init

app.py

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+import gradio as gr
+import numpy as np
+import joblib
+
+# Load the model
+rf_model = joblib.load("rf_model.pkl")
+
+features = {
+    "CD4": [0, 500, 1],
+    "AST/ALT": [0, 100, 1],
+    "ALT": [0, 2000, 1],
+    "Hb": [1, 150, 1],
+    "CRP": [1, 500, 1],
+    "ALB": [10, 50, 1],
+    "POAL": [0, 1, 1],
+    "ALC": [0, 5, 1],
+    "Age (years)": [12, 100, 1],
+    "WBC": [0, 20, 1],
+    "PLT": [1, 800, 1],
+    "AST": [0, 2000, 1],
+}
+
+
+# Define the inference function
+def predict(*args):
+    try:
+        # Convert input values to numpy array
+        input_values = [float(arg) for arg in args]
+
+        # Reshape to (1, n_features)
+        input_array = np.array(input_values).reshape(1, -1)
+
+        # Use the model for inference
+        prediction_proba = rf_model.predict_proba(input_array)
+        prediction = rf_model.predict(input_array)
+
+        # Get the confidence of the prediction being class 1 (probability)
+        confidence = prediction_proba[0][1]
+
+        # Return the result
+        if prediction[0] == 1:
+            return f"Prediction: 1\nConfidence: {confidence:.2f}"
+        else:
+            return f"Prediction: 0\nConfidence: {confidence:.2f}"
+    except Exception as e:
+        return f"Inference error: {str(e)}"
+
+
+# Create Gradio interface
+inputs = [
+    gr.Number(value=v[0], label=k, minimum=v[0], maximum=v[1], step=v[2])
+    for k, v in features.items()
+]  # Dynamically generate input components
+outputs = gr.Textbox(label="Inference Result")  # Output component
+
+# create queue
+interface = gr.Interface(
+    fn=predict,  # Inference function
+    inputs=inputs,  # Dynamically generated input components
+    outputs=outputs,  # Output component
+    title="Random Forest Model Inference",  # Interface title
+    description="Input feature values to get the model's inference result.",  # Interface description
+    live=False,  # Whether to update in real-time (set to False, requires button click)
+    flagging_mode="never",
+)
+
+# Enable queue
+interface.queue()
+
+# Launch the app
+interface.launch(share=True)

requirements.txt

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+joblib
+gradio
+numpy

rf_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b84b76ac8818fc3243df79eb164e4e878a5704de54699574d89f6ed5b524a7cb
+size 2015641