Rename app_.py to app.py

app.py

@@ -0,0 +1,104 @@
+import gradio as gr
+import pandas as pd
+import umap
+import matplotlib.pyplot as plt
+import os
+import tempfile
+import scanpy as sc
+import argparse
+import subprocess
+from io import BytesIO
+from evaluate import AnndataProcessor
+from accelerate import Accelerator
+from huggingface_hub import hf_hub_download
+
+
+def main(input_file_path, species):
+    
+    # clone and cd into UCE repo
+    os.system('git clone https://github.com/minwoosun/UCE.git')
+    os.chdir('UCE')
+
+    ##############
+    #     UCE    #
+    ##############
+
+    #  python eval_single_anndata.py --adata_path "./data/10k_pbmcs_proc.h5ad" --dir "./" --model_loc "minwoosun/uce-100m"
+    script_name = "eval_single_anndata.py"
+    args = ["--adata_path", input_file_path, "--dir", "/home/user/app/UCE/", "--model_loc", "minwoosun/uce-100m"]
+    command = ["python", script_name] + args
+    
+    try:
+        result = subprocess.run(command, capture_output=True, text=True, check=True)
+        print(result.stdout)
+        print(result.stderr)
+    except subprocess.CalledProcessError as e:
+        print(f"Error executing command: {e}")
+
+
+    ##############
+    #    UMAP    #
+    ##############
+    UMAP = True
+
+    if (UMAP):
+        adata = sc.read_h5ad('/home/user/app/UCE/10k_pbmcs_proc_uce_adata.h5ad')
+
+        labels = pd.Categorical(adata.obs["cell_type"])
+
+        reducer = umap.UMAP(n_neighbors=15, min_dist=0.1, n_components=2, random_state=42)
+        embedding = reducer.fit_transform(adata.obsm["X_uce"])
+
+        plt.figure(figsize=(10, 8))
+
+        # Create the scatter plot
+        scatter = plt.scatter(embedding[:, 0], embedding[:, 1], c=labels.codes, cmap='Set1', s=50, alpha=0.6)
+
+        # Create a legend
+        handles = []
+        for i, cell_type in enumerate(labels.categories):
+            handles.append(plt.Line2D([0], [0], marker='o', color='w', label=cell_type,
+                                      markerfacecolor=plt.cm.Set1(i / len(labels.categories)), markersize=10))
+
+        plt.legend(handles=handles, title='Cell Type')
+        plt.title('UMAP projection of the data')
+        plt.xlabel('UMAP1')
+        plt.ylabel('UMAP2')
+        
+        # Save plot to a BytesIO object
+        buf = BytesIO()
+        plt.savefig(buf, format='png')
+        buf.seek(0)
+        
+        # Read the image from BytesIO object
+        img = plt.imread(buf, format='png')
+    else:
+        img = None
+        print("no image")
+
+    # this need to be changed based on data file name
+    output_file = '/home/user/app/UCE/10k_pbmcs_proc_uce_adata.h5ad'
+    
+    return img, output_file
+
+    
+if __name__ == "__main__":   
+
+    # Define Gradio inputs and outputs
+    file_input = gr.File(label="Upload a .h5ad single cell gene expression file")
+    species_input = gr.Dropdown(choices=["human", "mouse"], label="Select species")
+    image_output = gr.Image(type="numpy", label="UMAP of UCE Embeddings")
+    file_output = gr.File(label="Download embeddings")
+
+    # Create the Gradio interface
+    demo = gr.Interface(
+        fn=main,
+        inputs=[file_input, species_input],
+        outputs=[image_output, file_output],
+        title="UCE 100M Demo",
+        description="Upload a .h5ad single cell gene expression file, and get a UMAP scatter plot along with the UMAP coordinates in a CSV file."
+    )
+
+    demo.launch()    
+
+

app_.py

@@ -1,41 +0,0 @@
-import gradio as gr
-import pandas as pd
-import umap
-import matplotlib.pyplot as plt
-import os
-import tempfile
-import scanpy as sc
-import argparse
-import subprocess
-from io import BytesIO
-from evaluate import AnndataProcessor
-from accelerate import Accelerator
-from huggingface_hub import hf_hub_download
-
-
-def main(input_file_path, species):
-    
-    # clone and cd into UCE repo
-    os.system('git clone https://github.com/minwoosun/UCE.git')
-    os.chdir('UCE')
-
-    #  python eval_single_anndata.py --adata_path "./data/10k_pbmcs_proc.h5ad" --dir "./" --model_loc "minwoosun/uce-100m"
-    script_name = "eval_single_anndata.py"
-    args = ["--adata_path", input_file_path, "--dir", "./", "--model_loc", "minwoosun/uce-100m"]
-    command = ["python", script_name] + args
-    
-    try:
-        result = subprocess.run(command, capture_output=True, text=True, check=True)
-        print(result.stdout)
-        print(result.stderr)
-    except subprocess.CalledProcessError as e:
-        print(f"Error executing command: {e}")
-
-        
-   
-    
-
-if __name__ == "__main__":   
-    
-
-