new version

app.py

@@ -1,40 +1,30 @@
 
+
 import streamlit as st
 #subtitle 
-st.markdown("version: 1.2")
+st.markdown("version: 2.0")
 #title 
 st.title("Smart Detection System of AI-Generated Text Models")
 
 #subtitle 
 st.markdown("This is a POC for Smart Detection System of AI Generated Text Models project (:blue[MSc Data Analytics]), it is a pre-trained model that detect the probablities of using any of the known LLM (chatgpt3, chatgpt4, GoogleBard, HuggingfaceChat)")
 
+import pickle
+import pandas as pd
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.linear_model import RidgeClassifier
 import os
 import requests
-# import pickle
-import pandas as pd
-import nltk
-import spacy
-from nltk.corpus import stopwords
-from nltk.tokenize import word_tokenize, sent_tokenize
+
 import numpy as np
 ############
-from nltk.stem import WordNetLemmatizer
-from nltk import ne_chunk, pos_tag, word_tokenize
-from nltk.tree import Tree
-from joblib import dump, load
-nltk.download('wordnet')
-nltk.download('maxent_ne_chunker')
-nltk.download('words')
-
-#######
-nltk.download('punkt')
-nltk.download('stopwords')
-nltk.download('averaged_perceptron_tagger')
+
+
 
 # Check if the file exists
-if not os.path.isfile('RandomForestClassifier.joblib'):
+if not os.path.isfile('ridge_100%_BOW_ngram_full_text.pkl'):
 
-    url = 'https://jaifar.net/RandomForestClassifier.joblib'
+    url = 'https://jaifar.net/ridge_100%_BOW_ngram_full_text.pkl'
     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',
     }
@@ -42,116 +32,230 @@ if not os.path.isfile('RandomForestClassifier.joblib'):
     response = requests.get(url, headers=headers)
 
 
-    with open('RandomForestClassifier.joblib', 'wb') as file:
+    with open('ridge_100%_BOW_ngram_full_text.pkl', 'wb') as file:
         file.write(response.content)
 
 
-# Load the model from the file
-clf_loaded = load('RandomForestClassifier.joblib')
-
-# # At this point, the pickle file should exist, either it was already there, or it has been downloaded and extracted.
-# with open('RandomForestClassifier.pkl', 'rb') as file:
-#     clf_loaded = pickle.load(file)
+# At this point, the pickle file should exist, either it was already there, or it has been downloaded and extracted.
+with open('ridge_100%_BOW_ngram_full_text.pkl', 'rb') as file:
+    clf_loaded = pickle.load(file)
 
 input_paragraph = st.text_area("Input your text here")
 words_counts = word_tokenize(input_paragraph)
 final_words = len(words_counts)
 st.write('Words counts: ', final_words)
 
-
-
-# df = pd.DataFrame(columns=["paragraph"])
-# df = df.append({"paragraph": input_paragraph}, ignore_index=True)
+# Creates a button named 'Press me'
+press_me_button = st.button("Which Model Used?")
 
 df = pd.DataFrame([input_paragraph], columns=["paragraph"])
 
-
-
-# Variable to control number of words to retrieve
-num_words = 500
-
-# Retrieving only the first num_words words of the paragraph
-input_paragraph = ' '.join(word_tokenize(input_paragraph)[:num_words])
-
 # Extracting features
 def extract_features(text):
-    words = word_tokenize(text)
-    sentences = sent_tokenize(text)
-
-    avg_word_length = sum(len(word) for word in words if word.isalpha()) / len(words)
-    avg_sent_length = sum(len(sent) for sent in sentences) / len(sentences)
-    punctuation_count = len([char for char in text if char in '.,;:?!'])
-    stopword_count = len([word for word in words if word in stopwords.words('english')])
-
-    lemmatizer = WordNetLemmatizer()
-    lemma_count = len(set(lemmatizer.lemmatize(word) for word in words))
-
-    named_entity_count = len([chunk for chunk in ne_chunk(pos_tag(words)) if isinstance(chunk, Tree)])
-
-    tagged_words = nltk.pos_tag(words)
-    pos_counts = nltk.FreqDist(tag for (word, tag) in tagged_words)
-    pos_features = {
-        'pos_IN': pos_counts['IN'],
-        'pos_DT': pos_counts['DT'],
-        'pos_NN': pos_counts['NN'],
-        'pos_,': pos_counts[','],
-        'pos_VBZ': pos_counts['VBZ'],
-        'pos_WDT': pos_counts['WDT'],
-        'pos_TO': pos_counts['TO'],
-        'pos_VB': pos_counts['VB'],
-        'pos_VBG': pos_counts['VBG'],
-        'pos_.': pos_counts['.'],
-        'pos_JJ': pos_counts['JJ'],
-        'pos_NNS': pos_counts['NNS'],
-        'pos_RB': pos_counts['RB'],
-        'pos_CC': pos_counts['CC'],
-        'pos_VBN': pos_counts['VBN'],
-    }
+    vectorizer = CountVectorizer(ngram_range=(1, 2))
 
