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import cv2
from tensorflow.keras.models import load_model
import gradio as gr
import tensorflow as tf
import cv2
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
# Load the trained model
model = load_model('/content/cat_classifier_model.h5')
# Function to predict whether an image contains a cat
def predict_cat(image_content):
# Convert image content to PIL Image
img = Image.open(BytesIO(image_content))
img = img.convert('RGB')
img = img.resize((224, 224))
img_array = np.array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = img_array / 255.0 # Rescale to values between 0 and 1 (same as during training)
prediction = model.predict(img_array)
if prediction[0][0] > 0.5:
return "not a tablet"
else:
return "is a tablet"
# Create a Gradio interface
iface = gr.Interface(
fn=predict_cat,
inputs=gr.Image(type='file', label='Upload an image of a tablet'),
outputs='text'
)
# Launch the interface with share=True to create a public link
iface.launch(share=True)
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