try/except

app.py

@@ -218,55 +218,56 @@ def add_vectorized_features(df):
 
 # Function define AI_vs_AI_RandomForest_88_Samples
 def AI_vs_AI_RandomForest_88_Samples(df):
-    
-    # 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)
+    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)
 
         # 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)
-
-    predicted_llm = clf_loaded.predict(input_features)
-    st.write(f"Predicted LLM: {predicted_llm[0]}")
+        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_proba = clf_loaded.predict_proba(input_features)
-    probabilities = predicted_proba[0]
-    labels = clf_loaded.classes_
+        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'}
+        # 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)}
 
-    # 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 
+    except Exception as e:
+        st.write(f"An error occurred: {str(e)}")
 
-    # 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):