Upload 4 files

aal_mask_pad.nii.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:230ad628e55f722a2ebaba02bebd913e7d523ab3725fc8aa2891f052ec9ec43f
+size 12050

app.py

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+import gradio as gr
+import nibabel as nib
+import numpy as np
+import os
+import shutil
+import pickle
+import pandas as pd
+
+# Function to load the model from the pickle file
+def load_model():
+    with open('svm_pipeline.pkl', 'rb') as f:
+        return pickle.load(f)
+
+# Load the trained model
+model = load_model()
+
+# Function to load image data from a filepath
+def get_image_data(filepath):
+    '''
+    Access the floating point data of an image
+    
+    Input: Filepath to the image
+    
+    Output: The image's floating point data
+    '''
+    img = nib.load(filepath)
+    data = img.get_fdata()
+    return data
+
+# Function to create a vector from a region by time matrix from an image using the atlas
+def image_to_vector(image_data, atlas_data):
+    '''
+    Create a vector from a region by time matrix from an image using the atlas
+    
+    Input:
+        - Data for the image to take points of
+        - Data from the atlas to apply to the image data
+    
+    Output: A vector of the image's region by time matrix
+    '''
+    # Assuming the time dimension is the last dimension in the image data
+    time_dim = image_data.shape[-1]
+    column_names = [f'time_{i}' for i in range(time_dim)]
+    region_names = [f'region_{region}' for region in np.unique(atlas_data)]
+
+    # Reshape the image data to 2D (voxels x time)
+    reshaped_image_data = image_data.reshape(-1, time_dim)
+
+    # Create DataFrame with image data
+    df_times = pd.DataFrame(reshaped_image_data, columns=column_names)
+    
+    # Reshape the atlas data to 1D (voxels)
+    reshaped_atlas_data = atlas_data.reshape(-1)
+    
+    # Combine atlas regions with image data
+    df_full = pd.concat([pd.Series(reshaped_atlas_data, name='atlas_region'), df_times], axis=1)
+    
+    # Group by atlas region and compute mean over time
+    regions_x_time = df_full.groupby('atlas_region').mean()
+    regions_x_time.index = region_names
+    
+    # Flatten the region x time matrix to a vector
+    regions_x_time_vector = regions_x_time.to_numpy().reshape(-1)
+    return regions_x_time_vector
+
+# Function to preprocess the input image and extract features
+def preprocess_and_extract_features(nifti_data, atlas_data):
+    '''
+    Preprocess the input image data and extract features using the atlas.
+    
+    Input:
+        - nifti_data: The NIfTI image data
+        - atlas_data: The atlas data
+    
+    Output: Extracted feature vector
+    '''
+    features = image_to_vector(nifti_data, atlas_data)
+    num_required_features = 116
+
+    # If fewer features are found, pad with zeros; if more, truncate
+    if features.size < num_required_features:
+        features = np.pad(features, (0, num_required_features - features.size), 'constant')
+    else:
+        features = features[:num_required_features]
+
+    return features.reshape(1, -1)
+
+def predict_region(input_file):
+    temp_file_path = None  # Initialize temp_file_path to None
+    try:
+        # Create a temporary file with the correct extension
+        temp_file_path = input_file.name + ".nii.gz"
+        shutil.copy(input_file.name, temp_file_path)
+        
+        # Load the NIfTI file and the atlas
+        img = nib.load(temp_file_path)
+        data = img.get_fdata()
+        
+        # Path to the atlas file
+        atlas_filepath = 'aal_mask_pad.nii.gz'  # Corrected file extension
+        if not os.path.exists(atlas_filepath):
+            raise FileNotFoundError(f"Atlas file not found at: {atlas_filepath}")
+        
+        atlas_data = get_image_data(atlas_filepath)
+        
+        # Preprocess and extract features
+        features = preprocess_and_extract_features(data, atlas_data)
+
+        # Predict using the loaded model
+        prediction = model.predict(features)
+        return str(prediction[0])
+    except Exception as e:
+        return f"Error: {e}"
+    finally:
+        # Clean up the temporary file
+        if temp_file_path and os.path.exists(temp_file_path):
+            os.remove(temp_file_path)
+            
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=predict_region,
+    inputs=gr.File(label="Region Image (NIfTI file)"),
+    outputs="text",
+    title="Region Prediction",
+    description="Upload a region image in NIfTI format to get the prediction.",
+    allow_flagging="never"  # Disable flagging
+)
+
+# Launch the Gradio interface
+interface.launch(share=True)

requirements.txt

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+gradio
+nibabel
+numpy
+scikit-learn
+pandas

svm_pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2cabd035b158794778139cfbefa402a17f4038ab2a8e26f6144956d68b8c8b8c
+size 351512