From a83f6d772cb652eb83b6960d6d1cb180f1479789 Mon Sep 17 00:00:00 2001
From: "pol.plana" <123639572+pol-33@users.noreply.github.com>
Date: Tue, 22 Oct 2024 13:47:29 +0200
Subject: [PATCH 1/5] Plugin Allotjat Localment

---
 backend/requirements.txt                      |   2 +-
 plugin/GAISSALabel_plugin.zip                 | Bin 0 -> 3436 bytes
 plugin/GAISSALabel_plugin/calculator.py       |  42 ++++++++++++++++++
 plugin/GAISSALabel_plugin/gaissaplugin.py     |  12 +++++
 plugin/GAISSALabel_plugin/main_script.py      |  17 +++++++
 plugin/GAISSALabel_plugin/plugin_interface.py |   5 +++
 plugin/GAISSALabel_plugin/requirements.txt    |   1 +
 7 files changed, 78 insertions(+), 1 deletion(-)
 create mode 100644 plugin/GAISSALabel_plugin.zip
 create mode 100644 plugin/GAISSALabel_plugin/calculator.py
 create mode 100644 plugin/GAISSALabel_plugin/gaissaplugin.py
 create mode 100644 plugin/GAISSALabel_plugin/main_script.py
 create mode 100644 plugin/GAISSALabel_plugin/plugin_interface.py
 create mode 100644 plugin/GAISSALabel_plugin/requirements.txt

