c

diff --git a/.gitignore b/.gitignore
deleted file mode 100644
index 441aad5..0000000
--- a/.gitignore
+++ /dev/null
@@ -1,145 +0,0 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-.hypothesis/
-.pytest_cache/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-.python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-work/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# CMake
-CMakeFiles/
-CMakeCache.txt
-CMakeScripts/
-CTestTestfile.cmake
-cmake_install.cmake
-install_manifest.txt
-*.cmake
-*.cmake.in
-*.db
-
-#test files
-test1.py
diff --git a/requirements.txt b/requirements.txt
index 661750d..e69de29 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,20 +0,0 @@
-Flask==2.2.5
-Flask-Mail==0.9.1
-Flask-SocketIO==5.1.1
-Flask-SQLAlchemy==2.5.1
-SQLAlchemy==1.4.47
-PyYAML==6.0
-Pillow==9.0.1
-numpy==1.21.6
-opencv-python==4.6.0.66
-ipywidgets==7.7.2
-matplotlib==3.5.3
-h5py==3.7.0
-torch==1.13.1
-kornia==0.7.3
-kornia_rs==0.1.5
-torchvision==0.14.1
-scikit-learn==1.3.2
-pytorch-lightning==2.2.5
-pytorch-metric-learning==2.6.0
-modal==0.64.102
diff --git a/src/UNav_core/src/feature/global_extractor.py b/src/UNav_core/src/feature/global_extractor.py
index c454cfa..408489b 100644
--- a/src/UNav_core/src/feature/global_extractor.py
+++ b/src/UNav_core/src/feature/global_extractor.py
@@ -6,31 +6,23 @@ import torch
 
 class Global_Extractors():
     def __init__(self, config):
-        self.root = config['IO_root']
+        self.root=config['IO_root']
         self.extractor = config['feature']['global']
-        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")  # Set device
 
     def netvlad(self, content):
-        model = NetVladFeatureExtractor(join(self.root, content['ckpt_path']), 
-                                        arch=content['arch'],
-                                        num_clusters=content['num_clusters'],
-                                        pooling=content['pooling'], 
-                                        vladv2=content['vladv2'], 
-                                        nocuda=content['nocuda'])
-        if hasattr(model, 'model'):  # Check if it has a model attribute
-            model.model = model.model.to(self.device)  # Move model to the GPU
-         # Move NetVlad model to GPU if available
-        return model
-
-    def mixvpr(self, content):
+        return NetVladFeatureExtractor(join(self.root,content['ckpt_path']), arch=content['arch'],
+         num_clusters=content['num_clusters'],
+         pooling=content['pooling'], vladv2=content['vladv2'], nocuda=content['nocuda'])
+    
+    def mixvpr(self,content):
         model = VPRModel(backbone_arch=content["backbone_arch"], 
-                         layers_to_crop=[content['layers_to_crop']],
-                         agg_arch=content['agg_arch'],
-                         agg_config=content['agg_config'],
-                         )
-        state_dict = torch.load(content["ckpt_path"], map_location=self.device)  # Load model state to the same device
+                        layers_to_crop=[content['layers_to_crop']],
+                        agg_arch=content['agg_arch'],
+                        agg_config=content['agg_config'],
+                        )
+
+        state_dict = torch.load(content["ckpt_path"])
         model.load_state_dict(state_dict)
-        model = model.to(self.device)  # Move MixVPR model to GPU if available
         model.eval()
         return model
 
@@ -43,4 +35,4 @@ class Global_Extractors():
             if extractor == 'vlad':
                 pass
             if extractor == 'bovw':
-                pass
+                pass
\ No newline at end of file
diff --git a/src/UNav_core/src/track/hierarchical_localization.py b/src/UNav_core/src/track/hierarchical_localization.py
index 44d8ecc..51da7c2 100644
--- a/src/UNav_core/src/track/hierarchical_localization.py
+++ b/src/UNav_core/src/track/hierarchical_localization.py
@@ -56,33 +56,25 @@ class Coarse_Locator:
         return torch.tensor(descriptors, dtype=torch.float32).to(self.device), segment_ids
 
