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from fastai.vision.all import load_learner
import gradio as gr
import numpy as np
import pathlib
temp = pathlib.PosixPath
pathlib.PosixPath = pathlib.WindowsPath
# Function for recognizing species and behavior from an image
def recognize_image(input_image):
# Add your model loading code here if not already loaded
model = load_learner("final-v0.pkl")
# Make predictions
pred, idx, probabilities = model.predict(input_image)
# Get all labels from the model's vocabulary
all_labels = model.dls.vocab
# List of behavior labels
behavior_labels = ['F', 'H', 'M', 'M', 'P', 'R', 'T']
# Get the top-k predicted labels
top_k = 4
top_indices = (-probabilities).argsort()[:top_k]
top_labels = [all_labels[i] for i in top_indices]
top_probabilities = [float(probabilities[i]) for i in top_indices]
# Separate labels into behavior and species categories
behavior_predictions = [label for label in top_labels if label in behavior_labels]
species_predictions = [label for label in top_labels if label not in behavior_labels]
# Create dictionaries for species and behavior predictions with their probabilities
species_result = {
'Species Predictions': dict(zip(species_predictions, [round(prob, 4) for prob in top_probabilities if prob not in behavior_labels])),
}
behavior_result = {
'Behavior Predictions': dict(zip(behavior_predictions, [round(prob, 4) for prob in top_probabilities if prob in behavior_labels])),
}
return species_result, behavior_result
# Gradio interface
input_image = gr.inputs.Image(type='numpy', label='Upload Image')
output_species = gr.outputs.Label(label='Species Predictions')
output_behavior = gr.outputs.Label(label='Behavior Predictions')
gr.Interface(
fn=recognize_image,
inputs=input_image,
outputs=[output_species, output_behavior],
title='Species and Behavior Recognition',
description='Upload an image and get predictions for species and behavior.'
).launch()
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