Update app.py

app.py

@@ -7,57 +7,43 @@ model = joblib.load("isolation_forest_model.joblib")
 scaler = joblib.load("standard_scaler.joblib")
 features = joblib.load("features_to_scale.joblib")
 
-def predict(json_input):
 
+def predict(json_input):
     try:
-
         data = json.loads(json_input)
 
-        row = {}
-
-        # Global values
-        row["CWL_SEC_LOAD"] = data["cooling_load"]
-        row["OA_TEMP_WB"] = data["wet_bulb"]
+        units = data.get("units", [])
 
-        # Unit 1
-        row["CHL_COMP_SPD_CTRL_1"] = data["units"][0]["speed"]
-        row["CT_FAN_SPD_CTRL_1"] = data["units"][0]["fan"]
-        row["CHL_CW_FLOW_1"] = data["units"][0]["flow"]
-
-        # Unit 2
-        row["CHL_COMP_SPD_CTRL_2"] = data["units"][1]["speed"]
-        row["CT_FAN_SPD_CTRL_2"] = data["units"][1]["fan"]
-        row["CHL_CW_FLOW_2"] = data["units"][1]["flow"]
+        row = {
+            "CWL_SEC_LOAD": data.get("cooling_load", 0),
+            "OA_TEMP_WB": data.get("wet_bulb", 0),
+        }
 
-        # Unit 3
-        row["CHL_COMP_SPD_CTRL_3"] = data["units"][2]["speed"]
-        row["CT_FAN_SPD_CTRL_3"] = data["units"][2]["fan"]
-        row["CHL_CW_FLOW_3"] = data["units"][2]["flow"]
+        for i in range(3):
+            unit = units[i] if i < len(units) else {}
 
-        # Fill missing features
-        for col in features:
-            if col not in row:
-                row[col] = 0
+            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])
-        df = df[features]
+        df = pd.DataFrame([row]).reindex(columns=features, fill_value=0)
 
         scaled = scaler.transform(df)
 
-        prediction = model.predict(scaled)
-        score = model.decision_function(scaled)
+        pred = model.predict(scaled)[0]
+        score = model.decision_function(scaled)[0]
 
         return {
-            "prediction": "Fault"
-            if prediction[0] == -1
-            else "Normal",
-
-            "fault_score": float(score[0])
+            "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"),