test

app.py

@@ -1,130 +1,133 @@
-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
 import streamlit as st
-
-# 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}%")
+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}%")