simplify

main.py

@@ -1,28 +1,18 @@
 import gradio as gr
-import torch
-import requests
 import frontmatter
-from torchvision import transforms
 
 description = frontmatter.load("README.md").content
-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
+    return "hi"
 
 
 def run():
     demo = gr.Interface(
         fn=predict,
-        inputs=gr.Image(type="pil"),
-        outputs=gr.Label(num_top_classes=3),
+        inputs=gr.Textbox(),
+        outputs=gr.Textbox(),
         description=description
     )
 

requirements.txt

@@ -1,5 +1,2 @@
 gradio
-torch
-torchvision
-requests
 python-frontmatter