Create app.py

app.py

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+import gradio as gr
+from cellpose import models
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+import tempfile
+from PIL import Image
+import io 
+from huggingface_hub import hf_hub_download
+
+HF_REPO_ID = "myang4218/cellposemodel" 
+HF_MODEL_FILENAME = "cellposemodel.npy"
+
+
+
+model_path = hf_hub_download(repo_id=HF_REPO_ID, filename=HF_MODEL_FILENAME)
+print(f"Model downloaded to: {model_path}")
+
+# Load pretrained Cellpose model (nuclei or cyto depending on your data)
+model = models.CellposeModel(gpu=True, pretrained_model=model_path)
+
+
+def segment_and_count(image):
+    # Convert PIL Image to numpy array
+    image_np = np.array(image)
+
+    # If grayscale, convert to RGB
+    if len(image_np.shape) == 2:
+        image_np = cv2.cvtColor(image_np, cv2.COLOR_GRAY2RGB)
+
+    # Run Cellpose
+    masks, flows, styles = model.eval(image_np, diameter=None, channels=[0, 0])
+
+    # Count unique cells
+    cell_count = len(np.unique(masks)) - 1  # subtract 1 for background
+
+    # Overlay mask for display
+    overlay = image_np.copy()
+    overlay[masks > 0] = [255, 0, 0]  # red overlay for segmented cells
+
+    # Convert result to PIL Image for output
+    overlay_image = Image.fromarray(overlay)
+
+    return cell_count, overlay_image
+
+# Gradio interface
+demo = gr.Interface(
+    fn=segment_and_count,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Number(label="Number of Cells"), gr.Image(type="pil", label="Segmented Overlay")],
+    title="Cell Counter with Cellpose",
+    description="Upload a microscopy image and count the number of cells using Cellpose segmentation."
+)
+
+if __name__ == "__main__":
+    demo.launch()