Create app.py

app.py

@@ -0,0 +1,83 @@
+import gradio as gr
+import fitz
+import torch
+from transformers import pipeline
+import time, logging, re, pandas as pd, docx, pytesseract, openpyxl, textract, mimetypes
+from PIL import Image
+from io import BytesIO
+from striprtf.striprtf import rtf_to_text
+
+logging.basicConfig(level=logging.ERROR)
+device = -1  # CPU-only
+print("⚠️ CPU-only. Expect ~10–15s for 300,000 chars.")
+
+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):
+    start = time.time()
+    print(f"File: {file.name if hasattr(file, 'name') else 'unknown'}")
+    try:
+        file_bytes = file.read() if hasattr(file, 'read') else file
+        mime, _ = mimetypes.guess_type(file.name) if hasattr(file, 'name') else (None, None)
+        text = ""
+        if mime == 'application/pdf':
+            doc = fitz.open(stream=file_bytes, filetype="pdf")
+            text = "".join(page.get_text("text") for page in doc)
+        elif mime in ['text/plain', 'text/rtf']:
+            text = rtf_to_text(file_bytes.decode("utf-8", errors="ignore")) if mime == 'text/rtf' else file_bytes.decode("utf-8", errors="ignore")
+        elif mime in ['text/csv', 'application/vnd.ms-excel']:
+            text = " ".join(pd.read_csv(BytesIO(file_bytes)).astype(str).values.flatten())
+        elif mime == 'application/vnd.openxmlformats-officedocument.wordprocessingml.document':
+            doc = docx.Document(BytesIO(file_bytes))
+            text = " ".join(p.text for p in doc.paragraphs if p.text)
+        elif mime in ['image/jpeg', 'image/png']:
+            img = Image.open(BytesIO(file_bytes)).convert('L').resize((int(img.width * 300 / img.height), 300))
+            text = pytesseract.image_to_string(img)
+        elif mime == 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet':
+            df = pd.read_excel(BytesIO(file_bytes), engine='openpyxl')
+            text = " ".join(df.astype(str).values.flatten())
+        else:
+            text = textract.process(file_bytes).decode("utf-8", errors="ignore")
+        text = re.sub(r"\$\s*([^$]+)\s*\$", r"\1", text)
+        text = re.sub(r"\\cap", "intersection", text)
+        text = re.sub(r"\s+", " ", text).strip()
+        text = "".join(c for c in text if ord(c) < 128)
+        print(f"Extracted chars: {len(text)}")
+    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+1000] for i in range(0, len(text), 1000)]
+    print(f"Chunks created: {len(chunks)}")
+    if not chunks: return "❌ No chunks to summarize"
+    summaries = []
+    for i in range(0, len(chunks), 4):
+        if time.time() - start > 15:
+            summaries.append("⚠️ Stopped early")
+            break
+        batch = chunks[i:i+4]
+        if any(sum(1 for c in chunk if not c.isalnum()) / len(chunk) > 0.7 for chunk in batch):
+            summaries.append(f"**Chunk {i+1}–{i+len(batch)}**: Skipped (equation-heavy)")
+            continue
+        try:
+            results = summarizer(batch, max_length=50, min_length=10, do_sample=False)
+            summaries.extend(f"**Chunk {i+j+1}**:\n{r['summary_text']}" for j, r in enumerate(results))
+        except Exception as e:
+            summaries.append(f"**Chunk {i+1}–{i+len(batch)}**: ❌ 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="📄 Any File", type="file"),
+    outputs=gr.Textbox(label="📝 Summary"),
+    title="Fast Summarizer", description="300,000+ chars in ~10–15s (CPU)"
+)
+
+if __name__ == "__main__":
+    try:
+        demo.launch(share=False, server_port=7860)
+    except Exception as e:
+        print(f"❌ Gradio launch failed: {str(e)}")