Update app.py

app.py

@@ -1,6 +1,11 @@
 import streamlit as st
 from transformers import pipeline
 
+# Initialize pipelines (including summarization)
+ner = pipeline("ner")
+qa = pipeline("question-answering")
+text_gen = pipeline("text-generation")
+summarization = pipeline("summarization")  # Initialize summarization pipeline here
 
 def main():
   """
@@ -17,33 +22,15 @@ def main():
   # Select task from dropdown menu
   selected_task = st.selectbox("Choose NLP Task:", ["NER", "QA", "Text Generation", "Text Summarization"])
 
-  # Initialize pipelines (including summarization) before conditional statements
-  ner = pipeline("ner")
-  qa = pipeline("question-answering")
-  text_gen = pipeline("text-generation")
-  summarization = pipeline("summarization")
-
   # Perform NLP task based on selection
   if user_input and selected_task:
     if selected_task == "NER":
       analysis = ner(user_input)
-      st.write("**Named Entities:**")
-      for entity in analysis:
-          # Check for 'label' or 'group' key in entity
-          entity_group = entity.get('label', entity.get('group', 'NA'))
-          st.write(f"- {entity['word']} ({entity_group})")
+      # ... rest of NER code
     elif selected_task == "QA":
       # ... rest of QA code
-    elif selected_task == "Text Generation":  # Ensure proper indentation here
-      # Choose generation task from another dropdown
-      generation_task = st.selectbox("Choose Generation Task:", ["Text summarization (short)", "Poem", "Code"])
-      if generation_task == "Text summarization (short)":
-          analysis = summarization(user_input, max_length=50, truncation=True)
-      else:
-          # Experiment with different prompts and max_length for creative text generation
-          prompt = st.text_input("Enter Prompt (Optional):", "")
-          analysis = text_gen(prompt if prompt else user_input, max_length=50, truncation=True)
-      st.write("**Generated Text:**", analysis[0]['generated_text'])
+    elif selected_task == "Text Generation":
+      # ... rest of Text Generation code
     else:
       analysis = summarization(user_input, max_length=100, truncation=True)
       st.write("**Summary:**", analysis[0]['summary_text'])