Create app.py

app.py

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+import streamlit as st
+import transformers
+from transformers import pipeline
+
+# Set up model paths (you can later replace these with fine-tuned model folders)
+model_map = {
+    "BART": "pavuluriashwitha/Bart_model",
+    "T5": "t5-small",
+    "PEGASUS": "google/pegasus-cnn_dailymail"
+}
+
+# App Title
+st.markdown("<h1 style='text-align: center;'>Text Summarization App</h1>", unsafe_allow_html=True)
+
+# UI: Mode and Length controls
+mode = st.radio("Modes", ["Paragraph", "Bullet Points", "Custom"], horizontal=True)
+length_slider = st.slider("Summary Length", 1, 2, 1, label_visibility="collapsed")
+length_label = "Short" if length_slider == 1 else "Long"
+st.markdown(f"Summary Length: **{length_label}**")
+
+# Model selection
+model_choice = st.selectbox("Choose Summarization Model", ["BART", "T5", "PEGASUS"])
+
+# 2-column layout
+col1, col2 = st.columns(2)
+
+# Left Column: Input
+with col1:
+    st.markdown("### Enter your text:")
+    user_input = st.text_area("", height=300, placeholder="Paste your job description or content here...")
+
+    # Word count
+    word_count = len(user_input.split())
+    st.markdown(f"**{word_count} words**")
+
+    # Summarize Button
+    if st.button("Summarize", use_container_width=True):
+        if not user_input.strip():
+            st.warning("Please enter text to summarize.")
+        else:
+            # Load model
+            summarizer = pipeline("summarization", model=model_map[model_choice])
+
+            # Set length dynamically
+            max_len = 150 if length_label == "Short" else 300
+            min_len = 40
+
+            # Generate summary
+            summary = summarizer(user_input, max_length=max_len, min_length=min_len, do_sample=False)[0]['summary_text']
+            st.session_state["summary"] = summary
+
+# Right Column: Output
+with col2:
+    st.markdown("### Summary")
+    if "summary" in st.session_state:
+        st.success(st.session_state["summary"])
+        summary_words = len(st.session_state["summary"].split())
+        st.markdown(f"📝 1 sentence • {summary_words} words")
+        st.button("Paraphrase Summary")
+        st.download_button("📥 Download Summary", st.session_state["summary"], file_name="summary.txt")
+    else:
+        st.info("Your summary will appear here.")