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import math
import gradio as gr
import os
import json
# Paths to the JSON files
json_files = {
"Kinetics700": "kinetics700_tune_.json",
"STAR-benchmark": "starb_tune_.json",
"FineDiving": "finediving_tune_.json"
}
VIDEO_NAME = 'video_name'
QUESTION = 'question'
LABEL = 'label'
PREDICTION = 'prediction'
left_side_columns = [VIDEO_NAME]
right_side_columns = [QUESTION, LABEL, PREDICTION]
batch_size = 4
target_size = (1024, 1024)
def func(index, dataset):
json_file = json_files[dataset]
start_index = index * batch_size
end_index = start_index + batch_size
with open(json_file, 'r') as f:
data = json.load(f)
all_examples = data[start_index:end_index]
values_lst = []
for example_idx, example in enumerate(all_examples):
values = get_instance_values(example, dataset)
values_lst += values
return values_lst
def get_instance_values(example, dataset_name):
example[VIDEO_NAME] = os.path.abspath(os.path.join(dataset_name, example[VIDEO_NAME]))
values = []
for k in left_side_columns + right_side_columns:
value = example[k]
values.append(value)
return values
demo = gr.Blocks()
def get_col(example, dataset_name):
instance_values = get_instance_values(example, dataset_name)
with gr.Column():
inputs_left = []
assert len(left_side_columns) == len(instance_values[:len(left_side_columns)]) # excluding the video
for key, value in zip(left_side_columns, instance_values[:len(left_side_columns)]):
if key == VIDEO_NAME:
if os.path.exists(value): # Check if the video file exists
input_k = gr.Video(value=value)
else:
input_k = gr.Textbox(value=f"Video file not found: {value}", label=f"{key.capitalize()}")
else:
label = key.capitalize().replace("_", " ")
input_k = gr.Textbox(value=value, label=f"{label}")
inputs_left.append(input_k)
with gr.Accordion("Click for details", open=False):
text_inputs_right = []
assert len(right_side_columns) == len(instance_values[len(left_side_columns):])
for key, value in zip(right_side_columns, instance_values[len(left_side_columns):]):
label = key.capitalize().replace("_", " ")
if key == PREDICTION:
text_input_k = gr.Textbox(value=value, label=f"{label}", lines=7)
elif key == QUESTION:
text_input_k = gr.Textbox(value=value, label=f"{label}", lines=2)
else:
text_input_k = gr.Textbox(value=value, label=f"{label}")
text_inputs_right.append(text_input_k)
return inputs_left, text_inputs_right
with demo:
with gr.Column():
dataset_dropdown = gr.Dropdown(choices=list(json_files.keys()), label="Select Dataset", value="Kinetics700")
# Load the selected dataset to determine the number of samples
dataset = dataset_dropdown.value
with open(json_files[dataset], 'r') as f:
data = json.load(f)
num_samples = len(data)
slider = gr.Slider(minimum=0, maximum=math.floor(num_samples / batch_size) - 1, step=1, label='Page')
with gr.Row():
index = slider.value
start_index = 0 * batch_size
end_index = start_index + batch_size
all_examples = data[start_index:end_index]
all_inputs_left_right = []
for example_idx, example in enumerate(all_examples):
inputs_left, text_inputs_right = get_col(example, dataset)
inputs_left_right = inputs_left + text_inputs_right
all_inputs_left_right += inputs_left_right
slider.change(func, inputs=[slider, dataset_dropdown], outputs=all_inputs_left_right)
dataset_dropdown.change(func, inputs=[slider, dataset_dropdown], outputs=all_inputs_left_right)
demo.launch()
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