diff --git a/config.yaml b/demo/config.yaml
similarity index 100%
rename from config.yaml
rename to demo/config.yaml
diff --git a/demo.jpg b/demo/demo.jpg
similarity index 100%
rename from demo.jpg
rename to demo/demo.jpg
diff --git a/handler.cpp b/handler.cpp
index d3dabfd..984e0f7 100644
--- a/handler.cpp
+++ b/handler.cpp
@@ -5,8 +5,6 @@
 #ifdef __linux__
 #include <unistd.h>
 #endif
-#define STB_IMAGE_WRITE_IMPLEMENTATION
-#include "stb_image_write.h"
 #include "stb_image.h"
 #include <mutex>
 #include "utils.h"
@@ -37,13 +35,6 @@ using namespace cv;
 #include <iostream>
 #include <string>
 #include "image.h"
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-#include <unistd.h>
-#include<sys/socket.h>	//socket
-#include<sys/types.h>
-#include<netinet/in.h>
 image make_empty_image(int w, int h, int c)
 {
     image out;
@@ -97,7 +88,7 @@ cv::Mat image_to_mat(image img)
     int height = img.h;
     cv::Mat mat = cv::Mat(height, width, CV_8UC(channels));
     int step = mat.step;
-
+ 
     for (int y = 0; y < img.h; ++y) {
         for (int x = 0; x < img.w; ++x) {
             for (int c = 0; c < img.c; ++c) {
@@ -107,19 +98,24 @@ cv::Mat image_to_mat(image img)
         }
     }
     return mat;
-}
+}   
+    std::vector<std::string> classesNames;
+    image im;
+    cv::Mat frame;
+    cv::Mat gray;
+    int h=0;
+    int w=0;
+    int channels;
     char ntype = 'y';
     const char *config_filename = "../demo/config.yaml";
     const char * net = "../demo/yolo4_fp32.rt";
-//    const char * img_path = "../demo/demo.jpg";
     char * img_data;
     bool show = false;
     bool verbose;
-    int classes, map_points, map_levels;
+    int classes, map_points, map_levels ,len;
+    string ustring;
     float map_step, IoU_thresh, conf_thresh;
     tk::dnn::Yolo3Detection yolo;
- //   tk::dnn::CenternetDetection cnet;
-//    tk::dnn::MobilenetDetection mbnet;
     tk::dnn::DetectionNN *detNN;
     int n_classes = classes;
     std::vector<tk::dnn::Frame> images;
@@ -145,48 +141,27 @@ string name_from_path(string path)
     removePathAndExtension(net, net_name);
     std::cout<<"Network: "<<net_name<<std::endl;
 
-    //open files (if needed)
-    //std::ofstream times, memory, coco_json;  
     int n_classes = classes;
-//    float conf_threshold=0.001;
     detNN = &yolo;
     detNN->init(net, n_classes, 1, conf_thresh);
-
-    //read images 
-   // std::ifstream all_labels(labels_path);
-//    std::cout << timeSinceEpochMillisec() << std::endl;
-    std::string l_filename;
-
-    if(show)
-        cv::namedWindow("detection", cv::WINDOW_NORMAL);
     return;}
-//    init_bag();
-//    int images_done;
- //   for (images_done=0 ; std::getline(all_labels, l_filename) && images_done < n_images ; ++images_done) {
-   //     std::cout <<COL_ORANGEB<< "Images done:\t" << images_done<< "\n"<<COL_END;
-    void handler::handle_post(http_request request){
-  //  init_bag();
 
-//    const char * img_path=
-    //tk::dnn::Frame f;
+
+    void handler::handle_post(http_request request){
     BOOST_LOG_TRIVIAL(info) << "[" << name_from_path(string(__FILE__)) << "  " << __LINE__ << "] " << request.to_string();
 
