Update app.py

app.py

@@ -2,26 +2,38 @@ from transformers import BartForConditionalGeneration, BartTokenizer
 import gradio as gr
 import torch
 
+# ── 1. Load Model ──────────────────────────────────────────────────
 print("Loading model...")
-MODEL_NAME = "facebook/bart-large-cnn"
+
+MODEL_NAME = "sshleifer/distilbart-cnn-12-6"  # lightweight, works on free tier
 tokenizer  = BartTokenizer.from_pretrained(MODEL_NAME)
 model      = BartForConditionalGeneration.from_pretrained(MODEL_NAME)
-device     = "cuda" if torch.cuda.is_available() else "cpu"
+device     = "cpu"   # free HF Spaces has no GPU
 model      = model.to(device)
-print(f"Ready ✅ on {device}")
 
+print("Model ready ✅")
+
+# ── 2. Summarization Function ──────────────────────────────────────
 def summarize_text(user_input, history, max_len, min_len, bullet_mode):
     user_input = user_input.strip()
 
+    # Empty input
     if not user_input:
-        history.append({"role": "assistant", "content": "⚠️ Please enter some text."})
+        history.append({"role": "assistant", "content": "⚠️ Please enter some text to summarize."})
         return history, history
 
+    # Short input — treat as greeting
     if len(user_input.split()) < 30:
         history.append({"role": "user",      "content": user_input})
-        history.append({"role": "assistant", "content": "👋 Please paste longer text (30+ words) to summarize."})
+        history.append({"role": "assistant", "content": (
+            "👋 Hello! I'm your **Text Summarizer**.\n\n"
+            "Paste any long article, paragraph, or document (30+ words) "
+            "and I'll summarize it instantly.\n\n"
+            "Use the ⚙️ settings on the right to adjust length and format."
+        )})
         return history, history
 
+    # Summarize
     try:
         inputs = tokenizer(
             user_input,
@@ -30,7 +42,7 @@ def summarize_text(user_input, history, max_len, min_len, bullet_mode):
             truncation=True
         ).to(device)
 
-        ids = model.generate(
+        summary_ids = model.generate(
             inputs["input_ids"],
             max_new_tokens=int(max_len),
             min_new_tokens=int(min_len),
@@ -40,18 +52,29 @@ def summarize_text(user_input, history, max_len, min_len, bullet_mode):
             no_repeat_ngram_size=3
         )
 
-        summary = tokenizer.decode(ids[0], skip_special_tokens=True)
+        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
 
+        # Format output
         if bullet_mode:
             sentences = summary.replace("?", ".").replace("!", ".").split(". ")
-            body = "\n".join(f"• {s.strip().capitalize()}" for s in sentences if s.strip())
-            out  = f"📌 **Summary (Bullets)**:\n\n{body}"
+            bullets   = "\n".join(
+                f"• {s.strip().capitalize()}"
+                for s in sentences if s.strip()
+            )
+            out = f"📌 **Summary (Bullet Points)**:\n\n{bullets}"
         else:
             out = f"📌 **Summary**:\n\n{summary}"
 
-        orig = len(user_input.split())
-        summ = len(summary.split())
-        out += f"\n\n---\n📊 *{orig} words → {summ} words | Reduced by {round((1 - summ/orig)*100, 1)}%*"
+        # Word count stats
+        orig_words = len(user_input.split())
+        summ_words = len(summary.split())
+        reduction  = round((1 - summ_words / orig_words) * 100, 1)
+        out += (
+            f"\n\n---\n"
+            f"📊 *Original: {orig_words} words → "
+            f"Summary: {summ_words} words | "
+            f"Reduced by {reduction}%*"
+        )
 
         label = user_input[:80] + "..." if len(user_input) > 80 else user_input
         history.append({"role": "user",      "content": label})
@@ -59,7 +82,7 @@ def summarize_text(user_input, history, max_len, min_len, bullet_mode):
 
     except Exception as e:
         history.append({"role": "user",      "content": user_input[:60] + "..."})
-        history.append({"role": "assistant", "content": f"❌ Error: {e}"})
+        history.append({"role": "assistant", "content": f"❌ Error: {str(e)}"})
 
     return history, history
 
@@ -68,40 +91,81 @@ def clear_chat():
     return [], []
 
