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}%")