app.py

import gradio as gr
import tensorflow as tf

# Load your TensorFlow model
model = tf.keras.models.load_model("/content/bird_species_classification_model.h5")

# Define your class names if needed
class_names = ['ABBOTTS BABBLER', 'ABBOTTS BOOBY', 'ABYSSINIAN GROUND HORNBILL', 'AFRICAN CROWNED CRANE', 'AFRICAN EMERALD CUCKOO', 'AFRICAN FIREFINCH', 'AFRICAN OYSTER CATCHER', 'AFRICAN PIED HORNBILL', 'AFRICAN PYGMY GOOSE', 'ALBATROSS', 'ALBERTS TOWHEE', 'ALEXANDRINE PARAKEET', 'ALPINE CHOUGH', 'ALTAMIRA YELLOWTHROAT', 'AMERICAN AVOCET', 'AMERICAN BITTERN', 'AMERICAN COOT', 'AMERICAN FLAMINGO', 'AMERICAN GOLDFINCH', 'AMERICAN KESTREL']

# 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 = gr.inputs.Image()  # Remove the `shape` argument
label = gr.outputs.Label()

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