Update app.py

app.py

@@ -1,50 +1,62 @@
 import streamlit as st
 import transformers
-import keybert
 from transformers import pipeline
-from keybert import KeyBERT
 
-# Load summarizer and keyword model
-summarizer_bart = pipeline("summarization", model="facebook/bart-large-cnn")
-kw_model = KeyBERT()
+# Set up model paths (you can later replace these with fine-tuned model folders)
+model_map = {
+    "BART": "facebook/bart-large-cnn",
+    "T5": "t5-small",
+    "PEGASUS": "google/pegasus-cnn_dailymail"
+}
 
-# Title
+# App Title
 st.markdown("<h1 style='text-align: center;'>Text Summarization App</h1>", unsafe_allow_html=True)
 
-# Modes
+# 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}**")
 
-# Summary length
-length = st.slider("Summary Length", 1, 2, 1, label_visibility="collapsed")
-summary_type = "Short" if length == 1 else "Long"
-st.write(f"Summary Length: **{summary_type}**")
+# Model selection
+model_choice = st.selectbox("Choose Summarization Model", ["BART", "T5", "PEGASUS"])
 
-# Layout
+# 2-column layout
 col1, col2 = st.columns(2)
 
+# Left Column: Input
 with col1:
-    st.markdown("### Paste your text below:")
-    user_input = st.text_area("", height=300, placeholder="Paste job description or any paragraph...")
-    
-    if user_input.strip():
-        # Extract keywords using KeyBERT
-        keywords = [kw[0] for kw in kw_model.extract_keywords(user_input, top_n=5)]
-        selected_keywords = st.multiselect("Select Keywords", keywords, default=keywords)
-        st.markdown(f"**{len(user_input.split())} words**")
-
-        if st.button("Summarize", use_container_width=True):
-            summary = summarizer_bart(user_input, max_length=150 if summary_type == "Short" else 250,
-                                      min_length=40, do_sample=False)[0]['summary_text']
-            st.session_state['summary'] = summary
-    else:
-        st.warning("Please enter some text.")
-
+    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'])
-        st.markdown(f"1 sentence • {len(st.session_state['summary'].split())} words")
+    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")
+        st.download_button("📥 Download Summary", st.session_state["summary"], file_name="summary.txt")
     else:
-        st.info("Your summary will appear here after clicking **Summarize**.")
+        st.info("Your summary will appear here.")