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import streamlit as st
from PIL import Image
from tensorflow.keras.models import load_model
import tensorflow as tf
import numpy as np
from keras.preprocessing.image import img_to_array
import os
@st.cache_resource
def load_model():
model_path = "model.h5" # Update with the actual CAPTCHA model path
return tf.keras.models.load_model(model_path)
model = load_model()
def prepare_captcha_image(img):
# Resize image to the input shape required by the CAPTCHA model
img = img.resize((200, 50)) # Adjust size according to the trained model
img_array = np.array(img)
img_array = img_array / 255.0 # Normalize image
img_array = np.expand_dims(img_array, axis=0)
# Predict the CAPTCHA characters
predictions = model.predict(img_array)
# Assuming the model outputs one-hot encoded characters, decode the predictions
decoded_captcha = ''.join([chr(np.argmax(pred) + ord('A')) for pred in predictions])
return decoded_captcha, predictions
def run():
st.title("CAPTCHA Prediction")
img_file = st.file_uploader("Upload a CAPTCHA Image", type=["jpg", "png"])
if img_file is not None:
img = Image.open(img_file)
st.image(img, use_column_width=False)
# Create the directory if it doesn't exist
upload_dir = './upload_images/'
os.makedirs(upload_dir, exist_ok=True)
# Save the uploaded image
save_image_path = os.path.join(upload_dir, img_file.name)
with open(save_image_path, "wb") as f:
f.write(img_file.getbuffer())
# Predict the CAPTCHA
predicted_captcha, score = prepare_captcha_image(img)
st.success(f"**Predicted CAPTCHA: {predicted_captcha}**")
run()
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