app.py

from gradio import Interface, Image, Label
import tensorflow as tf
# Load your TensorFlow model
model = tf.keras.models.load_model("a.h5")

# Define your class names if needed
class_names = ['Asian-Green-Bee-Eater', 'Brown-Headed-Barbet', 'Cattle-Egret', 'Common-Kingfisher', 'Common-Myna', 'House-Crow', 'Indian-Grey-Hornbill', 'Indian-Peacock', 'Indian-Roller', 'White-Breasted-Kingfisher']


# Function to make predictions
def classify_image(image):
    # Preprocess the image
    img = tf.image.resize(image, (224, 224))
    img = tf.expand_dims(img, 0)  # Add batch dimension
    # Make prediction
    prediction = model.predict(img)
    predicted_class = class_names[prediction.argmax()]
    return predicted_class

# Gradio interface
image = Image()  # Remove the `shape` argument
label = Label()

# Create interface
interface = Interface(classify_image, image, label,
                      title="Bird Species Classification",
                      description="Upload an image of a bird to classify its species.").launch()