Delete app.py

app.py

@@ -1,46 +0,0 @@
-import gradio as gr
-import fitz
-import torch
-from transformers import pipeline
-import time, io
-
-device = 0 if torch.cuda.is_available() else -1
-if device == -1: raise RuntimeError("GPU required for 5–10s target")
-
-summarizer = pipeline("summarization", model="google/pegasus-xsum", device=device, torch_dtype=torch.int8)
-
-def extract_text(file_bytes):
-    if file_bytes[:4].startswith(b'%PDF'):
-        doc = fitz.open(stream=file_bytes, filetype="pdf")
-        text = "".join(page.get_text("text", flags=16) for page in doc)
-        doc.close()
-        return text
-    try: return file_bytes.decode("utf-8")
-    except: return "❌ Unsupported format (PDF/TXT only)"
-
-async def summarize_file(file_bytes):
-    start = time.time()
-    text = extract_text(file_bytes)[:300000] or "❌ No text found"
-    if len(text.strip()) == 0: return text
-    chunks = [text[i:i+15000] for i in range(0, len(text), 15000)]
-    if not chunks: return "❌ No chunks to summarize"
-    summaries = []
-    for i in range(0, len(chunks), 10):
-        if time.time() - start > 7: 
-            summaries.append("⚠️ Stopped early")
-            break
-        batch = chunks[i:i+10]
-        try:
-            batch_summaries = summarizer(batch, max_length=40, min_length=10, do_sample=False, batch_size=10)
-            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")
-    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 ~5s (GPU)"
-)
-
-if __name__ == "__main__":
-    demo.launch(share=False)