first push

app.py

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+import gradio as gr
+import pandas as pd
+from green_score import GREEN
+
+# Include the GREEN class code here
+# -------------------------------------
+# Paste the GREEN class code above this line
+# -------------------------------------
+
+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="Example 1",
+            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()

requirements.txt

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+green_score
+gradio