test

app.py

@@ -1,25 +1,49 @@
-import gradio as gr
+import gradio
 from fastai.vision.all import *
 
-def greet(name):
+
+def gradioeet(name):
     return "Hello " + name + "!!"
 
-learn = load_learner("model.pkl");
 
 def is_cat(x): return x[0].isupper()
 
 
-categories = {"No_Cat","Cat"}
+# path = untar_data(URLs.DOGS) / 'images'
+# dls = ImageDataLoaders.from_name_func('.',
+#                                       get_image_files(path), valid_pct=0.2, seed=42,
+#                                       label_func=is_cat,
+#                                       item_tfms=Resize(192))
+#
+# dls.valid.show_batch(max_n=4, nrows=1)
+#
+
+#
+# learn = vision_learner(dls, resnet18, metrics=error_rate)
+# learn.fine_tune(3)
+
+# interp = ClassificationInterpretation.from_learner(learn)
+# interp.plot_confusion_matrix()
+#
+# learn.export('model2.pkl')
+
+
+learn = load_learner("model_last.pkl")
+
+categories = {"No_Cat", "Cat"}
+
+
 def classify_image(img):
-    pred,idx,probs = learn.predict(img)
-    return dict(zip(categories, map(float,probs)))
+    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 = ["[email protected]","[email protected]","[email protected]","[email protected]"]
-intf = gr.Interface(fn=classify_image,inputs = image,outputs = label,examples = examples)
-intf.launch(inline=False)
 
+image = gradio.Image(height=192, width=192)
+label = gradio.Label()
+examples = ["[email protected]", "[email protected]", "[email protected]",
+            "[email protected]"]
+intf = gradio.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# demo = gradio.Interface(fn=gradioeet, inputs="text", outputs="text")
+# demo.launch(inline=False)