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Update both_app.py
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import warnings
warnings.filterwarnings("ignore")
import gradio as gr
import pandas as pd
import torch
import gc
import os
from typing import Dict, List
from src.video_model import describe_video
from src.utils import parse_string, parse_annotations
# --- Function to process single video ---
def process_video_and_questions(video, standing, hands, location, screen):
video_name = os.path.basename(video)
query = f"Answer the questions from the video\n"
additional_info = []
if standing:
additional_info.append("Is the subject in the video standing or sitting?\n")
if hands:
additional_info.append("Is the subject holding any object in their hands?\n")
if location:
additional_info.append("Is the subject present indoors?\n")
if screen:
additional_info.append("Is the subject interacting with a screen in the background by facing the screen?\n")
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
response = describe_video(video, final_prompt)
final_response = f"<video_name>{video_name}</video_name>" + " \n" + response
conditions = {
'standing': (standing, 'standing: 1', 'standing: None'),
'hands': (hands, 'hands.free: 1', 'hands.free: None'),
'location': (location, 'indoors: 1', 'indoors: None'),
'screen': (screen, 'screen.interaction_yes: 1', 'screen.interaction_yes: None')
}
for key, (condition, to_replace, replacement) in conditions.items():
if not condition:
final_response = final_response.replace(to_replace, replacement)
return final_response
# Function to save data to a CSV file using pandas
def save_to_csv(observations: List[Dict], output_dir: str = "outputs") -> str:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Convert the list of dictionaries to a pandas DataFrame
df = pd.DataFrame(observations)
# Specify the CSV file path
csv_file = os.path.join(output_dir, "video_observations.csv")
# Save the DataFrame to a CSV file
df.to_csv(csv_file, index=False)
return csv_file
# Function to process a single video and return the observation data
def process_single_video(video_path, standing, hands, location, screen) -> Dict:
video_name = os.path.basename(video_path) # Extract video name from the path
query = "Describe this video in detail and answer the questions"
additional_info = []
if standing:
additional_info.append("Is the subject in the video standing or sitting?\n")
if hands:
additional_info.append("Is the subject holding any object in their hands?\n")
if location:
additional_info.append("Is the subject present indoors?\n")
if screen:
additional_info.append("Is the subject interacting with a screen in the background by facing the screen?\n")
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:
<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_path, final_prompt)
final_response = f"<video_name>{video_name}</video_name>" + " \n" + response
conditions = {
'standing': (standing, 'standing: 1', 'standing: None'),
'hands': (hands, 'hands.free: 1', 'hands.free: None'),
'location': (location, 'indoors: 1', 'indoors: None'),
'screen': (screen, 'screen.interaction_yes: 1', 'screen.interaction_yes: None')
}
for key, (condition, to_replace, replacement) in conditions.items():
if not condition:
final_response = final_response.replace(to_replace, replacement)
# Parse the response to extract video name and annotations
parsed_content = parse_string(final_response, ["video_name", "annotation"])
video_name = parsed_content['video_name'][0] if parsed_content['video_name'] else None
annotations_dict = parse_annotations(parsed_content['annotation']) if parsed_content['annotation'] else {}
# Return the observation as a dictionary
return {'video_name': video_name, **annotations_dict}
# Function to process all videos in a folder
def process_multiple_videos(video_files: List[str], standing, hands, location, screen):
all_observations = []
for video_path in video_files:
observation = process_single_video(video_path, standing, hands, location, screen)
if observation['video_name']: # Only add valid observations
all_observations.append(observation)
else:
print("Error processing video:", video_path) # Log any errors
# Clear GPU cache
torch.cuda.empty_cache()
gc.collect()
# Save all observations to a CSV file and return the file path
csv_file = save_to_csv(all_observations)
return "Processing completed. Download the CSV file.", csv_file
# Gradio interface
def gradio_interface_single(video, standing, hands, location, screen):
return process_video_and_questions(video, standing, hands, location, screen)
def gradio_interface_multiple(video_files, standing, hands, location, screen):
video_file_paths = [video.name for video in video_files] # Extract file paths from uploaded files
return process_multiple_videos(video_file_paths, standing, hands, location, screen)
with gr.Blocks() as demo:
with gr.Tab("Single Video Processing"):
with gr.Row():
with gr.Column():
video = gr.Video(label="Video")
standing = gr.Checkbox(label="Standing")
hands = gr.Checkbox(label="Hands Free")
location = gr.Checkbox(label="Indoors")
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.File(label="Download CSV", interactive=False)
submit_btn.click(
fn=gradio_interface_single,
inputs=[video, standing, hands, location, screen],
outputs=response
)
generate_csv_btn.click(
fn=save_to_csv,
inputs=response,
outputs=csv_output
)
with gr.Tab("Batch Video Processing"):
with gr.Row():
video_files = gr.File(file_count="multiple", file_types=["video"], label="Upload multiple videos")
standing = gr.Checkbox(label="Standing")
hands = gr.Checkbox(label="Hands Free")
location = gr.Checkbox(label="Indoors")
screen = gr.Checkbox(label="Screen Interaction")
submit_btn = gr.Button("Process Videos")
download_link = gr.File(label="Download CSV")
submit_btn.click(
fn=gradio_interface_multiple,
inputs=[video_files, standing, hands, location, screen],
outputs=[response, download_link]
)
demo.launch(debug=False)