Spaces:
Sleeping
Sleeping
File size: 2,895 Bytes
2df9f11 81766be 2df9f11 81766be 2df9f11 81766be 2df9f11 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
import gradio as gr
import pandas as pd
from green_score import GREEN
import spaces
@spaces.GPU # Add the GPU decorator for functions that need GPU access
def run_green(ref_text, hyp_text, model_name="StanfordAIMI/GREEN-radllama2-7b"):
refs = [ref_text.strip()]
hyps = [hyp_text.strip()]
green_scorer = GREEN(model_name, output_dir=".")
mean, std, green_score_list, summary, result_df = green_scorer(refs, hyps)
final_summary = (
f"**Model:** {model_name}\n\n"
f"**Mean GREEN Score:** {mean:.4f}\n"
f"**Std Deviation:** {std:.4f}\n\n"
f"**Detailed Summary:**\n{summary}"
)
return final_summary, pd.DataFrame(result_df), green_score_list
# Example pairs
examples = {
"Example 1": {
"ref": "Interstitial opacities without changes.",
"hyp": "Interstitial opacities at bases without changes.",
},
"Example 2": {
"ref": "The heart size is normal. Lungs are clear without any infiltrates.",
"hyp": "The heart size is mildly enlarged. Mild infiltrates in the left upper lobe.",
},
"Example 3": {
"ref": "Lung volumes are low, causing bronchovascular crowding. The cardiomediastinal silhouette is unremarkable.",
"hyp": "Endotracheal tubes have been removed. Pulmonary aeration is slightly improved with no pleural effusions.",
}
}
def update_fields(choice):
if choice == "Custom":
return gr.update(value="", interactive=True), gr.update(value="", interactive=True)
else:
return gr.update(value=examples[choice]["ref"], interactive=False), gr.update(value=examples[choice]["hyp"], interactive=False)
with gr.Blocks(title="GREEN Score Evaluation Demo") as demo:
gr.Markdown("# GREEN Score Evaluation Demo")
gr.Markdown(
"This demo evaluates a single pair of reference and hypothesis reports to compute the GREEN score."
)
with gr.Row():
choice = gr.Radio(
label="Choose Input Type",
choices=["Custom"] + list(examples.keys()),
value="Custom",
interactive=True
)
ref_input = gr.Textbox(
label="Reference Report",
lines=3
)
hyp_input = gr.Textbox(
label="Hypothesis Report",
lines=3
)
choice.change(
update_fields,
inputs=choice,
outputs=[ref_input, hyp_input],
)
model_name_input = gr.Textbox(
label="Model Name",
value="StanfordAIMI/GREEN-radllama2-7b",
placeholder="Enter the HuggingFace model name"
)
run_button = gr.Button("Compute GREEN Score")
summary_output = gr.Markdown()
df_output = gr.DataFrame()
score_list_output = gr.JSON()
run_button.click(
run_green,
inputs=[ref_input, hyp_input, model_name_input],
outputs=[summary_output, df_output, score_list_output]
)
demo.launch()
|