diff --git a/Frontend/src/pages/UserPanel/Ai.jsx b/Frontend/src/pages/UserPanel/Ai.jsx
index 19c2ffd..db9f1d2 100644
--- a/Frontend/src/pages/UserPanel/Ai.jsx
+++ b/Frontend/src/pages/UserPanel/Ai.jsx
@@ -33,9 +33,9 @@ const Ai = () => {
         throw new Error("Prediction request failed");
       }
 
-      const data = await response.json();
+      const result = await response.json();
       // Assuming the API returns a JSON object with an "output" field.
-      setPrediction(data.output || "No prediction returned");
+      setPrediction(result.data || "No prediction returned");
     } catch (err) {
       console.error("Error during prediction:", err);
       setError(err.message);
diff --git a/models/server.js b/models/server.js
index 7649954..c82087c 100644
--- a/models/server.js
+++ b/models/server.js
@@ -3,10 +3,19 @@ const express = require("express");
 const { spawn } = require("child_process");
 const path = require("path");
 const upload = require("./Middleware/multer");
+const cors = require("cors");
 
 const app = express();
 const PORT = 3000;
 
+const corsOptions = {
+  origin: "http://localhost:5173",
+  methods: "GET,HEAD,PUT,PATCH,POST,DELETE",
+  credentials: true,
+};
+
+app.use(cors(corsOptions));
+
 // Endpoint to run the Python script
 app.post("/predict", upload.single("image"), (req, res) => {
   console.log("File is uploaded successfully" + req.file?.path);
@@ -22,7 +31,13 @@ app.post("/predict", upload.single("image"), (req, res) => {
   // Collect data from the script
   pythonProcess.stdout.on("data", (data) => {
     console.log(`Output: ${data}`);
-    res.send(data.toString()); // Send the output back to the client
+
+    res.status(200).json({
+      success: true,
+      message: "Image uploaded successfully",
+      data: data.toString(),
+    });
+    //res.send(data.toString()); // Send the output back to the client
   });
 
   // Handle errors