Delete main.py

main.py

@@ -1,123 +0,0 @@
-from fastapi import FastAPI, UploadFile, File
-from fastapi.responses import JSONResponse
-from fastapi.staticfiles import StaticFiles
-from fastapi.middleware.cors import CORSMiddleware
-
-from PyPDF2 import PdfReader
-import docx
-from docx.enum.text import WD_COLOR_INDEX
-from io import BytesIO
-import os
-import uvicorn
-
-app = FastAPI(
-    title="StealthWriter",
-    description="Detect AI content in documents and generate reports.",
-    version="1.0.0",
-    docs_url="/docs",  # Swagger UI
-    redoc_url="/redoc",  # Redoc UI
-    openapi_url="/openapi.json"
-)
-
-
-# ✅ CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["http://localhost:5173", "https://stealth-writer.vercel.app/"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# === Global lazy-load model vars ===
-analyze_text = None
-generate_pdf_report = None
-
-@app.on_event("startup")
-async def load_model():
-    global analyze_text, generate_pdf_report
-    from detector.custom_model import analyze_text as at, generate_pdf_report as gpr
-    analyze_text = at
-    generate_pdf_report = gpr
-
-# === Paths ===
-REPORTS_DIR = os.path.join(os.path.dirname(__file__), "reports")
-os.makedirs(REPORTS_DIR, exist_ok=True)
-
-app.mount("/reports", StaticFiles(directory=REPORTS_DIR), name="reports")
-
-# === File text extraction ===
-def extract_text(file: UploadFile, ext: str) -> str:
-    content = file.file.read()
-    file_bytes = BytesIO(content)
-
-    if ext == ".txt":
-        return content.decode("utf-8", errors="ignore")
-    elif ext == ".pdf":
-        reader = PdfReader(file_bytes)
-        return "".join([page.extract_text() or "" for page in reader.pages])
-    elif ext == ".docx":
-        doc = docx.Document(file_bytes)
-        return "\n".join([para.text for para in doc.paragraphs])
-    else:
-        raise ValueError("Unsupported file type")
-
-# === Main endpoint ===
-@app.post("/api/detect")
-async def detect(file: UploadFile = File(...)):
-    try:
-        ext = os.path.splitext(file.filename)[1].lower()
-        if ext not in [".txt", ".pdf", ".docx"]:
-            raise ValueError("Unsupported file format")
-
-        # Extract + Analyze
-        text = extract_text(file, ext)
-        result = analyze_text(text)
-
-        # === Save DOCX report ===
-        filename_base = os.path.splitext(file.filename)[0]
-        docx_filename = f"{filename_base}_report.docx"
-        docx_path = os.path.join(REPORTS_DIR, docx_filename)
-
-        doc = docx.Document()
-        doc.add_heading("AI Detection Summary", level=1)
-        doc.add_paragraph(f"Overall AI %: {result['overall_ai_percent']}%")
-        doc.add_paragraph(f"Total Sentences: {result['total_sentences']}")
-        doc.add_paragraph(f"AI Sentences: {result['ai_sentences']}")
-        doc.add_paragraph("Sentences detected as AI are highlighted in cyan.\n")
-        doc.add_heading("Sentence Analysis", level=2)
-
-        paragraph = doc.add_paragraph()
-        for para in result["results"]:
-            for sentence, is_ai, _ in para:
-                if not isinstance(sentence, str) or not sentence.strip():
-                    continue
-                run = paragraph.add_run(sentence + " ")
-                if is_ai:
-                    run.font.highlight_color = WD_COLOR_INDEX.TURQUOISE
-
-        doc.save(docx_path)
-
-        # === Save PDF report (uses ReportLab) ===
-        pdf_filename = generate_pdf_report(result, filename_base)
-
-        return {
-            "success": True,
-            "score": {
-                **{k: v for k, v in result.items() if k != "results"},
-                "results": [
-                    [{"sentence": s, "is_ai": is_ai, "ai_score": round(ai_score * 100, 2)} for s, is_ai, ai_score in para]
-                    for para in result["results"]
-                ]
-            },
-            "docx_url": f"/reports/{docx_filename}",
-            "pdf_url": f"/reports/{pdf_filename}"
-        }
-
-    except Exception as e:
-        return JSONResponse(content={"success": False, "error": str(e)}, status_code=500)
-
-# === Port binding for Render ===
-if __name__ == "__main__":
-    port = int(os.environ.get("PORT", 7860))  # Render will inject PORT
-    uvicorn.run("main:app", host="0.0.0.0", port=port)