Update app.py

app.py

@@ -7,6 +7,8 @@ from nltk.corpus import stopwords
 from nltk.stem import WordNetLemmatizer
 from transformers import pipeline
 from PIL import Image
+import matplotlib.pyplot as plt
+from wordcloud import WordCloud
 
 # Download required NLTK data
 nltk.download('stopwords')
@@ -90,36 +92,23 @@ 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)
+    return wordcloud
+
+# Function to generate bar graph for decoded predictions
+def generate_bar_graph(df):
+    prediction_counts = df["Decoded Prediction"].value_counts()
+    fig, ax = plt.subplots(figsize=(10, 6))
+    prediction_counts.plot(kind='bar', ax=ax, color='skyblue')
+    ax.set_title('Frequency of Decoded Predictions', fontsize=16)
+    ax.set_xlabel('Category', fontsize=12)
+    ax.set_ylabel('Frequency', fontsize=12)
+    st.pyplot(fig)
+
 # Streamlit App Layout
 st.set_page_config(page_title="News Classifier", page_icon="📰")
-
-# Custom CSS for responsive design (desktop/mobile optimization)
-st.markdown(
-    """
-    <style>
-        @media only screen and (max-width: 600px) {
-            .stApp {
-                padding: 10px;
-            }
-        }
-
-        @media only screen and (min-width: 601px) {
-            .stApp {
-                padding: 20px;
-            }
-        }
-
-        .stButton {
-            margin-top: 20px;
-        }
-
-        .stTextInput {
-            width: 100%;
-        }
-    </style>
-    """, unsafe_allow_html=True
-)
-
 cover_image = Image.open("cover.png")  # Ensure this image exists
 st.image(cover_image, caption="News Classifier 📢", use_container_width=True)
 
@@ -131,6 +120,10 @@ if st.button("🔍 Classify"):
         category, confidence = classify_text(text_input)
         st.write(f"Predicted Category: {category}")
         st.write(f"Confidence Level: {confidence}")
+        
+        # Generate word cloud for the cleaned text input
+        wordcloud = generate_word_cloud(text_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.")
 
@@ -147,6 +140,14 @@ if file_input:
             file_name=output_file,
             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 bar graph for decoded predictions frequency
+        generate_bar_graph(df)
     else:
         st.error(f"Error processing file: {output_file}")
 
@@ -160,4 +161,4 @@ if st.button("✉ Send"):
     st.write("Chatbot Response:")
     for q, a in history:
         st.write(f"Q: {q}")
-        st.write(f"A: {a}")
+        st.write(f"A: {a}")