     def coarse_vpr(self, image):
-            """
-            Perform coarse visual place recognition.
-            :param image: The query image for which to find the place.
-            :return: Top-k matches and a boolean indicating if the corresponding segment is found.
-            """
-            print("Starting coarse_vpr function")
-            
-            # Extract global descriptor from the query image
-            query_desc = self.global_extractor(image).to(self.device)
-            print(f"Extracted query descriptor: {query_desc.shape}")
-    
-            # Compute similarity between the query descriptor and database descriptors
-            sim = torch.einsum('id,jd->ij', query_desc, self.global_descriptors)
-            print(f"Computed similarity matrix: {sim.shape}")
-            
-            topk_indices = torch.topk(sim, self.config['retrieval_num'], dim=1).indices.cpu().numpy()
-            print(f"Top-k indices: {topk_indices}")
-    
-            # Retrieve the corresponding segment IDs for the top-k matches
-            topk_segments = self.segment_ids[topk_indices[0]]
-            print(f"Top-k segments: {topk_segments}")
-            
-            # Analyze top-k results
-            segment, success = self.analyze_topk_results(topk_segments)
-            print(f"Analyzed top-k results: segment={segment}, success={success}")
-            
-            return topk_segments, segment, success
+        """
+        Perform coarse visual place recognition.
+        :param image: The query image for which to find the place.
+        :return: Top-k matches and a boolean indicating if the corresponding segment is found.
+        """
+        # Extract global descriptor from the query image
+        query_desc = self.global_extractor(image).to(self.device)
+
+        # Compute similarity between the query descriptor and database descriptors
+        sim = torch.einsum('id,jd->ij', query_desc, self.global_descriptors)
+        topk_indices = torch.topk(sim, self.config['retrieval_num'], dim=1).indices.cpu().numpy()
+
+        # Retrieve the corresponding segment IDs for the top-k matches
+        topk_segments = self.segment_ids[topk_indices[0]]
+        
+        # Analyze top-k results
+        segment, success = self.analyze_topk_results(topk_segments)
+        
+        return topk_segments, segment, success
     
     def get_topk_segments(self, topk_indices):
         """
@@ -101,9 +93,6 @@ class Coarse_Locator:
         :param topk_segments: List of segment IDs corresponding to the top-k matches.
         :return: The most likely segment and a boolean indicating if localization succeeded.
         """
-        print("Starting analyze_topk_results function")
-        print(f"Top-k segments: {topk_segments}")
-    
         segment_counts = {}
         
         # First, count occurrences of each segment in topk_segments
@@ -113,11 +102,9 @@ class Coarse_Locator:
             else:
                 segment_counts[segment] = 1
         
-        print(f"Segment counts: {segment_counts}")
-        
         # Initialize a dictionary to accumulate counts for segments and their neighbors
         segment_wt_neighbor_counts = {}
-    
+
         # Accumulate counts including neighbor segments
         for segment, count in segment_counts.items():
             # Start with the count of the segment itself
@@ -132,13 +119,12 @@ class Coarse_Locator:
             # Record the accumulated count for the segment
             segment_wt_neighbor_counts[segment] = total_count
         
-        print(f"Segment with neighbor counts: {segment_wt_neighbor_counts}")
-        
         # Determine the segment with the highest total count
         most_likely_segment = max(segment_wt_neighbor_counts, key=segment_wt_neighbor_counts.get)
         success = (segment_wt_neighbor_counts[most_likely_segment] / len(topk_segments)) >= 0.1
         
         return most_likely_segment, success
+
     
     def get_segment_id(self, index):
         """
@@ -239,20 +225,15 @@ class Hloc():
         with torch.inference_mode():  # Use torch.no_grad during inference
             image_np = np.array(image)
             feats0 = self.local_feature_extractor(image_np)
-            image_np = np.array(image)
-            feats0 = self.local_feature_extractor(image_np)
         pts0_list,pts1_list,lms_list=[],[],[]
         max_len=0
         
-        valid_db_frame_name = []
-        
         valid_db_frame_name = []
         for i in topk[0]:
             pts0,pts1,lms=self.local_feature_matcher.lightglue(i, feats0)
             
             feat_inliner_size=pts0.shape[0]
             if feat_inliner_size>self.thre:
-                valid_db_frame_name.append(self.db_name[i])
                 valid_db_frame_name.append(self.db_name[i])
                 pts0_list.append(pts0)
                 pts1_list.append(pts1)
@@ -263,7 +244,6 @@ class Hloc():
         del self.query_desc, feats0
         torch.cuda.empty_cache()
         return valid_db_frame_name, pts0_list,pts1_list,lms_list,max_len
-        return valid_db_frame_name, pts0_list,pts1_list,lms_list,max_len
     
     def feature_matching_superglue(self,image,topk):
         """
@@ -340,48 +320,33 @@ class Hloc():
         #     return None, torch.tensor([]), None
 