diff --git a/backend/requirements.txt b/backend/requirements.txt
index 5d59e33..33dec46 100644
--- a/backend/requirements.txt
+++ b/backend/requirements.txt
@@ -86,7 +86,7 @@ six==1.16.0
 sqlparse==0.4.4
 tensorboard==2.13.0
 tensorboard-data-server==0.7.2
-tensorflow==2.13.1
+tensorflow==2.13.0
 tensorflow-estimator==2.13.0
 tensorflow-io-gcs-filesystem==0.34.0
 termcolor==2.3.0
diff --git a/plugin/GAISSALabel_plugin.zip b/plugin/GAISSALabel_plugin.zip
new file mode 100644
index 0000000000000000000000000000000000000000..6fe3ff209645c0ed29a1cfc9c5798b92f02959c4
GIT binary patch
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diff --git a/plugin/GAISSALabel_plugin/calculator.py b/plugin/GAISSALabel_plugin/calculator.py
new file mode 100644
index 0000000..f70d51c
--- /dev/null
+++ b/plugin/GAISSALabel_plugin/calculator.py
@@ -0,0 +1,42 @@
+import os, csv
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2_as_graph
+
+def calculateH5(model_path, output_path):
+    model = load_model(model_path)
+
+    #### MODEL SIZE (# parameters)
+    # Get number of parameters
+    num_params = model.count_params()
+
+    #### MODEL FILE SIZE
+    model_size_bytes = os.path.getsize(model_path)
+    model_size_mb = model_size_bytes / (1024 * 1024)
+
+    #### FLOPS
+    # convert tf.keras model into frozen graph to count FLOPS about operations used at inference
+    # FLOPS depends on batch size
+    inputs = [
+        tf.TensorSpec([1] + inp.shape[1:], inp.dtype) for inp in model.inputs
+    ]
+    real_model = tf.function(model).get_concrete_function(inputs)
+    frozen_func, _ = convert_variables_to_constants_v2_as_graph(real_model)
+
+    # Calculate FLOPS with tf.profiler
+    run_meta = tf.compat.v1.RunMetadata()
+    opts = tf.compat.v1.profiler.ProfileOptionBuilder.float_operation()
+    flops = tf.compat.v1.profiler.profile(
+        graph=frozen_func.graph, run_meta=run_meta, cmd="scope", options=opts
+    )
+    
+    
+    # Writing to CSV
+    with open(output_path, 'w', newline='') as file:
+        writer = csv.writer(file)
+
+        # Write the header
+        writer.writerow(['model_size', 'file_size', 'flops'])
+
+        # Write the values
+        writer.writerow([num_params, model_size_mb, flops.total_float_ops])
diff --git a/plugin/GAISSALabel_plugin/gaissaplugin.py b/plugin/GAISSALabel_plugin/gaissaplugin.py
new file mode 100644
index 0000000..f3c0cc8
--- /dev/null
+++ b/plugin/GAISSALabel_plugin/gaissaplugin.py
@@ -0,0 +1,12 @@
+import os
+from plugin_interface import PluginInterface
+from calculator import calculateH5
+
+class PlugIn(PluginInterface):
+    def generate_output(self, model_path, output_directory, filename):        
+        # Create the full path for the output file
+        output_path = os.path.join(output_directory, filename + '.csv')
+        
+        calculateH5(model_path, output_path)
+
+        print(f"Output file '{filename}' generated in '{output_directory}'")
diff --git a/plugin/GAISSALabel_plugin/main_script.py b/plugin/GAISSALabel_plugin/main_script.py
new file mode 100644
index 0000000..eee17c1
--- /dev/null
+++ b/plugin/GAISSALabel_plugin/main_script.py
@@ -0,0 +1,17 @@
+from gaissaplugin import PlugIn
+
+def main():
+
+    # Get user input for model path and output directory and filename
+    model_path = input("Enter the model's file path: ")
+    output_directory = input("Enter the output directory: ")
+    filename = input("Enter the filename: ")
+
+    # Instantiate the plugin
+    plugin = PlugIn()
+
+    # Call the plugin to generate the output
+    plugin.generate_output(model_path, output_directory, filename)
+
+if __name__ == "__main__":
+    main()
diff --git a/plugin/GAISSALabel_plugin/plugin_interface.py b/plugin/GAISSALabel_plugin/plugin_interface.py
new file mode 100644
index 0000000..34a3b9d
--- /dev/null
+++ b/plugin/GAISSALabel_plugin/plugin_interface.py
@@ -0,0 +1,5 @@
+# plugin_interface.py
+
+class PluginInterface:
+    def generate_output(self, output_directory, filename):
+        raise NotImplementedError("Subclasses must implement this method")
diff --git a/plugin/GAISSALabel_plugin/requirements.txt b/plugin/GAISSALabel_plugin/requirements.txt
new file mode 100644
index 0000000..66921a1
--- /dev/null
+++ b/plugin/GAISSALabel_plugin/requirements.txt
@@ -0,0 +1 @@
+tensorflow>=2.0,<3.0

From 313a759144898b5b48e6aa1583f961ef83aaa602 Mon Sep 17 00:00:00 2001
From: "pol.plana" <123639572+pol-33@users.noreply.github.com>
Date: Tue, 22 Oct 2024 19:24:52 +0200
Subject: [PATCH 2/5] Plugin servit localment

---
 frontend/public/GAISSALabel_plugin.zip        | Bin 0 -> 3436 bytes
 frontend/src/components/FileNewExperiment.vue |   2 +-
 plugin/README.md                              |  28 ++++++++++++++++++
 3 files changed, 29 insertions(+), 1 deletion(-)
 create mode 100644 frontend/public/GAISSALabel_plugin.zip
 create mode 100644 plugin/README.md

