Create app.py

app.py

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+from fastapi import FastAPI, Request, UploadFile, File, Form
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from transformers import pipeline
+import torch
+import io
+import pdfplumber
+from docx import Document
+
+app = FastAPI(title="Lecture Summarizer")
+
+app.mount("/static", StaticFiles(directory="static"), name="static")
+templates = Jinja2Templates(directory="templates")
+
+# Load model once (HF Spaces often have GPU — it will use it automatically if available)
+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",  # swap to "facebook/bart-large-cnn" for better quality if you upgrade hardware
+    device=device
+)
+
+def extract_text(content: bytes, filename: str) -> str | None:
+    filename = filename.lower()
+    try:
+        if filename.endswith('.pdf'):
+            with pdfplumber.open(io.BytesIO(content)) as pdf:
+                return "\n".join(page.extract_text() or "" for page in pdf.pages)
+        elif filename.endswith('.docx'):
+            doc = Document(io.BytesIO(content))
+            return "\n".join(para.text for para in doc.paragraphs if para.text.strip())
+        elif filename.endswith('.txt'):
+            return content.decode('utf-8', errors='replace')
+        return None
+    except:
+        return None
+
+def summarize_text(text: str, target_ratio: float = 0.32) -> str:
+    words = len(text.split())
+    if words < 100:
+        return text
+    target_length = int(words * target_ratio)
+    max_l = max(500, min(1300, target_length + 200))
+    min_l = max(300, int(target_length * 0.7))
+    try:
+        result = summarizer(
+            text,
+            max_length=max_l,
+            min_length=min_l,
+            length_penalty=1.8,
+            num_beams=4,
+            early_stopping=True,
+            do_sample=False,
+            truncation=True
+        )
+        return result[0]['summary_text']
+    except Exception as e:
+        print(f"Summarization error: {e}")
+        return "Error generating summary — text may be too long or empty."
+
+@app.get("/", response_class=HTMLResponse)
+async def home(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+@app.post("/summarize")
+async def summarize(
+    file: UploadFile | None = File(None),
+    text: str | None = Form(None)
+):
+    if file:
+        content = await file.read()
+        extracted = extract_text(content, file.filename)
+        if not extracted:
+            return {"error": "Could not extract text from file (supported: pdf, docx, txt)"}
+    elif text and text.strip():
+        extracted = text.strip()
+    else:
+        return {"error": "Provide either text or a file upload"}
+
+    summary = summarize_text(extracted)
+    return {"summary": summary}