app

app.py

@@ -1,96 +1,41 @@
-"""
-Webapp Front End
-"""
-
 import gradio as gr
-import joblib
-from data.clean_data import fetch_check
-
-from data.value_maps import category_maps, binary_maps
-
-MODEL_PATH = "Random_Foresttest_model.pkl"
-DEFAULT_VALUE = 99
-try:
-    rf_model = joblib.load(MODEL_PATH)
-    joblib.dump(rf_model, MODEL_PATH)
-except FileNotFoundError as e:
-    raise FileNotFoundError(
-        f"Model file not found at {MODEL_PATH}. Please check the path."
-    ) from e
-
-og_df = fetch_check(to_fetch=True, to_fillna=True, to_dropna=True)
-
-binary_inputs = {
-    feature: gr.Radio(
-        choices=list(mapping.keys()),
-        label=feature.replace("_", " "),
-    )
-    for feature, mapping in binary_maps.items()
-    if mapping
-}
-
-categorical_inputs = {
-    feature: gr.Dropdown(
-        choices=list(mapping.keys()),
-        label=feature.replace("_", " "),
-    )
-    for feature, mapping in category_maps.items()
-    if mapping
-}
-
-input_types = list(categorical_inputs.values()) + list(binary_inputs.values())
-
-for i in categorical_inputs:
-    print(f"input_types: {i}")
-for i in binary_inputs:
-    print(f"input_types: {i}")
-for i in input_types:
-    print(f"input_types: {i}")
+import pickle
+import pandas as pd
 
+# Load the trained model
+model_path = "tuned_model.pkl"
 
-def predict_outcome(*user_inputs):
-    """
-    Converts user inputs into model-friendly format, runs the prediction,
-    and returns the result.
-    """
-    # Use maps to set expected features
-    expected_features = list(categorical_inputs.keys()) + list(binary_inputs.keys())
+def load_model():
+    """Load the model from the pickle file."""
+    with open(model_path, "rb") as file:
+        return pickle.load(file)
 
-    input_data = dict(zip(expected_features, user_inputs))
-
-    # Ensure all required features are present and that the numerical values are used for the model
-    input_data = {}
-    for feature, user_input in zip(expected_features, user_inputs):
-        if feature in binary_maps:
-            # Convert 'Yes'/'No' to 1/0
-            input_data[feature] = binary_maps[feature].get(user_input, DEFAULT_VALUE)
-        elif feature in category_maps:
-            # Convert categorical values
-            input_data[feature] = category_maps[feature].get(user_input, DEFAULT_VALUE)
-        else:
-            # Default value for unexpected inputs
-            input_data[feature] = DEFAULT_VALUE
-
-    # Create a DataFrame for prediction
-    input_df = pd.DataFrame([input_data])[expected_features]
-
-    # Perform prediction
+# Prediction function
+def predict_with_model(*inputs):
     try:
-        prediction = rf_model.predict(input_df)[0]
-        return "High Risk" if prediction == 1 else "Low Risk"
-    except ValueError as e:
-        raise ValueError(f"Error during prediction: {e}") from e
-
-
-def build_interface():
-    """
-    Constructs the Gradio interface dynamically based on the dataset.
-    """
-    outputs = gr.Label(label="Prediction")
-    return gr.Interface(fn=predict_outcome, inputs=input_types, outputs=outputs)
-
-
-# Run the app
-if __name__ == "__main__":
-    interface = build_interface()
-    interface.launch()
+        model = load_model()  # Load the model dynamically
+        # Create a DataFrame for prediction
+        input_data = pd.DataFrame([inputs], columns=features)
+        # Make prediction
+        prediction = model.predict(input_data)
+        return f"Prediction: {'Risk of Heart Failure' if prediction[0] == 1 else 'No Risk'}"
+    except Exception as e:
+        return f"Error during prediction: {str(e)}"
+
+# Features derived from the CSV file
+features = ["Feature1", "Feature2", "Feature3"]  # Replace with actual feature names
+
+# Create input sliders
+input_sliders = [gr.Slider(0, 100, label=feature) for feature in features]
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=predict_with_model,
+    inputs=input_sliders,
+    outputs="text",
+    title="Heart Failure Prediction App",
+    description="Adjust the sliders to simulate feature values and predict heart failure risk.",
+)
+
+# Launch the app
+iface.launch()