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import streamlit as st
st.write("Test system if working")
import os
import requests
import subprocess # Import the subprocess module
from keras.models import load_model
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.preprocessing import LabelEncoder
import pickle
import numpy as np
# Custom headers for the HTTP request
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3',
}
# Debugging: Print current working directory initially
st.write(f"Initial Current Working Directory: {os.getcwd()}")
# Check if the model folder exists
zip_file_path = "my_authorship_model_zip.zip"
if not os.path.exists('my_authorship_model'):
try:
# Download the model
model_url = 'https://jaifar.net/ADS/my_authorship_model_zip.zip'
r = requests.get(model_url, headers=headers)
r.raise_for_status()
# Debugging: Check if download is successful by examining content length
st.write(f"Downloaded model size: {len(r.content)} bytes")
# Save the downloaded content
with open(zip_file_path, "wb") as f:
f.write(r.content)
# Debugging: Verify that the zip file exists
if os.path.exists(zip_file_path):
st.write("Zip file exists")
# # Debugging: List contents of the zip file using unzip
# subprocess.run(['unzip', '-l', zip_file_path])
# Extract the model using unzip
unzip_result = subprocess.run(['unzip', '-o', zip_file_path, '-d', 'my_authorship_model'])
# Debugging: Check unzip exit code (0 means success)
if unzip_result.returncode == 0:
st.write("Model folder successfully extracted using unzip")
# Debugging: List the directory contents after extraction
st.write("Listing directory contents:")
st.write(os.listdir('.'))
else:
st.write("Model folder was not extracted successfully using unzip")
exit(1)
else:
st.write("Zip file does not exist")
exit(1)
except Exception as e:
st.write(f"Failed to download or extract the model: {e}")
exit(1)
else:
st.write("Model folder exists")
# Debugging: Print current working directory after extraction
st.write(f"Current Working Directory After Extraction: {os.getcwd()}")
# Debugging: Check if model folder contains required files
try:
model_files = os.listdir('my_authorship_model')
st.write(f"Files in model folder: {model_files}")
except Exception as e:
st.write(f"Could not list files in model folder: {e}")
# Download the required files
file_urls = {
'tokenizer.pkl': 'https://jaifar.net/ADS/tokenizer.pkl',
'label_encoder.pkl': 'https://jaifar.net/ADS/label_encoder.pkl'
}
for filename, url in file_urls.items():
try:
r = requests.get(url, headers=headers)
r.raise_for_status()
with open(filename, 'wb') as f:
f.write(r.content)
except Exception as e:
st.write(f"Failed to download {filename}: {e}")
exit(1)
# Load the saved model
loaded_model = load_model("my_authorship_model")
# Load the saved tokenizer and label encoder
with open('tokenizer.pkl', 'rb') as handle:
tokenizer = pickle.load(handle)
with open('label_encoder.pkl', 'rb') as handle:
label_encoder = pickle.load(handle)
max_length = 300 # As defined in the training code
# Function to predict author for new text
def predict_author(new_text, model, tokenizer, label_encoder):
sequence = tokenizer.texts_to_sequences([new_text])
padded_sequence = pad_sequences(sequence, maxlen=max_length, padding='post', truncating='post')
prediction = model.predict(padded_sequence)
predicted_label = label_encoder.inverse_transform([prediction.argmax()])[0]
probabilities = prediction[0]
author_probabilities = {}
for idx, prob in enumerate(probabilities):
author = label_encoder.inverse_transform([idx])[0]
author_probabilities[author] = prob
return predicted_label, author_probabilities
st.markdown("CNN : version: 1.2")
new_text = st.text_area("Input your text here")
# Creates a button named 'Press me'
press_me_button = st.button("Which Model Used?")
if press_me_button:
predicted_author, author_probabilities = predict_author(new_text, loaded_model, tokenizer, label_encoder)
sorted_probabilities = sorted(author_probabilities.items(), key=lambda x: x[1], reverse=True)
st.write(f"The text is most likely written by: {predicted_author}")
st.write("Probabilities for each author are (sorted):")
for author, prob in sorted_probabilities:
st.write(f"{author}: {prob * 100:.2f}%")
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