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import streamlit as st
from PIL import Image
import tensorflow as tf
import numpy as np
import os
# Caching the model loading function to optimize performance
@st.cache_resource
def load_model():
model_path = "captcha.keras" # Update with the actual model path
return tf.keras.models.load_model(model_path)
# Load the model
model = load_model()
# Function to prepare the image for model prediction
def prepare_image(img):
try:
# Resize image to the input shape required by the model
img = img.resize((200, 50)) # Adjust size according to the trained model
img_array = np.array(img.convert('L')) # Convert to grayscale if necessary
img_array = img_array / 255.0 # Normalize image
img_array = np.expand_dims(img_array, axis=-1) # Add channel dimension
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
# Predict the output using the loaded model
predictions = model.predict(img_array)
# Decode predictions assuming the model outputs probabilities
decoded_captcha = ''.join([chr(np.argmax(pred) + ord('A')) for pred in predictions])
return decoded_captcha, predictions
except Exception as e:
st.error(f"Error preparing image: {e}")
return None, None
# Main function to run the Streamlit app
def run():
st.title("CAPTCHA Prediction")
img_file = st.file_uploader("Upload a CAPTCHA Image", type=["jpg", "png", "jpeg"])
if img_file is not None:
img = Image.open(img_file)
st.image(img, caption="Uploaded CAPTCHA", use_column_width=True)
# 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_image(img)
if predicted_captcha:
st.success(f"**Predicted CAPTCHA: {predicted_captcha}**")
else:
st.error("Failed to predict CAPTCHA.")
# Run the Streamlit app
if __name__ == "__main__":
run()
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