Update app.py

app.py

@@ -1,133 +1,133 @@
 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}%")
+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}%")