diff --git a/frontend/public/GAISSALabel_plugin.zip b/frontend/public/GAISSALabel_plugin.zip
new file mode 100644
index 0000000000000000000000000000000000000000..6fe3ff209645c0ed29a1cfc9c5798b92f02959c4
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diff --git a/frontend/src/components/FileNewExperiment.vue b/frontend/src/components/FileNewExperiment.vue
index a1ff806..4b6b6ed 100644
--- a/frontend/src/components/FileNewExperiment.vue
+++ b/frontend/src/components/FileNewExperiment.vue
@@ -63,7 +63,7 @@
     <p style="font-weight: bold">{{ $t('GAISSALabel plug-in') }}</p>
     <p>{{ $t('We offer you a plug-in that runs on the terminal of your server. It will help you generate a file with the \
         configuration parameters of your model. Specifically, it will provide the size of the model, the size of its file and the FLOPS.') }}</p>
-    <p>{{ $t('You can download it through this') }} <a href="https://drive.google.com/file/d/1oeYVYamzdtR6t4SV8U8Rd-9g-jm-u_ZU/view?usp=sharing" target="_blank">{{ $t('link') }}</a>.</p>
+    <p>{{ $t('You can download it through this') }} <a href="/GAISSALabel_plugin.zip" target="_blank">{{ $t('link') }}</a>.</p>
     <p>{{ $t('Then, follow these steps:') }}</p>
     <ol style="margin-left: 30px">
         <li><p>{{ $t('Decompress the downloaded file') }}</p></li>
diff --git a/plugin/README.md b/plugin/README.md
new file mode 100644
index 0000000..a4aec88
--- /dev/null
+++ b/plugin/README.md
@@ -0,0 +1,28 @@
+# GAISSALabel Plug-in
+
+We offer you a plug-in that runs on the terminal of your server. It will help you generate a file with the configuration parameters of your model. Specifically, it will provide the size of the model, the size of its file, and the FLOPS.
+
+
+## Installation and Usage
+
+1. Decompress the downloaded file.
+2. Access your console and navigate to the decompressed folder.
+3. Create a Python virtual environment using the command:
+    ```sh
+    python -m venv env
+    ```
+4. Activate the virtual environment:
+    ```sh
+    source env/bin/activate
+    ```
+5. Install the requirements:
+    ```sh
+    pip install -r requirements.txt
+    ```
+6. Run the script and follow the instructions of the plug-in:
+    ```sh
+    python main_script.py
+    ```
+
+## Updating the GAISSALabel Plug-in
+If the GAISSALabel Plug-in is modified, ensure that the new version of the ZIP file is copied to the public folder of the frontend Vue project with the name "GAISSALabel_plugin.zip", so users can access the updated plug-in through the download link.

From 5b3606b328fd308d8fd44a85c72fa510819b42ac Mon Sep 17 00:00:00 2001
From: "pol.plana" <123639572+pol-33@users.noreply.github.com>
Date: Sat, 26 Oct 2024 12:00:31 +0200
Subject: [PATCH 3/5] Plugin PyTorch compatibility

---
 backend/requirements.txt                   |   2 +-
 plugin/GAISSALabel_plugin.zip              | Bin 3436 -> 5994 bytes
 plugin/GAISSALabel_plugin/calculator.py    |  39 ++++++++++++++++++++-
 plugin/GAISSALabel_plugin/gaissaplugin.py  |  20 ++++++++---
 plugin/GAISSALabel_plugin/requirements.txt |   2 ++
 5 files changed, 57 insertions(+), 6 deletions(-)

diff --git a/backend/requirements.txt b/backend/requirements.txt
index 33dec46..a0db1ed 100644
--- a/backend/requirements.txt
+++ b/backend/requirements.txt
@@ -2,7 +2,7 @@ absl-py==2.0.0
 arrow==1.3.0
 asgiref==3.7.2
 astunparse==1.6.3
-backports.zoneinfo==0.2.1
+backports.zoneinfo==0.2.1;python_version<"3.9"
 cachetools==5.3.2
 certifi==2023.5.7
 charset-normalizer==3.2.0
diff --git a/plugin/GAISSALabel_plugin.zip b/plugin/GAISSALabel_plugin.zip
index 6fe3ff209645c0ed29a1cfc9c5798b92f02959c4..e67a72fc36d845c3ceed033f6f4cf4bbad307a2c 100644
GIT binary patch
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diff --git a/plugin/GAISSALabel_plugin/calculator.py b/plugin/GAISSALabel_plugin/calculator.py
index f70d51c..61ef0b7 100644
--- a/plugin/GAISSALabel_plugin/calculator.py
+++ b/plugin/GAISSALabel_plugin/calculator.py
@@ -2,6 +2,8 @@
 import tensorflow as tf
 from tensorflow.keras.models import load_model
 from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2_as_graph
+import torch
+from fvcore.nn import FlopCountAnalysis
 
