app.py

from fastai.vision.all import *
import gradio as gr
from timm import *

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()