import streamlit as st st.write("Test system if working") import zipfile import os import requests 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") # Extract the model using zipfile with zipfile.ZipFile(zip_file_path, 'r') as zip_ref: zip_ref.extractall('my_authorship_model') # Debugging: Check if the folder is successfully created if os.path.exists('my_authorship_model'): st.write("Model folder successfully extracted using zipfile") # 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 zipfile") 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}%")