 def calculateH5(model_path, output_path):
     model = load_model(model_path)
@@ -18,7 +20,8 @@ def calculateH5(model_path, output_path):
     # convert tf.keras model into frozen graph to count FLOPS about operations used at inference
     # FLOPS depends on batch size
     inputs = [
-        tf.TensorSpec([1] + inp.shape[1:], inp.dtype) for inp in model.inputs
+        tf.TensorSpec([1] + (list(inp.shape[1:]) if not isinstance(inp.shape[1:], list) else inp.shape[1:]), inp.dtype)
+        for inp in model.inputs
     ]
     real_model = tf.function(model).get_concrete_function(inputs)
     frozen_func, _ = convert_variables_to_constants_v2_as_graph(real_model)
@@ -40,3 +43,37 @@ def calculateH5(model_path, output_path):
 
         # Write the values
         writer.writerow([num_params, model_size_mb, flops.total_float_ops])
+
+def calculatePyTorch(model_path, output_path, input_shape=None):
+    model = torch.load(model_path)
+    model.eval()
+
+    #### MODEL SIZE (# parameters)
+    num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+    #### MODEL FILE SIZE
+    model_size_bytes = os.path.getsize(model_path)
+    model_size_mb = model_size_bytes / (1024 * 1024)
+
+    #### FLOPS for PyTorch model
+    # Check if the user provided an input shape, otherwise default to (1, 3, 224, 224) for image models
+    if input_shape is None:
+        print("No input shape provided. Defaulting to image model input shape: (1, 3, 224, 224)")
+        input_shape = (1, 3, 224, 224)
+
+    try:
+        # Create a dummy input tensor with the specified shape
+        if len(input_shape) == 2:  # For language models, we assume input shape like (batch_size, sequence_length)
+            input_tensor = torch.randint(0, 30522, input_shape, dtype=torch.long)
+        else:
+            input_tensor = torch.randn(input_shape)
+        flops = FlopCountAnalysis(model, input_tensor).total()
+    except Exception as e:
+        print(f"Error calculating FLOPS: {e}")
+        flops = "N/A"
+
+    # Writing to CSV
+    with open(output_path, 'w', newline='') as file:
+        writer = csv.writer(file)
+        writer.writerow(['model_size', 'file_size', 'flops'])
+        writer.writerow([num_params, model_size_mb, flops])
\ No newline at end of file
diff --git a/plugin/GAISSALabel_plugin/gaissaplugin.py b/plugin/GAISSALabel_plugin/gaissaplugin.py
index f3c0cc8..cd483a9 100644
--- a/plugin/GAISSALabel_plugin/gaissaplugin.py
+++ b/plugin/GAISSALabel_plugin/gaissaplugin.py
@@ -1,12 +1,24 @@
 import os
 from plugin_interface import PluginInterface
-from calculator import calculateH5
+from calculator import calculateH5, calculatePyTorch
 
 class PlugIn(PluginInterface):
-    def generate_output(self, model_path, output_directory, filename):        
+    def generate_output(self, model_path, output_directory, filename):
         # Create the full path for the output file
         output_path = os.path.join(output_directory, filename + '.csv')
-        
-        calculateH5(model_path, output_path)
+
+        # Detect model type based on file extension or directory
+        if model_path.endswith('.h5'):
+            calculateH5(model_path, output_path)
+        elif model_path.endswith('.pt') or model_path.endswith('.pth'):
+            # Ask the user if it's not an image model and they need to provide the input shape
+            input_shape_str = input("Enter the input shape (e.g.: 1,3,224,224), or press Enter to default: ")
+            if input_shape_str:
+                input_shape = tuple(map(int, input_shape_str.split(',')))
+            else:
+                input_shape = None
+            calculatePyTorch(model_path, output_path, input_shape)
+        else:
+            raise ValueError("Unsupported model format. Please provide a .h5, .pt, .pth file or a SavedModel directory.")
 
