Update app.py

app.py

@@ -1,31 +1,50 @@
-from flask import Flask, request, jsonify
+import os
 import joblib
 import numpy as np
-import traceback
-from supabase import create_client
-import os
+from flask import Flask, request, jsonify
 from supabase import create_client
+from dotenv import load_dotenv
+
+# -----------------------------
+# Load Environment Variables
+# -----------------------------
+load_dotenv()
 
 SUPABASE_URL = os.getenv("SUPABASE_URL")
 SUPABASE_KEY = os.getenv("SUPABASE_KEY")
 
 supabase = create_client(SUPABASE_URL, SUPABASE_KEY)
-app = Flask(__name__)
 
-# Load model
+# -----------------------------
+# Load Model
+# -----------------------------
 model = joblib.load("predictive_model.pkl")
 
+# -----------------------------
+# Flask App
+# -----------------------------
+app = Flask(__name__)
+
+# -----------------------------
+# Health Check
+# -----------------------------
 @app.route("/", methods=["GET"])
 def home():
-    return "Predictive Maintenance API Running 🚀"
+    return jsonify({
+        "status": "API is running",
+        "message": "Predictive Maintenance Backend Active"
+    })
 
 
+# -----------------------------
+# Prediction Endpoint
+# -----------------------------
 @app.route("/predict", methods=["POST"])
 def predict():
     try:
         data = request.get_json()
 
-        # Expecting named features
+        # Extract features (must match training order)
         features = [
             data["Air_temperature"],
             data["Process_temperature"],
@@ -36,19 +55,57 @@ def predict():
             data["Type_M"]
         ]
 
-        prediction = model.predict([features])
+        features_array = np.array([features])
+
+        prediction = model.predict(features_array)
+        probability = model.predict_proba(features_array)[0][1]
+
+        status_text = "Failure" if prediction[0] == 1 else "No Failure"
+
+        # -----------------------------
+        # Insert into Supabase
+        # -----------------------------
+        supabase.table("machine_logs").insert({
+            "air_temperature": data["Air_temperature"],
+            "process_temperature": data["Process_temperature"],
+            "rotational_speed": data["Rotational_speed"],
+            "torque": data["Torque"],
+            "tool_wear": data["Tool_wear"],
+            "type_l": data["Type_L"],
+            "type_m": data["Type_M"],
+            "prediction": int(prediction[0])
+        }).execute()
 
         return jsonify({
             "prediction": int(prediction[0]),
-            "status": "No Failure" if prediction[0] == 0 else "Machine Failure"
+            "status": status_text,
+            "failure_probability": float(round(probability, 4))
         })
 
     except Exception as e:
-        return jsonify({
-            "error": str(e),
-            "trace": traceback.format_exc()
-        })
+        return jsonify({"error": str(e)}), 500
+
+
+# -----------------------------
+# Get Latest Logs (Dashboard)
+# -----------------------------
+@app.route("/logs", methods=["GET"])
+def get_logs():
+    try:
+        response = supabase.table("machine_logs") \
+            .select("*") \
+            .order("created_at", desc=True) \
+            .limit(10) \
+            .execute()
+
+        return jsonify(response.data)
+
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
 
 
+# -----------------------------
+# Run App (for local testing)
+# -----------------------------
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
+    app.run(host="0.0.0.0", port=7860)