updated

app.py

@@ -1,23 +1,7 @@
 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 greet(name):
+    return "Hello " + name + "!!"
 
-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
-
-demo = gr.Interface(fn=predict,
-        inputs=gr.inputs.Image(type="pil"),
-        outputs=gr.outputs.Label(num_top_classes=3),
-        examples=[["dog.jpg"]],
-        )
-
-demo.launch()
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()

app2.py

@@ -1,7 +0,0 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()

app_classification.py

@@ -0,0 +1,23 @@
+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
+
+demo = gr.Interface(fn=predict,
+        inputs=gr.inputs.Image(type="pil"),
+        outputs=gr.outputs.Label(num_top_classes=3),
+        examples=[["dog.jpg"]],
+        )
+
+demo.launch()