 
+# ── 3. Gradio UI ───────────────────────────────────────────────────
 with gr.Blocks(title="Text Summarizer", theme=gr.themes.Soft()) as demo:
 
-    gr.Markdown("# 📝 Text Summarization Chatbox\n### Powered by `facebook/bart-large-cnn`")
+    gr.Markdown("""
+    # 📝 Text Summarization Chatbox
+    ### Powered by `sshleifer/distilbart-cnn-12-6`
+    Paste any long text and get an instant summary!
+    """)
 
     with gr.Row():
+
+        # Left — Chat
         with gr.Column(scale=7):
-            chatbot = gr.Chatbot(height=450, type="messages")  # ✅ Gradio 6.x format
-            state   = gr.State([])
+            chatbot = gr.Chatbot(
+                height=450,
+                bubble_full_width=False,
+                type="messages"       # ✅ required for Gradio 6.x
+            )
+            state = gr.State([])
 
             with gr.Row():
-                txt   = gr.Textbox(
-                    placeholder="Paste text here...",
-                    lines=3,
+                txt_input = gr.Textbox(
+                    placeholder="Paste your article, report, or any long text here...",
                     show_label=False,
+                    lines=3,
                     scale=8
                 )
                 with gr.Column(scale=2):
-                    btn   = gr.Button("✨ Summarize", variant="primary")
-                    clear = gr.Button("🗑️ Clear",    variant="secondary")
+                    submit_btn = gr.Button("✨ Summarize", variant="primary")
+                    clear_btn  = gr.Button("🗑️ Clear",    variant="secondary")
 
+        # Right — Settings
         with gr.Column(scale=3):
             gr.Markdown("### ⚙️ Settings")
-            max_l  = gr.Slider(50,  500, value=150, step=10, label="Max Length")
-            min_l  = gr.Slider(10,  200, value=40,  step=5,  label="Min Length")
-            bullet = gr.Checkbox(label="🔵 Bullet Point Mode", value=False)
+
+            max_length = gr.Slider(
+                minimum=50, maximum=300,
+                value=130, step=10,
+                label="Max Summary Length (tokens)"
+            )
+            min_length = gr.Slider(
+                minimum=10, maximum=100,
+                value=30, step=5,
+                label="Min Summary Length (tokens)"
+            )
+            bullet_mode = gr.Checkbox(
+                label="🔵 Bullet Point Mode",
+                value=False
+            )
+
             gr.Markdown("""
             ---
             **💡 Tips:**
-            - Works best with 100–1000 word inputs
-            - Toggle Bullet Mode for point-wise output
-            """)
+            - Works best with **100–1000 word** inputs
+            - Articles, news, reports, essays
+            - Toggle **Bullet Mode** for point-wise output
+            - Adjust sliders to control summary length
 
-    btn.click(summarize_text,  inputs=[txt, state, max_l, min_l, bullet], outputs=[chatbot, state])
-    txt.submit(summarize_text, inputs=[txt, state, max_l, min_l, bullet], outputs=[chatbot, state])
-    clear.click(clear_chat, outputs=[chatbot, state])
+            ---
+            **🔄 Model:** `distilbart-cnn-12-6`
+            **🖥️ Device:** CPU
+            """)
 
-demo.launch()
+    # Events
+    submit_btn.click(
+        summarize_text,
+        inputs=[txt_input, state, max_length, min_length, bullet_mode],
+        outputs=[chatbot, state]
+    )
+    txt_input.submit(
+        summarize_text,
+        inputs=[txt_input, state, max_length, min_length, bullet_mode],
+        outputs=[chatbot, state]
+    )
+    clear_btn.click(clear_chat, outputs=[chatbot, state])
+
+# ── 4. Launch ──────────────────────────────────────────────────────
+demo.launch(server_name="0.0.0.0", server_port=7860)