Update app.py

app.py

@@ -1,39 +1,53 @@
 import gradio as gr
 import joblib
 import pandas as pd
+import json
 
-# Load models
+# Load files
 model = joblib.load("isolation_forest_model.joblib")
 scaler = joblib.load("standard_scaler.joblib")
 features = joblib.load("features_to_scale.joblib")
 
-def predict(*inputs):
+def predict(json_input):
+
     try:
+        # Convert JSON string to dict
+        data_dict = json.loads(json_input)
+
         # Create dataframe
-        data = pd.DataFrame([inputs], columns=features)
+        df = pd.DataFrame([data_dict])
+
+        # Ensure feature order
+        df = df[features]
 
         # Scale
-        scaled = scaler.transform(data)
+        scaled = scaler.transform(df)
 
         # Predict
         prediction = model.predict(scaled)
+        score = model.decision_function(scaled)
 
-        if prediction[0] == -1:
-            return "Anomaly Detected"
-        else:
-            return "Normal"
+        result = {
+            "prediction": "Anomaly"
+            if prediction[0] == -1
+            else "Normal",
 
-    except Exception as e:
-        return str(e)
+            "anomaly_score": float(score[0])
+        }
+
+        return result
 
-# Create input fields dynamically
-inputs = [gr.Number(label=f) for f in features]
+    except Exception as e:
+        return {"error": str(e)}
 
 demo = gr.Interface(
     fn=predict,
-    inputs=inputs,
-    outputs="text",
-    title="Anomaly Detection API"
+    inputs=gr.Textbox(
+        lines=20,
+        label="JSON Input"
+    ),
+    outputs="json",
+    title="HVAC Anomaly Detection"
 )
 
 demo.launch(server_name="0.0.0.0", server_port=7860)