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import gradio as gr
import numpy as np
import cv2
import pickle
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array
# Load the model and the label binarizer
model = load_model('cnn_model.h5')
label_binarizer = pickle.load(open('label_transform.pkl', 'rb'))
# Function to convert images to array
def convert_image_to_array(image):
try:
image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR)
if image is not None:
image = cv2.resize(image, (256, 256))
return img_to_array(image)
else:
return np.array([])
except Exception as e:
print(f"Error: {e}")
return None
def predict_image(image):
try:
image_array = convert_image_to_array(image)
if image_array.size == 0:
return "Invalid image"
# Normalize the image
image_array = np.array(image_array, dtype=np.float16) / 255.0
# Ensure the image_array has the correct shape (1, 256, 256, 3)
image_array = np.expand_dims(image_array, axis=0)
# Make a prediction
prediction = model.predict(image_array)
predicted_class = label_binarizer.inverse_transform(prediction)[0]
return predicted_class
except Exception as e:
return str(e)
# Define Gradio interface
interface = gr.Interface(
fn=predict_image,
inputs=gr.Image(type="numpy"),
outputs="text",
title="Image Classification",
description="Upload an image to get the predicted class."
)
if __name__ == "__main__":
interface.launch(share=True)
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