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import gradio as gr
import tensorflow as tf
import pandas as pd
import numpy as np
import cv2
# Load the trained model
model = tf.keras.models.load_model("model.h5")
# Load the bird labels
bird_labels = pd.read_csv("birds.csv")
def classify_image(input_image):
# Preprocess the input image
img = cv2.resize(input_image, (224, 224))
img_array = img / 255.0
img_array = img_array.reshape(1, 224, 224, 3)
# Make predictions
prediction = model.predict(img_array)
# Get the index of the maximum prediction
max_index = np.argmax(prediction[0])
# Get the bird's name and scientific name
bird_name = bird_labels.loc[max_index, "labels"]
scientific_name = bird_labels.loc[max_index, "scientific name"]
return bird_name, scientific_name
# Define the Gradio interface
image_input = gr.inputs.Image(shape=(224, 224))
outputs = [
gr.outputs.Label(label="Bird Name"),
gr.outputs.Label(label="Scientific Name")
]
iface = gr.Interface(
classify_image,
inputs=image_input,
outputs=outputs,
title="Bird Image Classifier",
description="Upload an image of a bird, and this classifier will predict the bird's name and its scientific name.",
examples=[
# Add paths to example images here
],
allow_flagging=False
)
# Launch the Gradio app on Hugging Face Spaces
iface.launch()
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