import gradio as gr
import joblib
import pandas as pd
import json

model = joblib.load("isolation_forest_model.joblib")
scaler = joblib.load("standard_scaler.joblib")
features = joblib.load("features_to_scale.joblib")


def predict(json_input):
    try:
        data = json.loads(json_input)

        units = data.get("units", [])

        row = {
            "CWL_SEC_LOAD": data.get("cooling_load", 0),
            "OA_TEMP_WB": data.get("wet_bulb", 0),
        }

        for i in range(3):
            unit = units[i] if i < len(units) else {}

            row[f"CHL_COMP_SPD_CTRL_{i+1}"] = unit.get("speed", 0)
            row[f"CT_FAN_SPD_CTRL_{i+1}"] = unit.get("fan", 0)
            row[f"CHL_CW_FLOW_{i+1}"] = unit.get("flow", 0)

        df = pd.DataFrame([row]).reindex(columns=features, fill_value=0)

        scaled = scaler.transform(df)

        pred = model.predict(scaled)[0]
        score = model.decision_function(scaled)[0]

        return {
            "prediction": "Fault" if pred == -1 else "Normal",
            "fault_score": float(score)
        }

    except json.JSONDecodeError:
        return {"error": "Invalid JSON"}
    except Exception as e:
        return {"error": str(e)}


demo = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(lines=20, label="JSON Input"),
    outputs="json",
    title="HVAC Fault Detection"
)

demo.launch(server_name="0.0.0.0", server_port=7860)