tr

app.py

@@ -23,7 +23,7 @@ nltk.download('stopwords')
 nltk.download('averaged_perceptron_tagger')
 
 #version
-st.markdown("v1.888")
+st.markdown("v1.88")
 
 
 # URL of the text file
@@ -218,56 +218,57 @@ def add_vectorized_features(df):
 
 # Function define AI_vs_AI_RandomForest_88_Samples
 def AI_vs_AI_RandomForest_88_Samples(df):
-    try:
-        # Check if the file exists
-        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/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',
-            }
-            
-            response = requests.get(url, headers=headers)
-            if response.status_code != 200:
-                raise Exception("Failed to download the file")
-            
-            # Save the file
-            with open('AI_vs_AI_RandomForest_88_Samples.pkl', 'wb') as file:
-                file.write(response.content)
+    
+    # Check if the file exists
+    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/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',
+        }
+
+        response = requests.get(url, headers=headers)
+
+        # Save the file
+        with open('AI_vs_AI_RandomForest_88_Samples.pkl', 'wb') as file:
+            file.write(response.content)
 
         # At this point, the pickle file should exist, either it was already there, or it has been downloaded and extracted.
         with open('AI_vs_AI_RandomForest_88_Samples.pkl', 'rb') as file:
             clf_loaded = pickle.load(file)
 
-        input_features = df['paragraph'].apply(extract_features_AI_vs_AI_RandomForest_88_Samples)
+    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]}")
+    predicted_llm = clf_loaded.predict(input_features)
+    st.write(f"Predicted LLM: {predicted_llm[0]}")
 
+    try:
         predicted_proba = clf_loaded.predict_proba(input_features)
-        probabilities = predicted_proba[0]
-        labels = clf_loaded.classes_
+    except Exception as e:
+        st.write(f"An error occurred: {str(e)}")
+        
+    labels = clf_loaded.classes_
 
-        # Create a mapping from old labels to new labels
-        label_mapping = {1: 'gpt3', 2: 'gpt4', 3: 'googlebard', 4: 'huggingface'}
+    # 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]
+    # 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)}
+    # 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)}
+    # 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)
-        return 
-    except Exception as e:
-        st.write(f"An error occurred: {str(e)}")
+    # 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)
+    return 
 
 def AI_vs_AI_Ridge_2000_Samples(df):