Update app.py

app.py

@@ -3,11 +3,12 @@ import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
-from wordlist_generator import generate_wordlist  # Assuming your generate_wordlist function is imported here
+from wordlist_generator import generate_wordlist  # A mock-up function for your project
 from dotenv import load_dotenv
 import os
 
-load_dotenv()  # Load environment variables from .env file
+# Load environment variables from .env file
+load_dotenv()
 access_token = os.getenv("HUGGINGFACE_ACCESS_TOKEN")
 
 # Page configuration
@@ -41,68 +42,72 @@ include_numbers = st.sidebar.checkbox("Include Numbers", value=True)
 # Display wordlist generation results
 st.header("Generated Wordlist Preview")
 
-# Call to the actual logic for wordlist generation
-wordlist = generate_wordlist(
-    size=wordlist_size, 
-    min_length=min_length, 
-    max_length=max_length, 
-    special_chars=include_special_chars, 
-    numbers=include_numbers
-)
-
-# Display the first 20 items in the wordlist
-st.write(f"Preview of {wordlist_size} words:")
-st.write(wordlist[:20])  # Show the first 20 words for brevity
-
-# Download link for the full wordlist
-st.markdown("### Download Full Wordlist")
-csv_data = pd.Series(wordlist).to_csv(index=False).encode()
-st.download_button(
-    label="Download Wordlist as CSV",
-    data=csv_data,
-    file_name="reconninja_wordlist.csv",
-    mime="text/csv"
-)
+# Handle the generation process
+try:
+    # Call to a mock-up function for wordlist generation (you will replace this with your actual logic)
+    wordlist = generate_wordlist(
+        size=wordlist_size, 
+        min_length=min_length, 
+        max_length=max_length, 
+        special_chars=include_special_chars, 
+        numbers=include_numbers
+    )
+    
+    # Show the first 20 items in the wordlist
+    st.write(f"Preview of {wordlist_size} words:")
+    st.dataframe(pd.DataFrame(wordlist[:20], columns=["Generated Words"]))  # Display as a table for better interaction
+    
+    # Provide a download link for the full wordlist
+    st.markdown("### Download Full Wordlist")
+    csv_data = pd.Series(wordlist).to_csv(index=False).encode()
+    st.download_button(
+        label="Download Wordlist as CSV",
+        data=csv_data,
+        file_name="reconninja_wordlist.csv",
+        mime="text/csv"
+    )
+
+except Exception as e:
+    st.error(f"Error generating wordlist: {e}")
 
 # Visualize wordlist statistics (for example, word length distribution)
-st.header("Wordlist Statistics")
-word_lengths = [len(word) for word in wordlist]
-word_length_df = pd.DataFrame(word_lengths, columns=["Word Length"])
-
-# Create a histogram to show the distribution of word lengths
-fig, ax = plt.subplots(figsize=(8, 6))
-sns.histplot(word_length_df["Word Length"], kde=True, bins=20, ax=ax)
-ax.set_title("Word Length Distribution")
-ax.set_xlabel("Word Length")
-ax.set_ylabel("Frequency")
-st.pyplot(fig)
+if wordlist:
+    st.header("Wordlist Statistics")
+    
+    # Calculate the word lengths
+    word_lengths = [len(word) for word in wordlist]
+    word_length_df = pd.DataFrame(word_lengths, columns=["Word Length"])
+
+    # Create a histogram to show the distribution of word lengths
+    fig, ax = plt.subplots(figsize=(8, 6))
+    sns.histplot(word_length_df["Word Length"], kde=True, bins=20, ax=ax)
+    ax.set_title("Word Length Distribution")
+    ax.set_xlabel("Word Length")
+    ax.set_ylabel("Frequency")
+    st.pyplot(fig)
 
 # Advanced Feature - Analyzing Wordlist Security
-st.header("Analyze Wordlist Security")
-
-# Slider for password entropy calculation
-entropy_slider = st.slider(
-    "Select Entropy Multiplier", 
-    min_value=1.0, 
-    max_value=10.0, 
-    value=3.0, 
-    step=0.1
-)
-
-# Simulate password entropy calculation (simple calculation for demonstration)
-entropy = np.log2(len(wordlist) ** entropy_slider)
-st.write(f"Estimated Entropy: {entropy:.2f} bits")
-
-# Showcase a mock security analysis (this would be expanded in your actual app)
-if entropy < 50:
-    st.warning("Low entropy detected! This wordlist might be vulnerable to brute-force attacks.")
-else:
-    st.success("Good entropy! This wordlist is secure against most brute-force attempts.")
-
-# Additional Analysis (Average Word Length)
-st.header("Additional Analysis: Average Word Length")
-avg_word_length = np.mean(word_lengths)
-st.write(f"Average Word Length: {avg_word_length:.2f} characters")
+if wordlist:
+    st.header("Analyze Wordlist Security")
+    
+    # Slider for password entropy calculation
+    entropy_slider = st.slider(
+        "Select Entropy Multiplier", 
+        min_value=1.0, 
+        max_value=10.0, 
+        value=3.0, 
+        step=0.1
+    )
+    
+    # Simulate password entropy calculation (simple calculation for demonstration)
+    entropy = np.log2(len(wordlist) ** entropy_slider)
+    st.write(f"Estimated Entropy: {entropy:.2f} bits")
+
+    # Showcase a mock security analysis (this would be expanded in your actual app)
+    if entropy < 50:
+        st.warning("Low entropy detected! This wordlist might be vulnerable to brute-force attacks.")
+    else:
+        st.success("Good entropy! This wordlist is secure against most brute-force attempts.")
 
 # Footer
 st.markdown("---")