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app.py

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+import gradio as gr
+import pickle
+import numpy as np
+
+# 모델 로드
+with open("weights/rf_with_3_roc-7596.pkl", "rb") as f:
+    model_3 = pickle.load(f)
+
+with open("weights/rf_with_5_roc-8557.pkl", "rb") as f:
+    model_5 = pickle.load(f)
+
+with open("weights/rf_with_10_roc-8502.pkl", "rb") as f:
+    model_10 = pickle.load(f)
+
+def predict(model_choice, *features):
+    if model_choice == "3 Features Model":
+        model = model_3
+        features = np.array(features[:3]).reshape(1, -1)
+    elif model_choice == "5 Features Model":
+        model = model_5
+        features = np.array(features[:5]).reshape(1, -1)
+    else:
+        model = model_10
+        features = np.array(features[:10]).reshape(1, -1)
+    
+    probs = model.predict_proba(features)[0]
+    pred_class = np.argmax(probs)
+    return {"Healthy Probability": probs[0], "Moderate Probability": probs[1], "Severe Probability": probs[2], "Predicted Class": ["Healthy", "Moderate", "Severe"][pred_class]}
+
+def update_inputs(model_choice):
+    if model_choice == "3 Features Model":
+        return [gr.update(visible=True)] * 3 + [gr.update(visible=False)] * 7
+    elif model_choice == "5 Features Model":
+        return [gr.update(visible=True)] * 5 + [gr.update(visible=False)] * 5
+    else:
+        return [gr.update(visible=True)] * 10
+
+# 3 환자 데이터
+patient_data = [
+    [3796, 1935, 1148, 2193, 650, 1091, 243, 10135, 260, 23640],  # 환자 1
+    [626, 515, 185, 9150, 1863, 587, 2196, 7021, 1149, 5980],  # 환자 2
+    [0, 328, 0, 258, 123, 116, 0, 423, 1145, 4916]   # 환자 3
+]
+
+def fill_patient_data(model_choice, patient_index):
+    data = patient_data[patient_index]
+    if model_choice == "3 Features Model":
+        return data[:3] + [None] * 7
+    elif model_choice == "5 Features Model":
+        return data[:5] + [None] * 5
+    else:
+        return data[:10]
+
+def clear_inputs():
+    return [None] * 10
+
+features = [
+    "CD8+ T Cell (EM CD27hi)",
+    "CD8+ T Cell (EMRA CD57hi)",
+    "gd T Cell", 
+    "Conventional DC", 
+    "NK Cell (CD56Low CD16hi CD571ow)",
+    "NK Cell (CD56low CD1Ghi CDS7hi)", 
+    "CD8+ T Cell (EMRA CDS7low)", 
+    "Monocytes (CD14+ CD16+)",
+    "B Cell (Plasmablast)", 
+    "CD4+ T Cell (Naive)"
+]
+
+
+# Gradio UI 구성
+with gr.Blocks() as demo:
+    gr.Markdown("## COVID-19 Severity Classification App")
+    with gr.Row():
+        with gr.Column(scale=1):
+            model_choice = gr.Radio(["3 Features Model", "5 Features Model", "10 Features Model"], label="Choose Model", value="5 Features Model")
+            inputs = [gr.Number(label=features[i], visible=(i < 5)) for i in range(10)]
+            with gr.Row():
+                gr.Button("Healthy").click(fill_patient_data, inputs=[model_choice, gr.State(0)], outputs=inputs)
+                gr.Button("Moderate").click(fill_patient_data, inputs=[model_choice, gr.State(1)], outputs=inputs)
+                gr.Button("Severe").click(fill_patient_data, inputs=[model_choice, gr.State(2)], outputs=inputs)
+            run_button = gr.Button("Run Prediction")
+        with gr.Column(scale=1):
+            output = gr.JSON(label="Prediction Output")
+            gr.Button("Clear").click(clear_inputs, outputs=inputs)
+    
+    model_choice.change(update_inputs, inputs=[model_choice], outputs=inputs)
+    run_button.click(predict, inputs=[model_choice] + inputs, outputs=output)
+
+# 앱 실행
+demo.launch()

requirements.txt

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+gradio
+numpy
+scikit-learn
+pickle-mixin

weights/rf_with_10_roc-8502.pkl

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+oid sha256:8a019e21a0c7a69d928cd8083a27905ebf0082f89b640544668e63afe1f1bd80
+size 337191

weights/rf_with_3_roc-7596.pkl

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+oid sha256:cc0ae55ebb5ddf3041e768bff175751e3b135d9670f8dd5dc9485cbf23ff974a
+size 262820

weights/rf_with_5_roc-8557.pkl

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+oid sha256:1141afebc705da4d713ae317ad33b1d3eab1d6423d0f89ea4476864651b490bb
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