app.py

import joblib
import numpy as np
import pandas as pd
import gradio as gr

# --- 1. Load Model and Features ---
# Ensure these two files are uploaded to your Hugging Face Space folder!
try:
    model = joblib.load('ckd_model.joblib')
    # This list ensures input fields are created and data is fed in the correct order (24 features)
    FEATURE_COLUMNS = joblib.load('model_features.joblib') 
except FileNotFoundError:
    raise FileNotFoundError("Model or feature files not found. Ensure 'ckd_model.joblib' and 'model_features.joblib' are uploaded.")

# --- 2. Define the Prediction Function ---
def predict_ckd(*inputs):
    """
    Takes 24 inputs (features) from the Gradio interface and returns the prediction.
    """
    # Convert inputs (which come as a tuple from Gradio) into a NumPy array
    # and reshape it to match the model's required input format (1 sample, 24 features)
    input_data = np.array(inputs).reshape(1, -1)
    
    # 1. Make the raw prediction (0 or 1)
    prediction = model.predict(input_data)[0]
    
    # 2. Get the probability for the "Not CKD" class (class 1)
    # This is often more informative than just a 0/1 result
    probability_not_ckd = model.predict_proba(input_data)[0][1] 
    
    # Determine the final output text and confidence
    if prediction == 0:
        result_text = "Positive for Chronic Kidney Disease (CKD)"
        confidence = 1 - probability_not_ckd # Confidence in CKD
        color = "red"
    else: # prediction == 1
        result_text = "Negative for Chronic Kidney Disease (CKD)"
        confidence = probability_not_ckd # Confidence in Not CKD
        color = "green"

    # Return prediction with formatted confidence
    return f"<h2 style='color: {color};'>Prediction: {result_text}</h2><p>Confidence: {confidence:.2f} ({confidence*100:.0f}%)</p>"


# --- 3. Create the Gradio Interface Components ---

# Dynamically generate the 24 input components based on the feature list
input_components = []
for feature in FEATURE_COLUMNS:
    # Use a generic Textbox for input, as Gradio will automatically handle numeric types
    # You might customize these later (e.g., using gr.Slider for age, gr.Radio for binary inputs)
    input_components.append(gr.Textbox(label=feature.upper(), placeholder=f"Enter value for {feature}"))

# --- 4. Launch the Gradio Interface ---
# The output component is set to HTML to allow for colored text output
output_component = gr.HTML(label="Prediction Result")

# Combine the function, inputs, and outputs into a Gradio interface
iface = gr.Interface(
    fn=predict_ckd,
    inputs=input_components,
    outputs=output_component,
    title="CKD Prediction Model (Random Forest)",
    description="Enter the 24 clinical parameters below to predict the risk of Chronic Kidney Disease. **Note: For binary features (yes/no, present/notpresent), use the encoded numerical values (0 or 1) used during training.**"
)

# Launch the app (Hugging Face Spaces runs this automatically)
iface.launch(share=False)