     map<utility::string_t, utility::string_t> http_get_vars = uri::split_query(request.request_uri().query());
     map<utility::string_t, utility::string_t>::iterator it = http_get_vars.find("name");
-//    std::cout<<request<<"\n";
-    //If 'name' is not in the query.
-    int len;
+   // int len;
     if(it == http_get_vars.end())
     {
         BOOST_LOG_TRIVIAL(error) << "[" << name_from_path(string(__FILE__)) << "  " << __LINE__ << "] " << "Image name not passed in query.";
         request.reply(status_codes::UnprocessableEntity,"Please pass image name in the query.");
         return;
     }https://github.com/baggageai/baggageai-code-one.git
-    std::cout<<http_get_vars["name"]<<"\n";
+   // std::cout<<http_get_vars["name"]<<"\n";
     string image_name = (string)http_get_vars["name"];
-    string ustring;
-    //reading binary data and storing it in a pointer
-//    std::vector<std::string> img_data=request.extract_vector();
+   // string ustring;
     request.extract_vector().then([image_name, &ustring, &len](vector<unsigned char> v) {
                 ustring = {v.begin(),v.end()};
                 len = ustring.size();
@@ -194,45 +169,25 @@ string name_from_path(string path)
     //printSize(ustring);
     BOOST_LOG_TRIVIAL(info) << "[" << name_from_path(string(__FILE__)) << "  " << __LINE__ << "] " << "Detection Started";
     unsigned char * sockData = (unsigned char *)ustring.c_str();
-   // std::cout<<ch<<"char"<<len<<"len"<<"\n";
-//    cv::Mat frame = cv::imread("demo.jpg", cv::IMREAD_COLOR);
-
-    int channels;
-    int h=0,w=0;
-    image im;
     unsigned char *data = stbi_load_from_memory(sockData, len, &w, &h, &channels, 0);
     im = load_image_file(data, channels, 0, 0, w, h);
-//   float *data = stbi_loadf_from_memory(ch, len, &w, &h, &channels, 0); 
-std::cout<<"imagedata"<<h<<"w"<<w<<"ch"<<channels<<"\n";
-    string outputPath = "/home/baggageai/files/build/testp.png";
-    // Write pictures
-   // stbi_write_png(outputPath.c_str(), w, h, channels, data, 0);
-    //cv::Mat frame=image_to_mat(data,h,w,channels);
-//    cv::Mat frame = cv::Mat(data,h, w, CV_16UC(channels));
- //   cv::imwrite("/home/baggageai/files/build/test.png", img);
- //   cv::Mat gray;
-    cv::Mat gray=image_to_mat(im);
-    cv::Mat frame;
-cv::Mat in[] = {gray, gray,gray};
-cv::merge(in, 3, frame);
-    cv::imwrite("/home/baggageai/files/build/test1.png", frame);
-    std::cout<<frame.channels();
+   // free(data);
+    //free(sockData);
+    gray=image_to_mat(im);
+//    free(im);
+    cv::Mat in[] = {gray, gray,gray};
+    cv::merge(in, 3, frame);
     std::vector<cv::Mat> batch_frames;
     batch_frames.push_back(frame);
     int height = frame.rows;
     int width = frame.cols;
-    std::cout<<height<<"width"<<width<<"\n";
-//    if(!frame.data) 
- //       break;
     std::vector<cv::Mat> batch_dnn_input;
     batch_dnn_input.push_back(frame.clone());
-    std::cout<<"test1"<<"\n";
     //inference 
     detected_bbox.clear();
     detNN->update(batch_dnn_input,1);
     detNN->draw(batch_frames);
     detected_bbox = detNN->detected;
-    std::cout<<"test2"<<"\n";
     try{
     json::value response;
     vector<json::value> jsonArray;
@@ -247,36 +202,32 @@ cv::merge(in, 3, frame);
         b.h = d.h / height;
         b.prob = d.prob;
         b.cl = d.cl;
-        //f.det.push_back(b);
+        f.det.push_back(b);
 	
 	json::value detection;
-        detection["label"] = json::value::number(b.cl);
-        detection["x"] = json::value::number(b.x);
-        detection["y"] = json::value::number(b.y);
-        detection["w"] = json::value::number(b.w);
-        detection["h"] = json::value::number(b.h);
+        detection["label"] = json::value::string(string("battery"));
+        detection["x"] = json::value::number(d.x);
+        detection["y"] = json::value::number(d.y);
+        detection["w"] = json::value::number(d.w);
+        detection["h"] = json::value::number(d.h);
         detection["prob"] = json::value::number(b.prob);
         jsonArray.push_back(detection);
         std::cout<< d.cl << " "<< d.prob << " "<< b.x << " "<< b.y << " "<< b.w << " "<< b.h <<"\n";
-
-        if(show)// draw rectangle for detection
-            cv::rectangle(batch_frames[0], cv::Point(d.x, d.y), cv::Point(d.x + d.w, d.y + d.h), cv::Scalar(0, 0, 255), 2);             
-    }
-    //images.push_back(f);
-    
-    if(show){
-        cv::imshow("detection", batch_frames[0]);
-        cv::waitKey(0);
-    }
+}
             response["detections"] = json::value::array(jsonArray);   //JSON Response
+//        free(jsonArray);
         request.reply(status_codes::OK,response.serialize());
-//        free(detectboxes);
+//        free(detected_bbox);
         BOOST_LOG_TRIVIAL(info) << "[" << name_from_path(string(__FILE__)) << "  " << __LINE__ << "] " << "Detection Completed and Response sent";
     }
     catch (exception const& e) {
         BOOST_LOG_TRIVIAL(error) << "[" << name_from_path(string(__FILE__)) << "  " << __LINE__ << "] " << e.what();
         request.reply(status_codes::BadRequest, e.what());
     }
+  //   free(data);
+//    free(sockData);
+    frame.release();
+    gray.release();
   //  std::cout << timeSinceEpochMillisec() << std::endl;
     return ;
     }