 
-    def pnp(self, image, feature2D, landmark3D):
+    def pnp(self,image,feature2D,landmark3D):
         """
         Start Perspective-n-points:
             Estimate the current location using implicit distortion model
         """
-        print("Starting pnp function")
-        if feature2D.size()[0] > 0:
-            print("Feature2D size is greater than 0")
-
-            if not isinstance(image, np.ndarray):
-                image = np.array(image)
-            
+        if feature2D.size()[0]>0:
             height, width, _ = image.shape
-            print(f"Image shape: height={height}, width={width}")
-            feature2D, landmark3D = feature2D.cpu().numpy(), landmark3D.cpu().numpy()
-            print("Converted feature2D and landmark3D to numpy arrays")
+            feature2D, landmark3D=feature2D.cpu().numpy(),landmark3D.cpu().numpy()
             out, p2d_inlier, p3d_inlier = coarse_pose(feature2D, landmark3D, np.array([width / 2, height / 2]))
-            print(f"Coarse pose output: {out}")
             self.list_2d.append(p2d_inlier)
             self.list_3d.append(p3d_inlier)
             self.initial_poses.append(out['pose'])
             self.pps.append(out['pp'])
-            print("Appended inliers and initial pose to lists")
             if len(self.list_2d) > self.config['implicit_num']:
-                print("List sizes exceeded implicit_num, popping oldest elements")
                 self.list_2d.pop(0)
                 self.list_3d.pop(0)
                 self.initial_poses.pop(0)
                 self.pps.pop(0)
-            pose = pose_multi_refine(self.list_2d, self.list_3d, self.initial_poses, self.pps, self.rot_base, self.T)
-            print(f"Refined pose: {pose}")
-    
-            # Reset reload num
+            pose = pose_multi_refine(self.list_2d, self.list_3d, self.initial_poses, self.pps,self.rot_base,self.T)
+
+            #reset reload num
             self.current_reload_num = 0
-            print("Reset current_reload_num to 0")
         else:
-            pose = None
+            pose =None
             self.logger.warning("!!!Cannot localize at this point, please take some steps or turn around!!!")
-            print("Feature2D size is 0, cannot localize")
-        print("Returning pose")
         return pose
-    
+
     def _determine_next_segment(self, candidates):
         candidate_histogram = {}
         max_counts = 0
diff --git a/src/modal_functions/config.yaml b/src/modal_functions/config.yaml
deleted file mode 100644
index 9992ff6..0000000
--- a/src/modal_functions/config.yaml
+++ /dev/null
@@ -1,43 +0,0 @@
-server:
-  host: "0.0.0.0"
-  port: 5000
-
-location:
-  place: New_York_City
-  building: LightHouse
-  floor: "6_floor"
-  scale: 0.01098358101
-
-IO_root: "/root/UNav-IO"
-
-devices: "cuda:0"
-
-hloc:
-  retrieval_num: 50
-  implicit_num: 1
-  ransac_thre: 10
-  # match_type: 'nvs'
-  match_type: "lightglue"
-  batch_mode: true
-  load_all_maps: False
-  map_loading_keyframes_reload: 0
-
-feature:
-  global:
-    netvlad:
-      ckpt_path: "parameters/paper"
-      arch: "vgg16"
-      vladv2: true
-      nocuda: false
-      num_clusters: 64
-      pooling: "netvlad"
-
-  local:
-    superpoint+lightglue:
-      detector_name: superpoint
-      nms_radius: 4
-      max_keypoints: 4096
-      matcher_name: lightglue
-      match_conf:
-        width_confidence: -1
-        depth_confidence: -1
diff --git a/src/modal_functions/logger_utils.py b/src/modal_functions/logger_utils.py
deleted file mode 100644
index 7130edf..0000000
--- a/src/modal_functions/logger_utils.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import logging
-
-
-def setup_logger(name="server_logger", level=logging.DEBUG):
-    logger = logging.getLogger(name)
-    logger.setLevel(level)
-
-    handler = logging.StreamHandler()
-    formatter = logging.Formatter(
-        "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-    )
-    handler.setFormatter(formatter)
-
-    logger.addHandler(handler)
-    return logger
diff --git a/src/modal_functions/misc/sample.png b/src/modal_functions/misc/sample.png
deleted file mode 100644
index f2cbe38..0000000
Binary files a/src/modal_functions/misc/sample.png and /dev/null differ
diff --git a/src/modal_functions/misc/sample2.png b/src/modal_functions/misc/sample2.png
deleted file mode 100644
index d374275..0000000
Binary files a/src/modal_functions/misc/sample2.png and /dev/null differ
diff --git a/src/modal_functions/misc/sample3.png b/src/modal_functions/misc/sample3.png
deleted file mode 100644
