Update app.py

app.py

@@ -1,7 +1,74 @@
 import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+import plotly.graph_objects as go
+from mpl_toolkits.mplot3d import Axes3D
+from skimage import measure
+from mpl_toolkits.mplot3d.art3d import Poly3DCollection
+from lungmask import LMInferer
+import SimpleITK as sitk
+import os
 
-def greet(name):
-    return "Hello " + name + "!!"
+# --- Lung Segmentation Functions ---
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+def process_dcm_file(file_path, inferer):
+    """Loads a DCM file, performs lung segmentation, and returns the mask."""
+    input_image = sitk.ReadImage(file_path)
+    segmentation = inferer.apply(input_image)
+    newseg = segmentation.reshape(512, 512)  # Assuming 512x512 images
+    return newseg
+
+def segment_lungs_from_dicom(dcm_folder):
+    """Segments lungs from DICOM files in a folder and returns a 3D volume."""
+    if not os.path.exists(dcm_folder) or not os.path.isdir(dcm_folder):
+        raise ValueError("Invalid DICOM folder path.")
+
+    inferer = LMInferer()
+    segmentation_masks = []
+    for filename in os.listdir(dcm_folder):
+        if filename.endswith(".dcm"):
+            file_path = os.path.join(dcm_folder, filename)
+            mask = process_dcm_file(file_path, inferer)
+            segmentation_masks.append(mask)
+    volume = np.stack(segmentation_masks, axis=0)
+    return volume
+
+# --- 3D Visualization Function ---
+
+def plot_3d_lungs(lungs_volume, threshold=0.5):
+    """Creates an interactive 3D plot of segmented lungs using Plotly (upright)."""
+    verts, faces, normals, values = measure.marching_cubes(lungs_volume.transpose(2, 1, 0), threshold)
+
+    # Apply rotation to make lungs upright
+    # Assuming you want to rotate 90 degrees counter-clockwise around the X-axis
+    rotation_angle_degrees = -90
+    rotation_angle_radians = np.radians(rotation_angle_degrees)
+    rotation_matrix = np.array([[1, 0, 0],
+                               [0, np.cos(rotation_angle_radians), -np.sin(rotation_angle_radians)],
+                               [0, np.sin(rotation_angle_radians), np.cos(rotation_angle_radians)]])
+    rotated_verts = np.dot(verts, rotation_matrix)
+
+    x, y, z = zip(*rotated_verts)  # Use rotated vertices
+    i, j, k = zip(*faces)
+
+    mesh = go.Mesh3d(x=x, y=y, z=z, i=i, j=j, k=k, opacity=0.7, color='lightblue')
+    fig = go.Figure(data=[mesh])
+    fig.update_layout(scene_aspectmode='data')  # Maintain aspect ratio
+    return fig
+
+
+
+# --- Create Gradio Interface ---
+
+inputs = gr.Textbox(label="DICOM Folder Path")
+output = gr.Plot(label="3D Segmented Lungs")
+
+iface = gr.Interface(
+    fn=lambda dcm_folder: plot_3d_lungs(segment_lungs_from_dicom(dcm_folder)),
+    inputs=inputs,
+    outputs=output,
+    title="3D Lung Segmentation Visualization",
+    description="Visualize segmented lungs from DICOM images.",
+)
+
+iface.launch()