feat: using different format

app copy.py

@@ -0,0 +1,15 @@
+import gradio as gr
+from transformers import pipeline
+
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+
+def predict(image):
+    predictions = pipeline(image)
+    return {p["label"]: p["score"] for p in predictions}
+
+gr.Interface(
+    predict,
+    inputs=gr.inputs.Image(label="Upload car candidate", type="filepath"),
+    outputs=gr.outputs.Label(num_top_classes=2),
+    title="Brazilian nacional? Or Imported?",
+).launch()

app.py

@@ -1,15 +1,32 @@
-import gradio as gr
-from transformers import pipeline
+from fastai.vision.all import *
+from gradio as gr
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+def is_imported(x): return x[o].isupper()
 
-def predict(image):
-    predictions = pipeline(image)
-    return {p["label"]: p["score"] for p in predictions}
+learn  = load_learner("model.pkl")
+categories = ('Imported', 'Nacional')
 
-gr.Interface(
-    predict,
-    inputs=gr.inputs.Image(label="Upload car candidate", type="filepath"),
-    outputs=gr.outputs.Label(num_top_classes=2),
-    title="Brazilian nacional? Or Imported?",
-).launch()
+def classify_image(img):
+    prd,idx,props = learn.predict(img)
+    return dict(zip(categories, map(float, props)))
+
+
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.Label()
+# examples = ["xx.jpg"]
+
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label)
+intf.launch(inline=False)
+# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+
+# def predict(image):
+#     predictions = pipeline(image)
+#     return {p["label"]: p["score"] for p in predictions}
+
+# gr.Interface(
+#     predict,
+#     inputs=gr.inputs.Image(label="Upload car candidate", type="filepath"),
+#     outputs=gr.outputs.Label(num_top_classes=2),
+#     title="Brazilian nacional? Or Imported?",
+# ).launch()