         print(f"Output file '{filename}' generated in '{output_directory}'")
diff --git a/plugin/GAISSALabel_plugin/requirements.txt b/plugin/GAISSALabel_plugin/requirements.txt
index 66921a1..9bb7b4b 100644
--- a/plugin/GAISSALabel_plugin/requirements.txt
+++ b/plugin/GAISSALabel_plugin/requirements.txt
@@ -1 +1,3 @@
 tensorflow>=2.0,<3.0
+torch>=2.0.0
+fvcore>=0.1.5
\ No newline at end of file

From 35990debf680622f79d5076fb69873147f64eed4 Mon Sep 17 00:00:00 2001
From: "pol.plana" <123639572+pol-33@users.noreply.github.com>
Date: Mon, 28 Oct 2024 10:58:57 +0100
Subject: [PATCH 4/5] Plugin tf SavedModel compatibility

---
 frontend/public/GAISSALabel_plugin.zip    | Bin 3436 -> 6501 bytes
 plugin/GAISSALabel_plugin.zip             | Bin 5994 -> 6501 bytes
 plugin/GAISSALabel_plugin/calculator.py   |  52 ++++++++++++++++++++++
 plugin/GAISSALabel_plugin/gaissaplugin.py |  10 ++++-
 4 files changed, 61 insertions(+), 1 deletion(-)

diff --git a/frontend/public/GAISSALabel_plugin.zip b/frontend/public/GAISSALabel_plugin.zip
index 6fe3ff209645c0ed29a1cfc9c5798b92f02959c4..95e30321b2a18bc8c94c958b081f7d9c4cbfd0ef 100644
GIT binary patch
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diff --git a/plugin/GAISSALabel_plugin.zip b/plugin/GAISSALabel_plugin.zip
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diff --git a/plugin/GAISSALabel_plugin/calculator.py b/plugin/GAISSALabel_plugin/calculator.py
index 61ef0b7..4601eb9 100644
--- a/plugin/GAISSALabel_plugin/calculator.py
+++ b/plugin/GAISSALabel_plugin/calculator.py
@@ -44,6 +44,58 @@ def calculateH5(model_path, output_path):
         # Write the values
         writer.writerow([num_params, model_size_mb, flops.total_float_ops])
 
