app.py

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

# =========================================================
# Load trained model
# =========================================================
# Upload your trained model file in the same Hugging Face Space
# Example model name: biomass_model.pkl

try:
    model = joblib.load("biomass_model.pkl")
    model_loaded = True
except:
    model_loaded = False


# =========================================================
# Prediction Function
# =========================================================
def predict_composition(cellulose, hemicellulose, lignin, moisture, ash):

    # Input array
    input_data = np.array([
        [cellulose, hemicellulose, lignin, moisture, ash]
    ])

    # If model exists
    if model_loaded:
        prediction = model.predict(input_data)

        return {
            "Predicted Biomass Composition": float(prediction[0])
        }

    # Dummy calculation if model not uploaded yet
    biomass_score = (
        0.35 * cellulose +
        0.25 * hemicellulose +
        0.20 * lignin -
        0.10 * moisture -
        0.10 * ash
    )

    return {
        "Estimated Biomass Score": round(biomass_score, 2)
    }


# =========================================================
# Gradio Interface
# =========================================================
with gr.Blocks(theme=gr.themes.Soft()) as demo:

    gr.Markdown(
        """
        # Biomass Composition Finder

        Enter biomass properties to estimate biomass composition.
        """
    )

    with gr.Row():
        cellulose = gr.Slider(0, 100, value=40, label="Cellulose (%)")
        hemicellulose = gr.Slider(0, 100, value=25, label="Hemicellulose (%)")

    with gr.Row():
        lignin = gr.Slider(0, 100, value=20, label="Lignin (%)")
        moisture = gr.Slider(0, 100, value=10, label="Moisture Content (%)")

    ash = gr.Slider(0, 50, value=5, label="Ash Content (%)")

    output = gr.JSON(label="Prediction Result")

    predict_btn = gr.Button("Predict")

    predict_btn.click(
        fn=predict_composition,
        inputs=[
            cellulose,
            hemicellulose,
            lignin,
            moisture,
            ash
        ],
        outputs=output
    )


# =========================================================
# Launch App
# =========================================================
demo.launch()