-    features = {
-        'avg_word_length': avg_word_length,
-        'avg_sent_length': avg_sent_length,
-        'punctuation_count': punctuation_count,
-        'stopword_count': stopword_count,
-        'lemma_count': lemma_count,
-        'named_entity_count': named_entity_count,
-    }
-    features.update(pos_features)
+    # Convert the paragraphs into a matrix of token counts
+    X_vect = vectorizer.fit_transform(text)
 
-    return pd.Series(features)
+    # Get the feature names
+    feature_names = vectorizer.get_feature_names_out()
 
+    # Convert the matrix to a DataFrame
+    X_df = pd.DataFrame(X_vect.toarray(), columns=feature_names)
+
+
+    return pd.Series(X_df)
 
-# Creates a button named 'Press me'
-press_me_button = st.button("Which Model Used?")
 
 if press_me_button:
     input_features = df['paragraph'].apply(extract_features)
     predicted_llm = clf_loaded.predict(input_features)
-    #st.write(f"Predicted LLM: {predicted_llm[0]}")
+    st.write(f"Predicted LLM: {predicted_llm[0]}")
+
+    # predicted_proba = clf_loaded.predict_proba(input_features)
+    # probabilities = predicted_proba[0]
+    # labels = clf_loaded.classes_
+
+    # # Create a mapping from old labels to new labels
+    # label_mapping = {1: 'gpt3', 2: 'gpt4', 3: 'googlebard', 4: 'huggingface'}
+
+    # # Apply the mapping to the labels
+    # new_labels = [label_mapping[label] for label in labels]
+
+    # # Create a dictionary that maps new labels to probabilities
+    # prob_dict = {k: v for k, v in zip(new_labels, probabilities)}
+
+    # # Convert probabilities to percentages and sort the dictionary in descending order
+    # prob_dict = {k: f'{v*100:.2f}%' for k, v in sorted(prob_dict.items(), key=lambda item: item[1], reverse=True)}
+
+    # # Print the dictionary
+    # #st.write(prob_dict)
+
+    # # Create a progress bar and a bar chart for each LLM
+    # for llm, prob in prob_dict.items():
+    #     st.write(llm + ': ' + prob)
+    #     st.progress(float(prob.strip('%'))/100)
+
+
+
+#####################################################################
+
+# import streamlit as st
+# #subtitle 
+# st.markdown("version: 1.2")
+# #title 
+# st.title("Smart Detection System of AI-Generated Text Models")
+
+# #subtitle 
+# st.markdown("This is a POC for Smart Detection System of AI Generated Text Models project (:blue[MSc Data Analytics]), it is a pre-trained model that detect the probablities of using any of the known LLM (chatgpt3, chatgpt4, GoogleBard, HuggingfaceChat)")
+
+# import os
+# import requests
+# # import pickle
+# import pandas as pd
+# import nltk
+# import spacy
+# from nltk.corpus import stopwords
+# from nltk.tokenize import word_tokenize, sent_tokenize
+# import numpy as np
+# ############
+# from nltk.stem import WordNetLemmatizer
+# from nltk import ne_chunk, pos_tag, word_tokenize
+# from nltk.tree import Tree
+# from joblib import dump, load
+# nltk.download('wordnet')
+# nltk.download('maxent_ne_chunker')
+# nltk.download('words')
+
+# #######
+# nltk.download('punkt')
+# nltk.download('stopwords')
+# nltk.download('averaged_perceptron_tagger')
+
+# # Check if the file exists
+# if not os.path.isfile('RandomForestClassifier.joblib'):
+
+#     url = 'https://jaifar.net/RandomForestClassifier.joblib'
+#     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',
+#     }
+
+#     response = requests.get(url, headers=headers)
+
+
+#     with open('RandomForestClassifier.joblib', 'wb') as file:
+#         file.write(response.content)
+
+
+# # Load the model from the file
+# clf_loaded = load('RandomForestClassifier.joblib')
+
+# # # At this point, the pickle file should exist, either it was already there, or it has been downloaded and extracted.
+# # with open('RandomForestClassifier.pkl', 'rb') as file:
+# #     clf_loaded = pickle.load(file)
+
+# input_paragraph = st.text_area("Input your text here")
+# words_counts = word_tokenize(input_paragraph)
+# final_words = len(words_counts)
+# st.write('Words counts: ', final_words)
+
+
+
+# # df = pd.DataFrame(columns=["paragraph"])
+# # df = df.append({"paragraph": input_paragraph}, ignore_index=True)
+
+# df = pd.DataFrame([input_paragraph], columns=["paragraph"])
+
+
+
+# # Variable to control number of words to retrieve
+# num_words = 500
+
+# # Retrieving only the first num_words words of the paragraph
+# input_paragraph = ' '.join(word_tokenize(input_paragraph)[:num_words])
+
+# # Extracting features
+# def extract_features(text):
+#     words = word_tokenize(text)
+#     sentences = sent_tokenize(text)
+
+#     avg_word_length = sum(len(word) for word in words if word.isalpha()) / len(words)
+#     avg_sent_length = sum(len(sent) for sent in sentences) / len(sentences)
+#     punctuation_count = len([char for char in text if char in '.,;:?!'])
+#     stopword_count = len([word for word in words if word in stopwords.words('english')])
+
+#     lemmatizer = WordNetLemmatizer()
+#     lemma_count = len(set(lemmatizer.lemmatize(word) for word in words))
+
+#     named_entity_count = len([chunk for chunk in ne_chunk(pos_tag(words)) if isinstance(chunk, Tree)])
+
+#     tagged_words = nltk.pos_tag(words)
+#     pos_counts = nltk.FreqDist(tag for (word, tag) in tagged_words)
+#     pos_features = {
+#         'pos_IN': pos_counts['IN'],
+#         'pos_DT': pos_counts['DT'],
+#         'pos_NN': pos_counts['NN'],
+#         'pos_,': pos_counts[','],
+#         'pos_VBZ': pos_counts['VBZ'],
+#         'pos_WDT': pos_counts['WDT'],
+#         'pos_TO': pos_counts['TO'],
+#         'pos_VB': pos_counts['VB'],
+#         'pos_VBG': pos_counts['VBG'],
+#         'pos_.': pos_counts['.'],
+#         'pos_JJ': pos_counts['JJ'],
+#         'pos_NNS': pos_counts['NNS'],
+#         'pos_RB': pos_counts['RB'],
+#         'pos_CC': pos_counts['CC'],
+#         'pos_VBN': pos_counts['VBN'],
+#     }
+
+#     features = {
+#         'avg_word_length': avg_word_length,
+#         'avg_sent_length': avg_sent_length,
+#         'punctuation_count': punctuation_count,
+#         'stopword_count': stopword_count,
+#         'lemma_count': lemma_count,
+#         'named_entity_count': named_entity_count,
+#     }
+#     features.update(pos_features)
+
+#     return pd.Series(features)
+
+
+# # Creates a button named 'Press me'
+# press_me_button = st.button("Which Model Used?")
+
+# if press_me_button:
+#     input_features = df['paragraph'].apply(extract_features)
+#     predicted_llm = clf_loaded.predict(input_features)
+#     #st.write(f"Predicted LLM: {predicted_llm[0]}")
+
+#     predicted_proba = clf_loaded.predict_proba(input_features)
+#     probabilities = predicted_proba[0]
+#     labels = clf_loaded.classes_
+
+#     # Create a mapping from old labels to new labels
+#     label_mapping = {1: 'gpt3', 2: 'gpt4', 3: 'googlebard', 4: 'huggingface'}
 