+def calculateSavedModel(model_path, output_path, input_shape=None):
+    # Load the model for inference compatibility
+    model = tf.keras.layers.TFSMLayer(model_path, call_endpoint='serving_default')
+
+    #### MODEL SIZE (# parameters)
+    num_params = int(sum(tf.reduce_prod(var.shape) for var in model.trainable_variables))
+
+    #### MODEL FILE SIZE
+    model_size_bytes = sum(os.path.getsize(os.path.join(dirpath, filename))
+                           for dirpath, _, filenames in os.walk(model_path)
+                           for filename in filenames)
+    model_size_mb = model_size_bytes / (1024 * 1024)
+
+    #### FLOPS Calculation
+    # Use a default input shape if none is provided
+    if input_shape is None:
+        print("No input shape provided. Defaulting to image model input shape: (1, 224, 224, 3)")
+        input_shape = (1, 224, 224, 3)
+
+    # Convert the model to a frozen graph for profiling
+    concrete_func = tf.function(lambda x: model(x)).get_concrete_function(
+        tf.TensorSpec(input_shape, model.dtype)
+    )
+    frozen_func, _ = convert_variables_to_constants_v2_as_graph(concrete_func)
+
+    # Now that we have the frozen function, we can proceed with profiling
+    with tf.compat.v1.Session(graph=frozen_func.graph) as sess:
+        # Run metadata collection for FLOPS and parameter profiling
+        run_meta = tf.compat.v1.RunMetadata()
+        opts = tf.compat.v1.profiler.ProfileOptionBuilder.float_operation()    
+        flops = tf.compat.v1.profiler.profile(
+            graph=sess.graph,
+            run_meta=run_meta,
+            cmd="op",
+            options=opts
+        )
+
+        # Ensure the profiler has been applied correctly
+        opts_params = tf.compat.v1.profiler.ProfileOptionBuilder.trainable_variables_parameter()    
+        params = tf.compat.v1.profiler.profile(
+            graph=sess.graph,
+            run_meta=run_meta,
+            cmd="op",
+            options=opts_params
+        )
+
+    #### Write Results to CSV
+    with open(output_path, 'w', newline='') as file:
+        writer = csv.writer(file)
+        writer.writerow(['model_size', 'file_size', 'flops'])
+        writer.writerow([num_params, model_size_mb, flops.total_float_ops if flops else 'N/A'])
+
 def calculatePyTorch(model_path, output_path, input_shape=None):
     model = torch.load(model_path)
     model.eval()
diff --git a/plugin/GAISSALabel_plugin/gaissaplugin.py b/plugin/GAISSALabel_plugin/gaissaplugin.py
index cd483a9..abde55d 100644
--- a/plugin/GAISSALabel_plugin/gaissaplugin.py
+++ b/plugin/GAISSALabel_plugin/gaissaplugin.py
@@ -1,6 +1,6 @@
 import os
 from plugin_interface import PluginInterface
-from calculator import calculateH5, calculatePyTorch
+from calculator import calculateH5, calculateSavedModel, calculatePyTorch
 
 class PlugIn(PluginInterface):
     def generate_output(self, model_path, output_directory, filename):
@@ -10,6 +10,14 @@ def generate_output(self, model_path, output_directory, filename):
         # Detect model type based on file extension or directory
         if model_path.endswith('.h5'):
             calculateH5(model_path, output_path)
+        elif os.path.isdir(model_path):  # SavedModel format is a directory
+            # Ask the user if it's not an image model and they need to provide the input shape
+            input_shape_str = input("Enter the input shape (e.g.: 1,3,224,224), or press Enter to default: ")
+            if input_shape_str:
+                input_shape = tuple(map(int, input_shape_str.split(',')))
+            else:
+                input_shape = None
+            calculateSavedModel(model_path, output_path, input_shape)
         elif model_path.endswith('.pt') or model_path.endswith('.pth'):
             # Ask the user if it's not an image model and they need to provide the input shape
             input_shape_str = input("Enter the input shape (e.g.: 1,3,224,224), or press Enter to default: ")

From 6eb7c5113fb2ac7c8532290a2f7d92ef43f25ca2 Mon Sep 17 00:00:00 2001
From: "pol.plana" <123639572+pol-33@users.noreply.github.com>
Date: Mon, 28 Oct 2024 14:25:57 +0100
Subject: [PATCH 5/5] Minor plugin improvements

Functions refatoring
Default values for output and output filename
Missing package added
Other minor issues
---
 frontend/public/GAISSALabel_plugin.zip     | Bin 6501 -> 6737 bytes
 plugin/GAISSALabel_plugin.zip              | Bin 6501 -> 6737 bytes
 plugin/GAISSALabel_plugin/calculator.py    |  35 ++++++++++-----------
 plugin/GAISSALabel_plugin/gaissaplugin.py  |  32 ++++++++++---------
 plugin/GAISSALabel_plugin/main_script.py   |  10 ++++--
 plugin/GAISSALabel_plugin/requirements.txt |   1 +
 6 files changed, 43 insertions(+), 35 deletions(-)

