app.py

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

# Load trained model
model = joblib.load("GRADIENTBOOSTING_WITH_SMOTE_model.pkl")

def predict_outbreak(*inputs):
    try:
        inputs = list(inputs)
        # Convert date to ordinal
        inputs[0] = datetime.datetime.strptime(inputs[0], "%Y-%m-%d").toordinal()
        inputs = np.array(inputs).reshape(1, -1)
        prediction = model.predict(inputs)
        return "Yes" if prediction[0] == 1 else "No"
    except Exception as e:
        return f"Error: {str(e)}"

input_components = [
    gr.Textbox(label="Date (YYYY-MM-DD)"),
    gr.Dropdown(["Winter", "Spring", "Summer", "Fall"], label="Season"),
    gr.Textbox(label="State"),
    gr.Number(label="Latitude"),
    gr.Number(label="Longitude"),
    gr.Number(label="Proximity to Water"),
    gr.Number(label="Humidity (%)"),
    gr.Checkbox(label="Wild Bird Migration Present"),
    gr.Checkbox(label="Neighboring Farm Outbreak"),
    gr.Dropdown(["Poultry", "Cattle", "Mixed", "Other"], label="Farm Type"),
    gr.Number(label="Vaccination Rate (%)"),
    gr.Number(label="Farm Size"),
    gr.Dropdown(["Internal", "External"], label="Feed Source"),
    gr.Dropdown(["River", "Well", "Tank", "Other"], label="Water Source"),
    gr.Number(label="Temperature (°C)"),
    gr.Checkbox(label="Recent Farm Visits"),
    gr.Checkbox(label="Other Animals Present"),
    gr.Number(label="Mortality Rate Last Week (%)"),
    gr.Checkbox(label="Protective Equipment Used"),
    gr.Dropdown(["Easy", "Difficult"], label="Farm Accessibility"),
    gr.Checkbox(label="Previous Outbreak")
]

output_component = gr.Textbox(label="Outbreak Within 7 Days?")

demo = gr.Interface(
    fn=predict_outbreak,
    inputs=input_components,
    outputs=output_component,
    title="Avian Influenza Risk Prediction",
    description="Predict avian influenza outbreak risk with a Gradient Boosting model."
)


# DO NOT call demo.launch() in Hugging Face Spaces