-    predicted_proba = clf_loaded.predict_proba(input_features)
-    probabilities = predicted_proba[0]
-    labels = clf_loaded.classes_
+#     # Apply the mapping to the labels
+#     new_labels = [label_mapping[label] for label in labels]
 
-    # Create a mapping from old labels to new labels
-    label_mapping = {1: 'gpt3', 2: 'gpt4', 3: 'googlebard', 4: 'huggingface'}
+#     # Create a dictionary that maps new labels to probabilities
+#     prob_dict = {k: v for k, v in zip(new_labels, probabilities)}
 
-    # Apply the mapping to the labels
-    new_labels = [label_mapping[label] for label in labels]
+#     # Convert probabilities to percentages and sort the dictionary in descending order
+#     prob_dict = {k: f'{v*100:.2f}%' for k, v in sorted(prob_dict.items(), key=lambda item: item[1], reverse=True)}
 
-    # Create a dictionary that maps new labels to probabilities
-    prob_dict = {k: v for k, v in zip(new_labels, probabilities)}
+#     # Print the dictionary
+#     #st.write(prob_dict)
 
-    # Convert probabilities to percentages and sort the dictionary in descending order
-    prob_dict = {k: f'{v*100:.2f}%' for k, v in sorted(prob_dict.items(), key=lambda item: item[1], reverse=True)}
+#     # Create a progress bar and a bar chart for each LLM
+#     for llm, prob in prob_dict.items():
+#         st.write(llm + ': ' + prob)
+#         st.progress(float(prob.strip('%'))/100)
 
-    # Print the dictionary
-    #st.write(prob_dict)
+############################################################
 
-    # Create a progress bar and a bar chart for each LLM
-    for llm, prob in prob_dict.items():
-        st.write(llm + ': ' + prob)
-        st.progress(float(prob.strip('%'))/100)
 
 # import streamlit as st
 # import os