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app.py

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+import gradio as gr
+import pandas as pd
+from pycaret.classification import load_model
+
+# Load the saved model
+model = load_model('best_automl_model')
+
+# Prediction function
+def predict_anemia(sex, red_pixel, green_pixel, blue_pixel):
+    # Map input values to a DataFrame
+    input_data = pd.DataFrame({
+        'Red Pixel': [red_pixel],
+        'Green Pixel': [green_pixel],
+        'Blue Pixel': [blue_pixel],
+        'Sex': [1 if sex == "Male" else 0]  # Encode 'Male' as 1, 'Female' as 0
+    })
+    
+    # Make predictions
+    prediction = model.predict(input_data)[0]
+    probability = model.predict_proba(input_data)[0][1]
+    
+    # Convert results
+    prediction_label = "Yes" if prediction == 1 else "No"
+    return prediction_label, f"{probability:.2%}"  # Probability as a percentage
+
+# Define input components with better labels and layout
+inputs = [
+    gr.Radio(["Male", "Female"], label="Sex", interactive=True),
+    gr.Slider(0, 100, step=0.1, label="Red Pixel Percentage (%)"),
+    gr.Slider(0, 100, step=0.1, label="Green Pixel Percentage (%)"),
+    gr.Slider(0, 100, step=0.1, label="Blue Pixel Percentage (%)"),
+]
+
+# Define output components
+outputs = [
+    gr.Textbox(label="Prediction", interactive=False),
+    gr.Textbox(label="Probability of Anemia (%)", interactive=False),
+]
+
+# Create the Gradio Interface
+interface = gr.Interface(
+    fn=predict_anemia,
+    inputs=inputs,
+    outputs=outputs,
+    title="✨ Anemia Prediction App ✨",
+    description=(
+        "This app predicts whether a person is anemic based on the percentages of red, "
+        "green, and blue pixels in their image and their sex. "
+        "Simply adjust the sliders and select the person's sex to get a prediction."
+    ),
+    theme="huggingface",  # Optional theme
+    live=False,  # Turn off live predictions for better performance
+    examples=[
+        ["Male", 45.0, 30.0, 25.0],
+        ["Female", 50.0, 28.0, 22.0],
+    ],
+)
+
+# Launch the app
+interface.launch()

best_automl_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b2380025f163eeb045e823c58b5ce95106d54622ee3b460c6f7bfd89657c4d70
+size 4375

requirements.txt

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+gradio
+pandas
+pycaret
+scikit-learn