diff --git a/frontend/public/GAISSALabel_plugin.zip b/frontend/public/GAISSALabel_plugin.zip
index 95e30321b2a18bc8c94c958b081f7d9c4cbfd0ef..5979528fd3082ca00cd5a6837acb4d40dbbc47c0 100644
GIT binary patch
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T<9o&biNXmXaW*XTF9z^G-sS5t

delta 3193
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diff --git a/plugin/GAISSALabel_plugin.zip b/plugin/GAISSALabel_plugin.zip
index 95e30321b2a18bc8c94c958b081f7d9c4cbfd0ef..5979528fd3082ca00cd5a6837acb4d40dbbc47c0 100644
GIT binary patch
delta 3474
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T<9o&biNXmXaW*XTF9z^G-sS5t

delta 3193
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diff --git a/plugin/GAISSALabel_plugin/calculator.py b/plugin/GAISSALabel_plugin/calculator.py
index 4601eb9..1bfa6cd 100644
--- a/plugin/GAISSALabel_plugin/calculator.py
+++ b/plugin/GAISSALabel_plugin/calculator.py
@@ -5,6 +5,19 @@
 import torch
 from fvcore.nn import FlopCountAnalysis
 
+def write_to_csv(output_path, values):
+    # Ensure the directory exists
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+    with open(output_path, 'w', newline='') as file:
+        writer = csv.writer(file)
+
+        # Write the header
+        writer.writerow(['model_size', 'file_size', 'flops'])
+
+        # Write the values
+        writer.writerow(values)
+
 def calculateH5(model_path, output_path):
     model = load_model(model_path)
 
@@ -32,17 +45,9 @@ def calculateH5(model_path, output_path):
     flops = tf.compat.v1.profiler.profile(
         graph=frozen_func.graph, run_meta=run_meta, cmd="scope", options=opts
     )
-    
-    
-    # Writing to CSV
-    with open(output_path, 'w', newline='') as file:
-        writer = csv.writer(file)
 
-        # Write the header
-        writer.writerow(['model_size', 'file_size', 'flops'])
-
-        # Write the values
-        writer.writerow([num_params, model_size_mb, flops.total_float_ops])
+    # Writing to CSV
+    write_to_csv(output_path, [num_params, model_size_mb, flops.total_float_ops])
 
 def calculateSavedModel(model_path, output_path, input_shape=None):
     # Load the model for inference compatibility
@@ -91,10 +96,7 @@ def calculateSavedModel(model_path, output_path, input_shape=None):
         )
 
     #### Write Results to CSV
-    with open(output_path, 'w', newline='') as file:
-        writer = csv.writer(file)
-        writer.writerow(['model_size', 'file_size', 'flops'])
-        writer.writerow([num_params, model_size_mb, flops.total_float_ops if flops else 'N/A'])
+    write_to_csv(output_path, [num_params, model_size_mb, flops.total_float_ops])
 
 def calculatePyTorch(model_path, output_path, input_shape=None):
     model = torch.load(model_path)
@@ -125,7 +127,4 @@ def calculatePyTorch(model_path, output_path, input_shape=None):
         flops = "N/A"
 
     # Writing to CSV
-    with open(output_path, 'w', newline='') as file:
-        writer = csv.writer(file)
-        writer.writerow(['model_size', 'file_size', 'flops'])
-        writer.writerow([num_params, model_size_mb, flops])
\ No newline at end of file
+    write_to_csv(output_path, [num_params, model_size_mb, flops])
\ No newline at end of file
diff --git a/plugin/GAISSALabel_plugin/gaissaplugin.py b/plugin/GAISSALabel_plugin/gaissaplugin.py
index abde55d..b5387cb 100644
--- a/plugin/GAISSALabel_plugin/gaissaplugin.py
+++ b/plugin/GAISSALabel_plugin/gaissaplugin.py
@@ -5,28 +5,30 @@
 class PlugIn(PluginInterface):
     def generate_output(self, model_path, output_directory, filename):
         # Create the full path for the output file
-        output_path = os.path.join(output_directory, filename + '.csv')
+        output_path = os.path.join(output_directory, f"{filename}.csv")
 
