screen.interaction_yes: 0</annotation> | |
| """ | |
| final_query = query + " " + " ".join(additional_info) | |
| final_prompt = final_query + " " + end_query | |
| # Assuming your describe_video function handles the video processing | |
| response = describe_video(video, final_prompt) | |
| final_response = f"<video_name>{video_name}</video_name>" + " " + response | |
| return final_response | |
| # def process_video_and_questions(video, sitting, hands, location, screen): | |
| # # Extract the video name (filename) | |
| # video_name = os.path.basename(video) | |
| # # Construct the query with the video name included | |
| # query = f"Describe the video in detail and answer the questions" | |
| # additional_info = [] | |
| # # Handle each checkbox option, including those not selected (None) | |
| # if sitting is not None: | |
| # additional_info.append("Is the subject in the video standing or sitting?") | |
| # else: | |
| # additional_info.append("<annotation>standing: None</annotation>") | |
| # if hands is not None: | |
| # additional_info.append("Is the subject holding any object in their hands, if so the hands are not free else they are free?") | |
| # else: | |
| # additional_info.append("<annotation>hands.free: None</annotation>") | |
| # if location is not None: | |
| # additional_info.append("Is the subject present indoors or outdoors?") | |
| # else: | |
| # additional_info.append("<annotation>indoors: None</annotation>") | |
| # if screen is not None: | |
| # additional_info.append("Is the subject interacting with a screen in the background by facing the screen?") | |
| # else: | |
| # additional_info.append("<annotation>screen.interaction_yes: None</annotation>") | |
| # # Updated end_query with structured prompt | |
| # end_query = """ | |
| # You're an AI assistant, and your goal is to provide the results of the video analysis in the correct format as described below: | |
| # <annotations> | |
| # - Provide the results in <annotation> tags, where 0 indicates False, 1 indicates True, and None indicates that no information is present. | |
| # - Use <annotation> tags for each attribute like indoors, standing, hands.free, and screen.interaction_yes. | |
| # </annotations> | |
| # """ | |
| # final_query = query + " " + " ".join(additional_info) | |
| # final_prompt = final_query + " " + end_query | |
| # # Assuming your describe_video function handles the video processing | |
| # response = describe_video(video, final_prompt) | |
| # final_response = f"<video_name>{video_name}</video_name>" + " " + response | |
| # return final_response | |
| def output_to_csv(final_response): | |
| # Parse the string to get the content | |
| parsed_content = parse_string(final_response, ["video_name", "annotation"]) | |
| # Extract video name (assuming there's only one video name) | |
| video_name = parsed_content['video_name'][0] if parsed_content['video_name'] else None | |
| # Convert the list of annotations into a dictionary | |
| annotations_dict = parse_annotations(parsed_content['annotation']) if parsed_content['annotation'] else {} | |
| # Combine the video name and annotation dictionary into a single row | |
| df = pd.DataFrame([{'video_name': video_name, **annotations_dict}]) | |
| return df | |
| # Examples for the interface | |
| examples = [ | |
| ["videos/2016-01-01_0100_US_KNBC_Channel_4_News_1867.16-1871.38_now.mp4", True, False, True, False], | |
| ["videos/2016-01-01_0200_US_KNBC_Channel_4_News_1329.12-1333.29_tonight.mp4", False, True, True, True], | |
| ["videos/2016-01-01_0830_US_KNBC_Tonight_Show_with_Jimmy_Fallon_725.45-729.76_tonight.mp4", True, False, False, True], | |
| ["videos/2016-01-01_0200_US_KOCE_The_PBS_Newshour_577.03-581.31_tonight.mp4", False, True, True, False], | |
| ["videos/2016-01-01_1400_US_KTTV-FOX_Morning_News_at_6AM_1842.36-1846.68_this_year.mp4", True, True, False, False], | |
| ["videos/2016-01-02_0735_US_KCBS_Late_Show_with_Stephen_Colbert_285.94-290.67_this_year.mp4", False, True, True, True], | |
| ["videos/2016-01-13_2200_US_KTTV-FOX_The_Doctor_Oz_Show_1709.79-1714.17_this_month.mp4", True, False, False, True], | |
| ["videos/2016-01-01_1400_US_KTTV-FOX_Morning_News_at_6AM_1842.36-1846.68_this_year.mp4", False, True, True, False], | |
| ["videos/2016-01-01_1300_US_KNBC_Today_in_LA_at_5am_12.46-16.95_this_morning.mp4", True, False, False, True], | |
| ["videos/2016-01-05_0200_US_KNBC_Channel_4_News_1561.29-1565.95_next_week.mp4", False, True, True, False], | |
| ["videos/2016-01-28_0700_US_KNBC_Channel_4_News_at_11PM_629.56-633.99_in_the_future.mp4", True, False, False, True] | |
| ] | |
| # Title, description, and article for the interface | |
| title = "GSoC Super Raid Annotator" | |
| description = "Annotate Videos" | |
| article = "<p style='text-align: center'><a href='https://github.com/OpenBMB/MiniCPM-V' target='_blank'>Model GitHub Repo</a> | <a href='https://huggingface.co/openbmb/MiniCPM-V-2_6' target='_blank'>Model Page</a></p>" | |
| custom_theme = gr.themes.Soft( | |
| primary_hue="red", | |
| secondary_hue="red") | |
| with gr.Blocks(theme=custom_theme) as demo: | |
| gr.Markdown(f"# {title}") | |
| gr.Markdown(description) | |
| gr.Markdown(article) | |
| with gr.Row(): | |
| with gr.Column(): | |
| video = gr.Video(label="Video") | |
| sitting = gr.Checkbox(label="Sitting/Standing") | |
| hands = gr.Checkbox(label="Hands Free/Not Free") | |
| location = gr.Checkbox(label="Indoors/Outdoors") | |
| screen = gr.Checkbox(label="Screen Interaction") | |
| submit_btn = gr.Button("Generate Annotations") | |
| generate_csv_btn = gr.Button("Generate CSV") | |
| with gr.Column(): | |
| response = gr.Textbox(label="Video Description", show_label=True, show_copy_button=True) | |
| csv_output = gr.DataFrame(label="CSV Output", headers=["video_name", "annotation"]) | |
| # Event handling for the Submit button | |
| submit_btn.click( | |
| fn=process_video_and_questions, | |
| inputs=[video, sitting, hands, location, screen], | |
| outputs=response | |
| ) | |
| # Event handling for the Generate CSV button | |
| generate_csv_btn.click( | |
| fn=output_to_csv, | |
| inputs=response, | |
| outputs=csv_output | |
| ) | |
| gr.Examples(examples=examples, inputs=[video, sitting, hands, location, screen]) | |
| demo.launch(debug=False) | |