AI_vs_AI_RandomForest_88_Samples.pkl

app.py

@@ -44,9 +44,9 @@ selected_option = st.selectbox('Select an Option', options)
 
 
 # Check if the file exists
-if not os.path.isfile('RandomForestClassifier.pkl'):
+if not os.path.isfile('AI_vs_AI_RandomForest_88_Samples.pkl'):
     # Download the zip file if it doesn't exist
-    url = 'https://jaifar.net/RandomForestClassifier.pkl'
+    url = 'https://jaifar.net/AI_vs_AI_RandomForest_88_Samples.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',
     }
@@ -54,7 +54,7 @@ if not os.path.isfile('RandomForestClassifier.pkl'):
     response = requests.get(url, headers=headers)
 
     # Save the file
-    with open('RandomForestClassifier.pkl', 'wb') as file:
+    with open('AI_vs_AI_RandomForest_88_Samples.pkl', 'wb') as file:
         file.write(response.content)
 
 
@@ -88,6 +88,52 @@ num_words = 500
 input_paragraph = ' '.join(word_tokenize(input_paragraph)[:num_words])
 
 
+# Extracting features
+def extract_features_AI_vs_AI_RandomForest_88_Samples(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)
 
 
 
@@ -157,14 +203,14 @@ def add_vectorized_features(df):
     return df
 
 
-# Function define 
+# Function define AI_vs_AI_RandomForest_88_Samples
 def AI_vs_AI_RandomForest_88_Samples(df):
     
     # 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:
+    with open('AI_vs_AI_RandomForest_88_Samples.pkl', 'rb') as file:
         clf_loaded = pickle.load(file)
     
-    input_features = df['paragraph'].apply(extract_features)
+    input_features = df['paragraph'].apply(extract_features_AI_vs_AI_RandomForest_88_Samples)
 
     predicted_llm = clf_loaded.predict(input_features)
     st.write(f"Predicted LLM: {predicted_llm[0]}")