Update app.py

app.py

@@ -3,16 +3,15 @@ from transformers import pipeline
 import torch
 import pdfplumber
 from docx import Document
-import io
 import os
 
-# Load model
+# Load faster model for CPU
 device = 0 if torch.cuda.is_available() else -1
 print(f"Using device: {'GPU' if device == 0 else 'CPU'}")
 
 summarizer = pipeline(
     "summarization",
-    model="sshleifer/distilbart-cnn-12-6",
+    model="Falconsai/text_summarization",  # Faster/smaller for quick tests
     device=device
 )
 
@@ -36,7 +35,9 @@ def extract_text(file_path):
     except Exception as e:
         return f"Error reading file: {str(e)}"
 
-def summarize(input_text, file_path, detail_level):
+def summarize(input_text, file_path, detail_level, progress=gr.Progress()):
+    progress(0, desc="Preparing text...")
+    
     if file_path is not None:
         text = extract_text(file_path)
     else:
@@ -52,16 +53,15 @@ def summarize(input_text, file_path, detail_level):
     target_ratio = detail_level
     target_length = int(words * target_ratio)
     
-    # Safeguards: cap lengths to prevent min > max
-    max_l = max(500, min(1400, target_length + 250))  # Hard cap at 1400 (model limit-ish)
-    min_l = max(100, int(target_length * 0.65))
+    # Lower caps for speed and to avoid warnings
+    max_l = max(200, min(512, target_length + 100))
+    min_l = max(50, int(target_length * 0.65))
     
-    # Force min_l < max_l if overflow
     if min_l >= max_l:
-        min_l = max_l - 100  # Reasonable fallback
-        if min_l < 100:
-            min_l = 100
+        min_l = max_l // 2
 
+    progress(0.4, desc="Summarizing... (10–60 sec for long text)")
+    
     try:
         result = summarizer(
             text,
@@ -73,21 +73,22 @@ def summarize(input_text, file_path, detail_level):
             do_sample=False,
             truncation=True
         )
+        progress(1.0, desc="Done!")
         return result[0]['summary_text']
     except Exception as e:
-        return f"Error during summarization: {str(e)}\n(Try shorter text, lower detail level, or paste instead of upload.)"
+        return f"Error: {str(e)}\n(Try shorter input or lower detail.)"
 
-# Interface
+# Interface with progress
 interface = gr.Interface(
     fn=summarize,
     inputs=[
-        gr.Textbox(lines=12, placeholder="Paste your lecture text here...", label="Lecture Text (Paste)"),
+        gr.Textbox(lines=12, placeholder="Paste lecture text...", label="Lecture Text (Paste)"),
         gr.File(file_types=[".pdf", ".docx", ".txt"], label="Upload Lecture File"),
-        gr.Slider(0.15, 0.60, value=0.32, step=0.01, label="Detail Level (higher = longer summary)")
+        gr.Slider(0.15, 0.60, value=0.32, step=0.01, label="Detail Level (higher = longer)")
     ],
     outputs=gr.Textbox(label="Generated Summary"),
     title="Lecture Summarizer",
-    description="Upload PDF/DOCX/TXT lecture or paste text. Adjust slider for detail. For very long files, use lower detail or chunk text.",
+    description="Paste or upload lecture. Progress shows during generation. For long files, lower detail or upgrade to GPU.",
     flagging_mode="never",
 )