index aa3e5a8..0000000
Binary files a/src/modal_functions/misc/sample3.png and /dev/null differ
diff --git a/src/modal_functions/modal_config.py b/src/modal_functions/modal_config.py
deleted file mode 100644
index 6215848..0000000
--- a/src/modal_functions/modal_config.py
+++ /dev/null
@@ -1,68 +0,0 @@
-from modal import App, Image, Mount, NetworkFileSystem, Volume
-from pathlib import Path
-
-volume = Volume.from_name("Visiondata")
-
-MODEL_URL = "https://download.pytorch.org/models/vgg16-397923af.pth"
-LIGHTGLUE_URL = "https://github.com/cvg/LightGlue/releases/download/v0.1_arxiv/superpoint_lightglue.pth"
-
-# Get the current file's directory
-current_dir = Path(__file__).resolve().parent
-
-# Construct the path to the src directory
-local_dir = current_dir / ".."
-
-
-def download_torch_hub_weights():
-    import torch
-    model_weights = torch.hub.load_state_dict_from_url(MODEL_URL, progress=True)
-    torch.save(model_weights, "vgg16_weights.pth")
-
-    lightglue_weights = torch.hub.load_state_dict_from_url(LIGHTGLUE_URL, progress=True)
-    torch.save(lightglue_weights,"superpoint_lightglue_v0-1_arxiv-pth")
-
-
-app = App(
-    name="unav-server",
-    mounts=[
-        Mount.from_local_dir(local_dir.resolve(), remote_path="/root"),
-        Mount.from_local_file(
-            "modal_functions/config.yaml", remote_path="/root/config.yaml"
-        ),
-    ],
-)
-
-unav_image = (
-    Image.debian_slim(python_version="3.8")
-    .run_commands(
-        "apt-get update",
-        "apt-get install -y cmake git libgl1-mesa-glx libceres-dev libsuitesparse-dev libgoogle-glog-dev libgflags-dev libatlas-base-dev libeigen3-dev",
-    )
-    .run_commands("git clone https://gitlab.com/libeigen/eigen.git eigen")
-    .workdir("/eigen")
-    .run_commands(
-        "git checkout 3.4",
-        "mkdir build",
-    )
-    .workdir("/eigen/build")
-    .run_commands(
-        "cmake ..",
-        "make",
-        "make install",
-    )
-    .workdir("/")
-    .run_commands(
-        "git clone https://github.com/cvg/implicit_dist.git implicit_dist",
-    )
-    .workdir("/implicit_dist")
-    .run_commands(
-        "ls",
-        "python3 -m venv .venv",
-        ". .venv/bin/activate",
-        "pip install .",
-        "pip freeze",
-    )
-    .pip_install_from_requirements("modal_functions/modal_requirements.txt")
-    .workdir("/root")
-    .run_function(download_torch_hub_weights)
-)
diff --git a/src/modal_functions/modal_requirements.txt b/src/modal_functions/modal_requirements.txt
deleted file mode 100644
index 769a6e0..0000000
--- a/src/modal_functions/modal_requirements.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-Pillow==9.0.1
-numpy==1.21.6
-unav==0.1.40
-prettytable==3.11.0
-timm==1.0.9
-einops==0.8.0
-ipywidgets==7.7.2
-matplotlib==3.5.3
-h5py==3.7.0
-torch==1.13.1
-kornia==0.7.3
-kornia_rs==0.1.5
-torchvision==0.14.1
-scikit-learn==1.3.2
-pytorch-lightning==2.2.5
-pytorch-metric-learning==2.6.0
-poselib
-Flask==2.2.5
-Flask-Mail==0.9.1
-Flask-SocketIO==5.1.1
-Flask-SQLAlchemy==2.5.1
-SQLAlchemy==1.4.47
\ No newline at end of file
diff --git a/src/modal_functions/test_modal_functions.py b/src/modal_functions/test_modal_functions.py
deleted file mode 100644
index aeb029e..0000000
--- a/src/modal_functions/test_modal_functions.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import base64
-import os
-
-import modal
-
-
-def main():
-    UnavServer = modal.Cls.lookup("unav-server", "UnavServer")
-    unav_server = UnavServer()
-    current_directory = os.getcwd()
-    full_image_path = os.path.join(
-        current_directory, "modal_functions/misc/sample3.png"
-    )
-    destination_id = "07993"
-    with open(full_image_path, "rb") as image_file:
-        image_data = image_file.read()
-        base64_encoded = base64.b64encode(image_data).decode("utf-8")
-    
-    print(
-        unav_server.planner.remote(
-            destination_id=destination_id,
-            base_64_image=base64_encoded,
-            session_id="test_session_id_2",
-            building="LightHouse",
-            floor="6_floor",
-            place="New_York_City",
-        )
-    )
-
-
-if __name__ == "__main__":
-    try:
-        main()
-    except Exception as e:
-        print(f"An error occurred: {e}")
diff --git a/src/modal_functions/unav.py b/src/modal_functions/unav.py
deleted file mode 100644
index 9bdfb80..0000000
--- a/src/modal_functions/unav.py
+++ /dev/null
@@ -1,122 +0,0 @@
-from modal import method, gpu, build, enter
-
-from modal_config import app, unav_image, volume
