| 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) |
