fix: application to Gradio.

Dockerfile

@@ -25,4 +25,4 @@ WORKDIR $HOME/app
 # Copy the current directory contents into the container at $HOME/app setting the owner to the user
 COPY --chown=user . $HOME/app
 
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["python", "main.py"]

main.py

@@ -1,15 +1,25 @@
-from fastapi import FastAPI
 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")
 
-app = FastAPI()
 
+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
 
-def greet(name):
-    return "Hello " + name + "!"
 
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs=gr.outputs.Label(num_top_classes=3),
+)
 
-@app.get("/")
-def read_root():
-    demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-    demo.launch()
+demo.launch()

requirements.txt

@@ -1,4 +1,4 @@
-fastapi==0.74.*
-requests==2.27.*
-uvicorn[standard]==0.17.*
 gradio
+torch
+torchvision
+requests