-from logger_utils import setup_logger
-
-
-@app.cls(
-    image=unav_image,
-    volumes={"/root/UNav-IO": volume},
-    gpu=gpu.Any(),
-    enable_memory_snapshot=True,
-    concurrency_limit=20,
-    allow_concurrent_inputs=20,
-)
-class UnavServer:
-
-    @build()
-    @enter()
-    def load_server(self):
-        from server_manager import Server
-        from modules.config.settings import load_config
-
-        config = load_config("config.yaml")
-
-        self.server = Server(logger=setup_logger(), config=config)
-
-    @method()
-    def get_destinations_list(self):
-
-        response = self.server.get_destinations_list(
-            building="LightHouse", floor="6_floor"
-        )
-        return response
-
-    @method()
-    def planner(
-        self,
-        session_id: str = "",
-        destination_id: str = "",
-        building: str = "",
-        floor: str = "",
-        place: str = "",
-        base_64_image: str = None,
-    ):
-
-        import json
-        import time
-        import base64
-        import io
-        from PIL import Image
-
-        """
-            Handle localization request by processing the provided image and returning the pose.
-        """
-
-        start_time = time.time()  # Start time for the entire function
-
-        query_image_data = (
-            base64.b64decode(base_64_image.split(",")[1])
-            if "," in base_64_image
-            else base64.b64decode(base_64_image)
-        )
-        query_image = Image.open(io.BytesIO(query_image_data)).convert("RGB")
-
-        print("Query Image Converted from base64 to PIL Image")
-
-        response = self.server.select_destination(
-            session_id=session_id,
-            place=place,
-            building=building,
-            floor=floor,
-            destination_id=destination_id,
-        )
-        if response == None:
-            print("Desintation Set to id: " + destination_id)
-        else:
-            print(response)
-
-        # Measure time for handle_localization
-        start_localization_time = time.time()
-        pose = self.server.handle_localization(frame=query_image, session_id=session_id)
-        end_localization_time = time.time()
-        localization_time = end_localization_time - start_localization_time
-        print(f"Localization Time: {localization_time:.2f} seconds")
-
-        print("Pose: ", pose)
-
-        # Measure time for handle_navigation
-        start_navigation_time = time.time()
-        trajectory = self.server.handle_navigation(session_id)
-        end_navigation_time = time.time()
-        navigation_time = end_navigation_time - start_navigation_time
-        print(f"Navigation Time: {navigation_time:.2f} seconds")
-
-        end_time = time.time()  # End time for the entire function
-        elapsed_time = (
-            end_time - start_time
-        )  # Calculate elapsed time for the entire function
-
-        print(
-            f"Total Execution Time: {elapsed_time:.2f} seconds"
-        )  # Print total elapsed time
-
-        scale = self.server.config["location"]["scale"]
-
-        return json.dumps({"trajectory": trajectory, "scale": scale})
-
-    @method()
-    def start_server(self):
-        import json
-
-        """
-        Initializes and starts the serverless instance.
-    
-        This function helps in reducing the server response time for actual requests by pre-warming the server. 
-        By starting the server in advance, it ensures that the server is ready to handle incoming requests immediately, 
-        thus avoiding the latency associated with a cold start.
-        """
-        print("Server Started...")
-
-        response = {"status": "success", "message": "Server started."}
-        return json.dumps(response)
diff --git a/src/modal_functions/volume/modalvolumedata_setup.py b/src/modal_functions/volume/modalvolumedata_setup.py
deleted file mode 100644
index a978dda..0000000
--- a/src/modal_functions/volume/modalvolumedata_setup.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import modal
-import gdown
-import os
-import shutil 
-import logging
-import yaml
-import zipfile
-from dotenv import load_dotenv
-import boto3
-
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-
-# Reference the requirements-modal.txt for installing dependencies
-image = modal.Image.debian_slim().pip_install_from_requirements("modal_functions/volumesetup_requirements.txt")
-
-volume = modal.Volume.from_name("Visiondata", create_if_missing=True)
-
