app

app.py

@@ -1,61 +1,48 @@
 import gradio as gr
-import pandas as pd
+import joblib
 import json
-from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import StandardScaler, OneHotEncoder
-
-# Load selected features from JSON file
-with open("selected_features.json", "r") as file:
-    selected_features = json.load(file)
-
-def preprocess_data(data):
-    # Identify numerical and categorical columns
-    numerical_cols = [col for col in data.columns if data[col].dtype in ['int64', 'float64']]
-    categorical_cols = [col for col in data.columns if col not in numerical_cols]
-    
-    # Preprocessing pipeline
-    preprocessor = ColumnTransformer(
-        transformers=[
-            ('num', StandardScaler(), numerical_cols),
-            ('cat', OneHotEncoder(sparse_output=False, drop='first'), categorical_cols)
-        ]
-    )
-    
-    # Apply preprocessing
-    processed_data = preprocessor.fit_transform(data)
-    feature_names = numerical_cols + list(preprocessor.named_transformers_['cat'].get_feature_names_out(categorical_cols))
-    return pd.DataFrame(processed_data, columns=feature_names)
-
-def process_manual_input(**kwargs):
-    # Create a DataFrame for a single patient from keyword arguments
-    input_data = pd.DataFrame([kwargs])
-    
-    # Preprocess the data
-    processed_data = preprocess_data(input_data)
-    return processed_data.to_csv(index=False)
-
-# GUI inputs for each feature
-gui_inputs = []
-for feature in selected_features:
-    if feature.startswith("Had"):  # Binary categorical features
-        gui_inputs.append(gr.Radio(label=feature, choices=["Yes", "No"], value="No"))
-    elif feature in ["BMI", "WeightInKilograms", "HeightInMeters"]:  # Numerical features
-        gui_inputs.append(gr.Slider(label=feature, minimum=0, maximum=300, step=0.1, value=25))
-    elif feature == "PhysicalHealthDays":  # Numerical feature with a smaller range
-        gui_inputs.append(gr.Slider(label=feature, minimum=0, maximum=30, step=1, value=5))
-    elif feature == "SleepHours":  # Hours of sleep
-        gui_inputs.append(gr.Slider(label=feature, minimum=0, maximum=24, step=0.5, value=8))
-    else:  # Default for any remaining numerical features
-        gui_inputs.append(gr.Slider(label=feature, minimum=0, maximum=100, step=1, value=50))
-
-# Create the Gradio app interface
-interface = gr.Interface(
-    fn=process_manual_input,
-    inputs=gui_inputs,
-    outputs=gr.Textbox(label="Processed Data (CSV Format)"),
-    title="Single Patient Data Preprocessor",
-    description="Input data for a single patient using sliders and radio buttons. The data will be preprocessed and displayed as CSV."
+import numpy as np
+
+# Load the trained model
+model = joblib.load('trained_model.pkl')
+
+# Load feature names
+with open('selected_features.json', 'r') as f:
+    feature_names = json.load(f)
+
+# Define prediction function
+def predict_heart_failure(*args):
+    """
+    Predict heart failure likelihood based on input features.
+
+    Args:
+        *args: Input values for each feature.
+
+    Returns:
+        str: Prediction result.
+        str: Prediction probability as a percentage.
+    """
+    input_data = np.array(args).reshape(1, -1)
+    prediction = model.predict(input_data)
+    probability = model.predict_proba(input_data)
+    result = "Likely" if prediction[0] == 1 else "Unlikely"
+    return result, f"{probability[0][1] * 100:.2f}%"
+
+# Create Gradio interface
+inputs = [gr.inputs.Number(label=feature) for feature in feature_names]
+outputs = [
+    gr.outputs.Textbox(label="Prediction"),
+    gr.outputs.Textbox(label="Probability (%)")
+]
+
+app = gr.Interface(
+    fn=predict_heart_failure,
+    inputs=inputs,
+    outputs=outputs,
+    title="Heart Failure Prediction",
+    description="Enter the values for the features to predict the likelihood of heart failure."
 )
 
 # Launch the app
-interface.launch()
+if __name__ == "__main__":
+    app.launch()