Create app.py

app.py

@@ -0,0 +1,75 @@
+import streamlit as st
+from transformers import pipeline
+
+# Cache the summarizer to avoid reloading on every interaction
+@st.cache_resource
+def load_summarizer():
+    return pipeline("summarization", model="facebook/bart-large-cnn")
+
+summarizer = load_summarizer()
+
+# Streamlit UI setup
+st.title("📝 Text Summarization App")
+
+# User input box (allows large input text)
+user_input = st.text_area("Enter text to summarize:", "", height=300)
+
+# Custom CSS to position the slider in the top-right corner
+st.markdown("""
+    <style>
+    .slider-container {
+        position: fixed;
+        top: 10px;
+        right: 10px;
+        z-index: 1000;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+# Slider for adjusting summary length in the top-right corner
+with st.container():
+    st.markdown('<div class="slider-container">', unsafe_allow_html=True)
+    summary_length = st.slider(
+        "Adjust Summary Length",
+        min_value=1,
+        max_value=3,
+        value=2,
+        step=1,
+        format="Summary Length: %d"
+    )
+    st.markdown('</div>', unsafe_allow_html=True)
+
+# Set min and max summary length based on the selected slider value
+if summary_length == 1:  # Short
+    min_len = 10
+    max_len = 50
+elif summary_length == 2:  # Medium
+    min_len = 50
+    max_len = 150
+else:  # Long
+    min_len = 150
+    max_len = 300
+
+# Display the selected range for feedback
+st.write(f"Selected Summary Length: {'Short' if summary_length == 1 else 'Medium' if summary_length == 2 else 'Long'}")
+
+# Button to trigger summarization
+if st.button("Summarize"):
+    if user_input.strip():  # Ensure there's input before summarizing
+        try:
+            # Generate summary using the model with custom min and max length
+            summarized_text = summarizer(user_input, 
+                                         max_length=max_len, 
+                                         min_length=min_len, 
+                                         length_penalty=2.0, 
+                                         num_beams=4, 
+                                         early_stopping=True)[0]['summary_text']
+
+            # Display the generated summary
+            st.subheader("Summarized Text:")
+            st.write(summarized_text)
+
+        except Exception as e:
+            st.error(f"An error occurred while summarizing: {e}")
+    else:
+        st.warning("Please enter some text to summarize.")