-app = modal.App("DataSetup", image=image, mounts=[modal.Mount.from_local_file(".env")])
-logging.info('created s3_client')
-load_dotenv()
-s3_client = boto3.client(
-    's3',
-    aws_access_key_id=os.getenv('AWS_ACCESS_KEY_ID'),
-    aws_secret_access_key=os.getenv('AWS_SECRET_ACCESS_KEY')
-)
-
-bucket_name = 'vis4ion'
-
-files = {
-    "demo_query.png": "17MzPE9TyKiNsi6G59rqLCMMd40cIK3bU",
-    "destination.json": "1sIzFujoumSsVlZqlwwO20l96ZziORP-w",
-    "hloc.yaml": "15JYLqU9Y56keMrg9ZfxwfbkbL6_haYpx",
-    "MapConnection_Graph.pkl": "199xZSc9jSajiCqzDW_AzhuqOp_YS41fZ",
-}
-
-@app.function(volumes={"/files": volume})   ## to create necesasry directories 
-def create_directories():  
-    os.makedirs(os.path.join("/files", "data", "New_York_City", "LightHouse"), exist_ok=True)
-    logging.info('created path : /files/data/New_York_City/LightHouse')
-    os.makedirs(os.path.join("/files", "configs"), exist_ok=True)
-    logging.info('created path : /files/Configs')
-
-@app.function(volumes={"/files": volume}, timeout=86400)
-def checkAndDownload_file_from_remoteStorage():   ## download the data to the respective locations
-    for filename, file_id in files.items():
-        logging.info(f"Processing {filename}")
-        if filename == "destination.json":
-            download_path = os.path.join('/files', 'data', 'destination.json')
-        elif filename == "hloc.yaml":
-            download_path = os.path.join("/files", "configs", "hloc.yaml")
-        elif filename == "MapConnection_Graph.pkl":
-            download_path = os.path.join("/files", "data", "New_York_City", "MapConnection_Graph.pkl")
-        else:
-            download_path = os.path.join("/files", filename)
-
-        if not os.path.exists(download_path):
-            gdown.download(f'https://drive.google.com/uc?id={file_id}', download_path, quiet=False)
-            logging.info(f"Downloaded {download_path}")
-        else:
-            logging.info(f"{download_path} already exists. Skipping download.")
-        
-    with open(os.path.join("/files", "configs", "hloc.yaml"), 'r') as file:
-        config = yaml.safe_load(file)
-    config['IO_root'] = "/root/UNav-IO"
-
-    with open(os.path.join("/files", "configs", "hloc.yaml"), 'w') as file:
-        yaml.safe_dump(config, file)
-
-    #downloading from s3 to modal volumes 
-    modal_directory = "/files/data"
-     # List objects in the S3 bucket
-    logging.info(f"Listing objects in the S3 bucket: {bucket_name}")
-    response = s3_client.list_objects_v2(Bucket=bucket_name)
-
-    if 'Contents' not in response:
-        logging.info(f"No objects found in S3 bucket {bucket_name}.")
-        return
-    
-    # Download each object from S3
-    for obj in response['Contents']:
-        s3_key = obj['Key']
-        file_path = os.path.join(modal_directory, s3_key)
-
-        # Check if the file already exists in the modal volume
-        if not os.path.exists(file_path):
-            # Create directories for nested keys if necessary
-            os.makedirs(os.path.dirname(file_path), exist_ok=True)
-
-            # Log the downloading process
-            logging.info(f"Downloading {s3_key} to {file_path}")
-
-            # Download the file from S3 to the modal volume
-            s3_client.download_file(bucket_name, s3_key, file_path)
-        else:
-            logging.info(f"{file_path} already exists, skipping download.")
-    
-    logging.info(f"All files from S3 bucket {bucket_name} have been downloaded to {modal_directory}")
-
-
-    logging.info("All files downloaded successfully from google drive and s3 bucket.")
-
-
-
-if __name__ == "__main__":
-    with app.run():
-        create_directories.remote()
-        checkAndDownload_file_from_remoteStorage.remote()
diff --git a/src/modal_functions/volume/volumedata_setup.py b/src/modal_functions/volume/volumedata_setup.py
deleted file mode 100644
index 3e2a540..0000000
--- a/src/modal_functions/volume/volumedata_setup.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import os
-import shutil 
-import logging
-import yaml
-import zipfile
-from dotenv import load_dotenv
-import boto3
-import gdown
-
-# Logging configuration
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-class DataManager:
-    def __init__(self, s3_bucket_name, env_file=".env"):
-        # Load environment variables
-        load_dotenv(env_file)
-
-        # AWS credentials setup
-        self.s3_client = boto3.client(
-            's3',
-            aws_access_key_id=os.getenv('AWS_ACCESS_KEY_ID'),
-            aws_secret_access_key=os.getenv('AWS_SECRET_ACCESS_KEY')
