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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
from tensorflow.keras.utils import CustomObjectScope
from tensorflow.keras.layers.experimental.preprocessing import RandomHeight
with CustomObjectScope({'RandomHeight': RandomHeight}):
model_0 = tf.keras.models.load_model('bestmodel.h5')
print("TensorFlow version:", tf.__version__)
print("GPU Available:", tf.config.list_physical_devices('GPU'))
# Configure TensorFlow to use memory growth
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
except RuntimeError as e:
print(e)
def classify_image(inp):
# Convert to PIL Image if we have a numpy array
image = None
if isinstance(inp, np.ndarray):
image = Image.fromarray(inp)
else:
image = inp
# Resize image to 224x224
image = image.resize((224, 224), Image.Resampling.LANCZOS)
# Convert to numpy array and ensure correct shape
image_array = np.array(image)
# Handle grayscale images
if len(image_array.shape) == 2:
image_array = np.stack([image_array] * 3, axis=-1)
# Add batch dimension and ensure correct shape
inp = image_array.reshape((-1, 224, 224, 3))
# inp = inp.reshape((-1, 224, 224, 3))
prediction = model_0.predict(inp)
output = ""
if prediction[0][prediction.argmax()] < 0.84:
output = "bonne image"
elif prediction.argmax() == 0:
output = "Rifle violence"
elif prediction.argmax() == 1:
output = "guns violence"
elif prediction.argmax() == 2:
output = "knife violence"
elif prediction.argmax() == 3:
output = "image porno"
elif prediction.argmax() == 4:
output = "personne habillée"
else:
output = "tank violence"
return output
image = gr.Image(height=224, width=224)
gr.Interface(
fn=classify_image, inputs=image, outputs="text",live=True,title="API de détection des images violentes",
).launch()
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