Update app.py

app.py

@@ -92,9 +92,11 @@ def chatbot_response(history, user_input, text_input=None, file_input=None):
     
     return history, answer
 
-# Function to generate word cloud
-def generate_word_cloud(text):
-    wordcloud = WordCloud(width=800, height=400, background_color="white").generate(text)
+# Function to generate word cloud from the 'content' column (from output CSV)
+def generate_word_cloud_from_output(df):
+    # Assuming 'content' column is the first column after processing
+    content_text = " ".join(df["content"].dropna().astype(str).tolist())
+    wordcloud = WordCloud(width=800, height=400, background_color="white").generate(content_text)
     return wordcloud
 
 # Function to generate bar graph for decoded predictions
@@ -122,7 +124,7 @@ if st.button("🔍 Classify"):
         st.write(f"Confidence Level: {confidence}")
         
         # Generate word cloud for the cleaned text input
-        wordcloud = generate_word_cloud(text_input)
+        wordcloud = generate_word_cloud_from_output(pd.DataFrame({"content": [text_input]}))  # Create a DataFrame for single input
         st.image(wordcloud.to_array(), caption="Word Cloud for Text Input", use_container_width=True)
     else:
         st.warning("Please enter some text to classify.")
@@ -141,10 +143,9 @@ if file_input:
             mime="text/csv"
         )
         
-        # Generate word cloud for the cleaned CSV data
-        bulk_text = " ".join(df["Decoded Prediction"].dropna().astype(str).tolist())
-        wordcloud = generate_word_cloud(bulk_text)
-        st.image(wordcloud.to_array(), caption="Word Cloud for CSV Data", use_container_width=True)
+        # Generate word cloud for the 'content' column of the processed CSV data
+        wordcloud = generate_word_cloud_from_output(df)
+        st.image(wordcloud.to_array(), caption="Word Cloud for CSV Content", use_container_width=True)
         
         # Generate bar graph for decoded predictions frequency
         generate_bar_graph(df)