-        )
-
-        self.bucket_name = s3_bucket_name
-        self.remotefilesIds = {
-            "demo_query.png": "17MzPE9TyKiNsi6G59rqLCMMd40cIK3bU",
-            "destination.json": "1sIzFujoumSsVlZqlwwO20l96ZziORP-w",
-            "hloc.yaml": "15JYLqU9Y56keMrg9ZfxwfbkbL6_haYpx",
-            "MapConnection_Graph.pkl": "199xZSc9jSajiCqzDW_AzhuqOp_YS41fZ",
-        }
-
-    def create_directories(self, base_path="/files"):
-        """Create necessary directories."""
-        os.makedirs(os.path.join(base_path, "data", "New_York_City", "LightHouse"), exist_ok=True)
-        logging.info('Created path: /files/data/New_York_City/LightHouse')
-
-        os.makedirs(os.path.join(base_path, "configs"), exist_ok=True)
-        logging.info('Created path: /files/configs')
-
-    def download_files_from_google_drive(self, base_path="/files"):
-        """Download files from Google Drive."""
-        for filename, file_id in self.remotefilesIds.items():
-            logging.info(f"Processing {filename}")
-            download_path = self.get_download_path(filename, base_path)
-
-            if not os.path.exists(download_path):
-                gdown.download(f'https://drive.google.com/uc?id={file_id}', download_path, quiet=False)
-                logging.info(f"Downloaded {download_path}")
-            else:
-                logging.info(f"{download_path} already exists. Skipping download.")
-
-    def get_download_path(self, filename, base_path):
-        """Generate the download path based on the file type."""
-        if filename == "destination.json":
-            return os.path.join(base_path, 'data', 'destination.json')
-        elif filename == "hloc.yaml":
-            return os.path.join(base_path, "configs", "hloc.yaml")
-        elif filename == "MapConnection_Graph.pkl":
-            return os.path.join(base_path, "data", "New_York_City", "MapConnection_Graph.pkl")
-        else:
-            return os.path.join(base_path, filename)
-
-    def modify_hloc_yaml(self, base_path="/files"):
-        """Modify hloc.yaml configuration."""
-        with open(os.path.join(base_path, "configs", "hloc.yaml"), 'r') as file:
-            config = yaml.safe_load(file)
-        config['IO_root'] = "/root/UNav-IO"
-
-        with open(os.path.join(base_path, "configs", "hloc.yaml"), 'w') as file:
-            yaml.safe_dump(config, file)
-
-    def download_files_from_s3(self, modal_directory="/files/data"):
-        """Download all files from the specified S3 bucket."""
-        logging.info(f"Listing objects in the S3 bucket: {self.bucket_name}")
-        response = self.s3_client.list_objects_v2(Bucket=self.bucket_name)
-
-        if 'Contents' not in response:
-            logging.info(f"No objects found in S3 bucket {self.bucket_name}.")
-            return
-        
-        # Download each object from S3
-        for obj in response['Contents']:
-            s3_key = obj['Key']
-            file_path = os.path.join(modal_directory, s3_key)
-
-            # Create directories for nested keys if necessary
-            os.makedirs(os.path.dirname(file_path), exist_ok=True)
-
-            logging.info(f"Downloading {s3_key} to {file_path}")
-            
-            # Download the file
-            self.s3_client.download_file(self.bucket_name, s3_key, file_path)
-
-        logging.info(f"All files from S3 bucket {self.bucket_name} have been downloaded to {modal_directory}")
-
-    def run(self, base_path="/files"):
-        """Main function to create directories, download files from Google Drive and S3."""
-        self.create_directories(base_path)
-        self.download_files_from_google_drive(base_path)
-        self.modify_hloc_yaml(base_path)
-        self.download_files_from_s3(os.path.join(base_path, "data"))
-        logging.info("All files downloaded successfully from Google Drive and S3 bucket.")
diff --git a/src/modal_functions/volume/volumesetup_requirements.txt b/src/modal_functions/volume/volumesetup_requirements.txt
deleted file mode 100644
index 298ced8..0000000
--- a/src/modal_functions/volume/volumesetup_requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-gdown
-PyYAML
-boto3
-python-dotenv
diff --git a/src/modules/routes/frame_routes.py b/src/modules/routes/frame_routes.py
index 06b16d9..351576f 100644
--- a/src/modules/routes/frame_routes.py
+++ b/src/modules/routes/frame_routes.py
@@ -46,20 +46,9 @@ def register_frame_routes(app, server, socketio):
             # Resize the image
             resized_image = frame.resize((new_width, new_height))
 
