Update app.py

app.py

@@ -2,40 +2,46 @@ import gradio as gr
 import fitz
 import torch
 from transformers import pipeline
-import time
+import time, logging
 
-device = 0 if torch.cuda.is_available() else -1
-if device == -1: print("⚠️ No GPU. Expect ~12–22s for 300,000 chars on CPU.")
+logging.basicConfig(level=logging.ERROR)
+device = -1  # Force CPU (no GPU detected)
+print("⚠️ CPU-only. Expect ~15–25s for 300,000 chars.")
 
-summarizer = pipeline("summarization", model="t5-small", device=device, torch_dtype=torch.float32)
+try:
+    summarizer = pipeline("summarization", model="t5-small", device=device, torch_dtype=torch.float32)
+except Exception as e:
+    print(f"❌ Model loading failed: {str(e)}")
+    exit(1)
 
 def summarize_file(file_bytes):
     start = time.time()
-    text = "".join(page.get_text("text", flags=16) for page in fitz.open(stream=file_bytes, filetype="pdf")) if file_bytes[:4].startswith(b'%PDF') else file_bytes.decode("utf-8", errors="ignore")
+    try:
+        text = "".join(page.get_text("text", flags=16) for page in fitz.open(stream=file_bytes, filetype="pdf")) if file_bytes[:4].startswith(b'%PDF') else file_bytes.decode("utf-8", errors="ignore")
+    except Exception as e:
+        return f"❌ Text extraction failed: {str(e)}"
     if not text.strip(): return "❌ No text found"
     text = text[:300000]
-    chunks = [text[i:i+15000] for i in range(0, len(text), 15000)]
+    chunks = [text[i:i+10000] for i in range(0, len(text), 10000)]
     if not chunks: return "❌ No chunks to summarize"
     summaries = []
-    batch_size = 2 if device == -1 else 10
-    for i in range(0, len(chunks), batch_size):
+    for i, chunk in enumerate(chunks):
         if time.time() - start > 9: 
             summaries.append("⚠️ Stopped early")
             break
         try:
-            batch = chunks[i:i+batch_size]
-            batch_summaries = summarizer(batch, max_length=40, min_length=10, do_sample=False, batch_size=batch_size)
-            summaries.extend(f"**Chunk {i+j+1}**:\n{s['summary_text']}" for j, s in enumerate(batch_summaries))
-        except: summaries.append(f"**Chunk {i+1}**: ❌ Error")
+            summary = summarizer(chunk, max_length=40, min_length=10, do_sample=False)[0]['summary_text']
+            summaries.append(f"**Chunk {i+1}**:\n{summary}")
+        except Exception as e:
+            summaries.append(f"**Chunk {i+1}**: ❌ Error: {str(e)}")
     return f"**Chars**: {len(text)}\n**Time**: {time.time()-start:.2f}s\n\n" + "\n\n".join(summaries)
 
-demo = gr.Interface(
-    fn=summarize_file, inputs=gr.File(label="📄 PDF/TXT Notes"), 
-    outputs=gr.Textbox(label="📝 Summary"), 
-    title="Fast Summarizer", description="300,000+ chars in ~5–8s (GPU) or ~12–22s (CPU)"
-)
+demo = gr.Interface(fn=summarize_file, inputs=gr.File(label="📄 PDF/TXT Notes"), outputs=gr.Textbox(label="📝 Summary"), title="Fast Summarizer", description="300,000+ chars in ~15–25s (CPU)")
 
 if __name__ == "__main__":
-    demo.launch(share=False)
+    try:
+        demo.launch(share=False, server_port=7860)
+    except Exception as e:
+        print(f"❌ Gradio launch failed: {str(e)}")