Update app.py

app.py

@@ -1,16 +1,12 @@
 import streamlit as st
 from transformers import pipeline
 
-# 1. Your Model Configuration
+# 1. Configuration
 MODEL_ID = "VoltIC/Automated-Text-Summarizer"
 
 @st.cache_resource
 def load_summarizer():
-    """
-    Loads the summarization pipeline from the Hugging Face Hub.
-    Using 'subfolder' because the model files are nested in 'summarizer_model'.
-    Using 'framework="pt"' to ensure PyTorch is used (avoids Keras 3 errors).
-    """
+    # Use subfolder because your files are in 'summarizer_model'
     return pipeline(
         "summarization", 
         model=MODEL_ID, 
@@ -18,46 +14,35 @@ def load_summarizer():
         framework="pt"
     )
 
-# 2. UI Setup
-st.set_page_config(page_title="AI TL;DR Tool", page_icon="📝")
+# 2. Page Config (MUST be the first Streamlit command)
+st.set_page_config(page_title="AI Summarizer", page_icon="📝")
+
+# 3. App Title & UI
 st.title("📝 Automated Text Summarizer")
-st.markdown("Generate a concise 2-3 sentence summary of any long article or legal document.")
+st.write("The model is loading. Please wait a moment...")
 
-# Initialize the model
+# Load the model
 try:
     summarizer = load_summarizer()
-    st.success("✅ Model loaded successfully!")
+    st.success("Model Ready!")
 except Exception as e:
-    st.error(f"❌ Error loading model: {e}")
+    st.error(f"Waiting for model files... {e}")
     st.stop()
 
-# 3. Input Area
-text_input = st.text_area("Paste your text here (News, Research, Legal, etc.):", height=300)
+# 4. Input and Logic
+text_input = st.text_area("Paste your article here:", height=300)
 
-# 4. Summarization Logic
-if st.button("Generate Summary"):
-    if len(text_input.strip()) < 50:
-        st.warning("Please enter a longer text (at least 50 characters) to summarize.")
+if st.button("Summarize"):
+    if text_input.strip():
+        with st.spinner("Summarizing..."):
+            # length_penalty helps keep the summary concise (2-3 sentences)
+            result = summarizer(
+                text_input, 
+                max_length=100, 
+                min_length=30, 
+                do_sample=False
+            )
+            st.subheader("Summary")
+            st.write(result[0]['summary_text'])
     else:
-        with st.spinner("🧠 AI is reading and condensing..."):
-            try:
-                # Setting max_length to ~80-100 usually results in 2-3 sentences
-                summary = summarizer(
-                    text_input, 
-                    max_length=100, 
-                    min_length=30, 
-                    do_sample=False
-                )
-                
-                st.divider()
-                st.subheader("Final Summary")
-                st.write(summary[0]['summary_text'])
-                
-                # Optional: Add a copy button for the user
-                st.button("Copy to Clipboard", on_click=lambda: st.write("Copying functionality requires a custom component, but you can highlight and copy above!"))
-                
-            except Exception as e:
-                st.error(f"An error occurred during summarization: {e}")
-
-st.divider()
-st.caption("Powered by BART-Large-CNN via Hugging Face Transformers.")
+        st.warning("Please enter some text.")