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app.py

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+from fastai.vision.all import *
+import gradio as gr
+
+learn = load_learner('model_extended.pkl')
+
+# categories = 'Sunflower', 'Orchid', 'Rose'
+
+def classify_image(img):
+    pred, idx, probs = learn.predict(img)
+    return pred
+
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.Label()
+examples = ['sunflower.jpeg', 'orchid.jpeg', 'rose.jpeg']
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)
+
+# from fastai.vision.all import *
+# import gradio as gr
+
+# # Load the pre-trained model
+# learn = load_learner('model.pkl')
+
+# # Define the categories that the model can classify
+# categories = ['Sunflower', 'Orchid', 'Rose']
+
+# # Define the function to classify an image and return the predicted category label
+# def classify_image(img):
+#     pred, idx, probs = learn.predict(img)
+#     return categories[idx]
+
+# # Define the input and output types for the Gradio interface
+# image_input = gr.inputs.Image(shape=(224, 224))
+# label_output = gr.outputs.Label()
+
+# # Define example images for the interface
+# examples = [
+#     ['sunflower.jpeg'],
+#     ['orchid.jpeg'],
+#     ['rose.jpeg']
+# ]
+
+# # Create the Gradio interface
+# interface = gr.Interface(
+#     fn=classify_image, 
+#     inputs=image_input, 
+#     outputs=label_output,
+#     examples=examples,
+#     title="Image Classifier",
+#     description="This app classifies images into three categories: Sunflower, Orchid, and Rose."
+# )
+
+# # Launch the interface
+# interface.launch()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:77a5e0369399e981322d28116b109679852326dc8db746aee089e7bdce19919a
+size 46958481

model_extended.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:661066c7fffc09dfbcdd13fb68c6d423bb70cc2a3ab64df42cd8456088dafd6c
+size 114659368

requirements.txt

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+fastai
+torch
+gradio
+numpy
+pandas