Update app.py

app.py

@@ -1,46 +1,34 @@
 import streamlit as st
 from transformers import pipeline
-import os
-
-# 1. Silencing TensorFlow & Streamlit startup "noise"
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 
 
+# 1. Config
 MODEL_ID = "VoltIC/Automated-Text-Summarizer"
 
-@st.cache_resource
-def load_summarizer():
-    # Adding subfolder ensures we target the right directory
-    return pipeline(
-        "summarization", 
-        model=MODEL_ID, 
-        subfolder="summarizer_model", 
-        framework="pt"
-    )
+# 2. Page Config (Must be first)
+st.set_page_config(page_title="AI Summarizer", layout="wide")
 
-# 2. Page Configuration
-st.set_page_config(page_title="AI Summarizer", page_icon="📝")
+# 3. Cached Loader
+@st.cache_resource
+def load_model():
+    # We use the subfolder since that's where your files are
+    return pipeline("summarization", model=MODEL_ID, subfolder="summarizer_model", framework="pt")
 
-st.title("📝 AI Text Summarizer")
+st.title("📝 Automated Text Summarizer")
 
-# 3. Handling the "Bare Mode" Startup
-# This check ensures the UI only tries to render when the environment is ready
+# 4. Error-Resistant Loading
 try:
-    with st.status("🚀 Initializing AI Engine...", expanded=True) as status:
-        st.write("Downloading model weights (1.6GB)...")
-        summarizer = load_summarizer()
-        status.update(label="✅ AI Engine Ready!", state="complete", expanded=False)
+    with st.spinner("Downloading AI Model (1.6GB)... This may take 3-5 minutes on first run."):
+        summarizer = load_model()
+    st.success("AI Model Loaded!")
 except Exception as e:
-    st.info("The app is currently warming up. Please refresh in 1 minute.")
+    st.warning("The model is still downloading in the background. Please wait 2 minutes and refresh.")
     st.stop()
 
-# 4. Main App Interface
-input_text = st.text_area("Paste your article or document below:", height=300)
-
+# 5. UI
+input_text = st.text_area("Paste text here:", height=250)
 if st.button("Summarize"):
-    if input_text.strip():
-        with st.spinner("Processing..."):
-            summary = summarizer(input_text, max_length=100, min_length=30)
+    if input_text:
+        with st.spinner("Summarizing..."):
+            output = summarizer(input_text, max_length=100, min_length=30, do_sample=False)
             st.subheader("Summary")
-            st.write(summary[0]['summary_text'])
-    else:
-        st.warning("Please provide some text to summarize.")
+            st.write(output[0]['summary_text'])