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#|export | |
from fastai.vision.all import * | |
import os | |
import gradio as gr | |
def is_flower(x): return x[0].isupper() | |
#export | |
learn = load_learner('mexicanPlants (1).pkl') | |
#|export | |
categories = learn.dls.vocab | |
def classify_image(img): | |
pred,idx,probs = learn.predict(img) | |
return dict(zip(categories,map(float,probs))) | |
# Get the current directory | |
current_dir = os.getcwd() | |
# Get a list of all files in the directory | |
all_files = os.listdir(current_dir) | |
# Create a list of categories | |
# Create an empty list to store the photos | |
photos = [] | |
# Loop through all the files | |
for file in all_files: | |
# Check if the file is a photo | |
if file.endswith(('.jpg', '.jpeg', '.png', '.bmp', '.gif')): | |
# If it is, add it to the list of photosgi | |
photos.append(file) | |
image = gr.inputs.Image(shape=(192,192)) | |
label = gr.outputs.Label() | |
examples = photos | |
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples) | |
intf.launch(inline=False) |