renamed the file

app.py

@@ -1,7 +1,21 @@
+#using gradio for deployment:
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+def is_bear(x): return x[0].isupper()
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+learn = load_learner("model.pkl")
+
+categories = ("grizzly bears", "black bears", "teddy bears")
+
+#Since gradio doesnt recognize tensors, we are uasing map function:
+def classify_image(img):
+    pred, idx, probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples =["black_teddy.jpg", "grizzly_bear.jpg", "black_bear.jpg"]
+
+intf = gr.Interface(fn=classify_image, inputs =image, outputs=label, examples=examples)
+intf.launch(inline=False)

app_1.py

@@ -1,21 +0,0 @@
-#using gradio for deployment:
-from fastai.vision.all import *
-import gradio as gr
-
-def is_bear(x): return x[0].isupper()
-
-learn = load_learner("model.pkl")
-
-categories = ("grizzly bears", "black bears", "teddy bears")
-
-#Since gradio doesnt recognize tensors, we are uasing map function:
-def classify_image(img):
-    pred, idx, probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
-
-image = gr.inputs.Image(shape=(192, 192))
-label = gr.outputs.Label()
-examples =["black_teddy.jpg", "grizzly_bear.jpg", "black_bear.jpg"]
-
-intf = gr.Interface(fn=classify_image, inputs =image, outputs=label, examples=examples)
-intf.launch(inline=False)

app_2.py

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