-            image_np = np.array(resized_image)
-
-            original_width, original_height = frame.size
-
-            new_width = 640
-            new_height = int((new_width / original_width) * original_height)
-
-            # Resize the image
-            resized_image = frame.resize((new_width, new_height))
-
             image_np = np.array(resized_image)
             
             if frame is not None:
-                client_frames[session_id] = image_np
                 client_frames[session_id] = image_np
                 response_data = {'status': 'frame received'}
 
@@ -76,7 +65,6 @@ def register_frame_routes(app, server, socketio):
 
                 buffered = io.BytesIO()
                 resized_image.save(buffered, format="JPEG")
-                resized_image.save(buffered, format="JPEG")
                 new_frame_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
                 response_data['floorplan_base64'] = pose_update_info.get('floorplan_base64')
                 socketio.emit('camera_frame', {'session_id': session_id, 'frame': new_frame_base64})
@@ -107,8 +95,6 @@ def register_frame_routes(app, server, socketio):
         else:
             return jsonify({'error': 'No frame available for this client'}), 404
 
-    @app.route('/get_image/<id>/<imageName>', methods=['POST'])
-    def get_image(id, imageName):
     @app.route('/get_image/<id>/<imageName>', methods=['POST'])
     def get_image(id, imageName):
         """
@@ -117,10 +103,6 @@ def register_frame_routes(app, server, socketio):
         data = request.json
         session_id = data.get('username')
     
-        image_path = os.path.join(server.root, 'logs', server.config['location']['place'], server.config['location']['building'], server.config['location']['floor'], id, 'images', imageName)
-        data = request.json
-        session_id = data.get('username')
-    
         image_path = os.path.join(server.root, 'logs', server.config['location']['place'], server.config['location']['building'], server.config['location']['floor'], id, 'images', imageName)
         if os.path.exists(image_path):
             return send_file(image_path, mimetype='image/png')
diff --git a/src/server_manager.py b/src/server_manager.py
index bf5d144..901530b 100644
--- a/src/server_manager.py
+++ b/src/server_manager.py
@@ -31,8 +31,6 @@ class Server(DataHandler):
 
         self.load_all_maps = config['hloc']['load_all_maps']
             
-        self.load_all_maps = config['hloc']['load_all_maps']
-            
         self.coarse_locator = Coarse_Locator(config=self.config)
         self.refine_locator = localization(self.coarse_locator, config=self.config, logger=self.logger)
         
@@ -42,7 +40,6 @@ class Server(DataHandler):
         self.localization_states = {}
         self.destination_states = {}
             
-            
         with open(os.path.join(self.root, 'data', 'scale.json'), 'r') as f:
             self.scale_data = json.load(f)
 
@@ -61,21 +58,9 @@ class Server(DataHandler):
 
         # image_rgb = resized_image.convert("RGB")
         
-        # self.image_np = np.array(image_rgb)
-        # original_width, original_height = image.size
-
-        # new_width = 640
-        # new_height = int((new_width / original_width) * original_height)
-
-        # # Resize the image
-        # resized_image = image.resize((new_width, new_height))
-
-        # image_rgb = resized_image.convert("RGB")
-        
         # self.image_np = np.array(image_rgb)
         ############################################# test data #################################################
         
-        
     def update_config(self, new_config):
         # Merge the new configuration with the existing one
         
@@ -131,7 +116,6 @@ class Server(DataHandler):
             'floorplan': floorplan_base64,
         }
 
-
     def get_destinations_list(self, building, floor):
         # Load destination data
         destinations = self.all_buildings_data.get(building,{}).get(floor,{}).get('destinations',{})
@@ -165,7 +149,6 @@ class Server(DataHandler):
         images = {id: os.listdir(os.path.join(base_path, id, 'images')) for id in ids if os.path.isdir(os.path.join(base_path, id, 'images'))}
         return images
 
-
     def _split_id(self, segment_id):
         # Load the current segment and its neighbors
         parts = segment_id.split('_')
@@ -173,11 +156,9 @@ class Server(DataHandler):
         floor = parts[1] + '_' + parts[2]  # Extract floor name (e.g., '6_floor')
         return building, floor
 
-
     def _update_next_step(self):
         pass
 
-
     def handle_localization(self, session_id, frame):
         """
         Handles the localization process for a given session and frame.
@@ -332,7 +313,6 @@ class Server(DataHandler):
         
         return pose_update_info
 
-
     def handle_navigation(self, session_id):
         if session_id not in self.destination_states:
             self.logger.error("Selected destination ID is not set.")