         # Detect model type based on file extension or directory
         if model_path.endswith('.h5'):
             calculateH5(model_path, output_path)
+        
         elif os.path.isdir(model_path):  # SavedModel format is a directory
-            # Ask the user if it's not an image model and they need to provide the input shape
-            input_shape_str = input("Enter the input shape (e.g.: 1,3,224,224), or press Enter to default: ")
-            if input_shape_str:
-                input_shape = tuple(map(int, input_shape_str.split(',')))
-            else:
-                input_shape = None
+            input_shape = self._get_input_shape("Enter the input shape (e.g.: 1,224,224,3), or press Enter to default: ")
             calculateSavedModel(model_path, output_path, input_shape)
-        elif model_path.endswith('.pt') or model_path.endswith('.pth'):
-            # Ask the user if it's not an image model and they need to provide the input shape
-            input_shape_str = input("Enter the input shape (e.g.: 1,3,224,224), or press Enter to default: ")
-            if input_shape_str:
-                input_shape = tuple(map(int, input_shape_str.split(',')))
-            else:
-                input_shape = None
+        
+        elif model_path.endswith(('.pt', '.pth')):
+            input_shape = self._get_input_shape("Enter the input shape (e.g.: 1,3,224,224), or press Enter to default: ")
             calculatePyTorch(model_path, output_path, input_shape)
+        
         else:
             raise ValueError("Unsupported model format. Please provide a .h5, .pt, .pth file or a SavedModel directory.")
-
+        
         print(f"Output file '{filename}' generated in '{output_directory}'")
+
+    def _get_input_shape(self, prompt):
+        # Prompts the user to enter an input shape and converts it to a tuple or returns None if no input is provided.
+        # Converts 'None' strings to actual None values for flexibility.
+        
+        input_shape_str = input(prompt)
+        if not input_shape_str:
+            return None
+        return tuple(int(dim) if dim != "None" else None for dim in input_shape_str.split(','))
diff --git a/plugin/GAISSALabel_plugin/main_script.py b/plugin/GAISSALabel_plugin/main_script.py
index eee17c1..2f77ec7 100644
--- a/plugin/GAISSALabel_plugin/main_script.py
+++ b/plugin/GAISSALabel_plugin/main_script.py
@@ -1,11 +1,17 @@
 from gaissaplugin import PlugIn
+import os
 
 def main():
 
     # Get user input for model path and output directory and filename
     model_path = input("Enter the model's file path: ")
-    output_directory = input("Enter the output directory: ")
-    filename = input("Enter the filename: ")
+
+    default_output_directory = "./plugin_output"
+    output_directory = input(f"Enter the output directory (or continue to default: {default_output_directory}): ") or default_output_directory
+
+    model_name = os.path.splitext(os.path.basename(model_path))[0]
+    default_filename = f"{model_name}_output"
+    filename = input(f"Enter the filename (or continue to default: {default_filename}): ") or default_filename
 
     # Instantiate the plugin
     plugin = PlugIn()
diff --git a/plugin/GAISSALabel_plugin/requirements.txt b/plugin/GAISSALabel_plugin/requirements.txt
index 9bb7b4b..ecbb277 100644
--- a/plugin/GAISSALabel_plugin/requirements.txt
+++ b/plugin/GAISSALabel_plugin/requirements.txt
@@ -1,3 +1,4 @@
 tensorflow>=2.0,<3.0
 torch>=2.0.0
+torchvision>=0.15.1
 fvcore>=0.1.5
\ No newline at end of file