compilation

main.py

@@ -1,30 +1,37 @@
 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 subprocess
+import tempfile
+
+def compile(source):
+    # Create a temporary file for the C source code
+    with tempfile.NamedTemporaryFile(suffix=".c", delete=False) as temp_c_file:
+        temp_c_file.write(source.encode())
+        temp_c_file_name = temp_c_file.name
+    
+    # Create a temporary file for the object file
+    with tempfile.NamedTemporaryFile(suffix=".o", delete=False) as temp_o_file:
+        temp_o_file_name = temp_o_file.name
+    
+    # Compile the C file to an object file
+    subprocess.run(["cc", "-c", temp_c_file_name, "-o", temp_o_file_name], check=True)
+    
+    # Read the compiled object file and load the bytes into a bytestring
+    with open(temp_o_file_name, "rb") as f:
+        compiled_bytes = f.read()
+    
+    return compiled_bytes
+
+def predict(source):
+    bytes = compile(source)
+    return 1.0
 
 
 def run():
     demo = gr.Interface(
         fn=predict,
-        inputs=gr.Image(type="pil"),
-        outputs=gr.Label(num_top_classes=3),
+        inputs=gr.Textbox,
+        outputs=gr.Number,
     )
 
     demo.launch(server_name="0.0.0.0", server_port=7860)
-
-
-if __name__ == "__main__":
-    run()

requirements.txt

@@ -1,4 +1,3 @@
 gradio
 torch
-torchvision
 requests