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f66a0f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | import gradio as gr
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np
model = tf.keras.models.load_model("birdie.h5")
class_labels = [...]
def predict_bird_species(img):
img = img.resize((224, 224))
img_array = image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
# Make predictions
predictions = model.predict(img_array)
predicted_class = class_labels[np.argmax(predictions)]
confidence = np.max(predictions)
return {predicted_class: float(confidence)}
# Gradio interface
image_input = gr.inputs.Image(shape=(224, 224))
label_output = gr.outputs.Label(num_top_classes=5)
app = gr.Interface(
fn=predict_bird_species,
inputs=image_input,
outputs=label_output,
title="Bird Species Classifier",
description="Upload an image of a bird to predict its species."
)
app.launch()
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