Update main.py

main.py

@@ -1,30 +1,33 @@
 import gradio as gr
-import torch
-import requests
-from torchvision import transforms
-
-model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True).eval()
-response = requests.get("https://git.io/JJkYN")
-labels = response.text.split("\n")
-
-
-def predict(inp):
-    inp = transforms.ToTensor()(inp).unsqueeze(0)
-    with torch.no_grad():
-        prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
-        confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
-    return confidences
-
+import os
+import subprocess
+
+def install_server():
+    output = ""
+    try:
+        os.mkdir("milvus_compose")
+        output += "Created directory 'milvus_compose'\n"
+        subprocess.run(["wget", "https://github.com/milvus-io/milvus/releases/download/v2.3.0-beta/milvus-standalone-docker-compose.yml", "-O", "milvus_compose/docker-compose.yml"], check=True)
+        output += "Downloaded 'docker-compose.yml'\n"
+        #subprocess.run(["docker-compose", "up", "-d"], check=True)
+        #output += "Started Milvus server\n"
+    except Exception as e:
+        output += str(e)
+    return output
+
+def list_files():
+    files = os.listdir('.')
+    return files
 
 def run():
-    demo = gr.Interface(
-        fn=predict,
-        inputs=gr.inputs.Image(type="pil"),
-        outputs=gr.outputs.Label(num_top_classes=3),
+    iface = gr.Interface(
+        fn=[install_server, list_files],
+        inputs=None,
+        outputs=["text", "text"],
+        layout="horizontal",
+        title="Milvus Server Installation"
     )
-
-    demo.launch(server_name="0.0.0.0", server_port=7860)
-
+    iface.launch(server_name="0.0.0.0", server_port=7860)
 
 if __name__ == "__main__":
     run()