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import gradio as gr
import tensorflow as tf
from tensorflow.keras.models import load_model
import numpy as np
import cv2
# Load the trained model
model_path = './model_checkpoint_manual_resnet.h5'
model = load_model(model_path)
class_names = ['ADONIS', 'AFRICAN GIANT SWALLOWTAIL', 'AMERICAN SNOOT', 'AN 88', 'APPOLLO', 'ARCIGERA FLOWER MOTH', 'ATALA', 'ATLAS MOTH', 'BANDED ORANGE HELICONIAN', 'BANDED PEACOCK']
# Define a function to preprocess the input image
def preprocess_image(img):
# Check if img is a file path or an image object
if isinstance(img, str):
# Load and preprocess the image
img = cv2.imread(img)
img = cv2.resize(img, (224, 224))
img = img / 255.0 # Normalize pixel values
img = np.expand_dims(img, axis=0) # Add batch dimension
elif isinstance(img, np.ndarray):
# If img is already an image array, resize it
img = cv2.resize(img, (224, 224))
img = img / 255.0 # Normalize pixel values
img = np.expand_dims(img, axis=0) # Add batch dimension
else:
raise ValueError("Unsupported input type. Please provide a file path or a NumPy array.")
return img
# Define the classification function
def classify_image(img):
# Preprocess the image
img = preprocess_image(img)
# Make predictions
predictions = model.predict(img)
# Get the predicted class label
predicted_class = np.argmax(predictions)
# Get the predicted class name
predicted_class_name = class_names[predicted_class]
return f"Predicted Class: {predicted_class_name}"
# Create a Gradio interface
iface = gr.Interface(fn=classify_image,
inputs="image",
outputs="text",
live=True)
# Launch the Gradio app
iface.launch() |