Upload main (1).py

main (1).py

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+from fastapi import FastAPI, File, UploadFile, HTTPException, Query
+from fastapi.middleware.cors import CORSMiddleware
+from typing import List, Dict
+from io import BytesIO
+from PIL import Image
+import uvicorn
+import os
+import numpy as np
+import cv2
+import re
+
+# PDF support
+try:
+    from pdf2image import convert_from_bytes
+    PDF_AVAILABLE = True
+except:
+    PDF_AVAILABLE = False
+
+# Models
+paddle_detector = None
+paddle_recognizer = None
+
+app = FastAPI(title="OCR Scan Vision API", version="1.0.0")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# -------------------- تنظيف النص العربي --------------------
+def clean_arabic_text(text: str) -> str:
+    """
+    - يحافظ على الكلمات العربية والأرقام
+    - يحول الرموز المهمة (: - / _) لمسافة
+    - يشيل باقي الرموز
+    - يزيل التشكيل
+    - يضبط المسافات
+    """
+    if not text:
+        return ""
+
+    # الرموز المهمة تتحول لمسافة
+    text = re.sub(r"[:\-_/]", " ", text)
+
+    # شيل باقي الرموز
+    text = re.sub(r"[^\u0600-\u06FF0-9\s]", "", text)
+
+    # إزالة التشكيل
+    text = re.sub(r"[\u064B-\u065F]", "", text)
+
+    # إزالة مسافات زيادة
+    text = re.sub(r"\s+", " ", text)
+
+    return text.strip()
+
+
+def get_models():
+    global paddle_detector, paddle_recognizer
+
+    if paddle_detector is None or paddle_recognizer is None:
+        try:
+            from paddlex import create_model
+            print("Loading PaddleX OCR models...")
+            paddle_detector = create_model("PP-OCRv5_server_det")
+            paddle_recognizer = create_model("arabic_PP-OCRv5_mobile_rec")
+            print("Models loaded.")
+        except Exception as e:
+            raise HTTPException(
+                status_code=500,
+                detail=f"OCR models failed to load: {str(e)}"
+            )
+
+    return paddle_detector, paddle_recognizer
+
+
+def process_image(img: np.ndarray, detector, recognizer, min_conf: float) -> List[Dict]:
+    h_img, w_img = img.shape[:2]
+
+    # 1️⃣ كشف النصوص
+    results = detector.predict(img)
+
+    all_rois = []
+    all_bboxes = []
+
+    for result in results:
+        boxes = result.get("dt_polys", [])
+        for box in boxes:
+            pts = np.array(box, dtype=np.int32)
+            x, y, w, h = cv2.boundingRect(pts)
+
+            x1 = max(x, 0)
+            y1 = max(y, 0)
+            x2 = min(x + w, w_img)
+            y2 = min(y + h, h_img)
+
+            if x2 > x1 and y2 > y1:
+                roi = img[y1:y2, x1:x2]
+                if roi.size > 0:
+                    all_rois.append(roi)
+                    all_bboxes.append([x1, y1, x2, y2])
+
+    # 2️⃣ التعرف على النصوص
+    ocr_results = []
+
+    for i, roi in enumerate(all_rois):
+        try:
+            rec_gen = recognizer.predict(roi)
+            rec = next(rec_gen)
+            raw_text = rec.get("rec_text", "")
+            score = float(rec.get("rec_score", 0.0))
+            text = clean_arabic_text(raw_text)
+        except:
+            text = ""
+            score = 0.0
+
+        if score >= min_conf and text:
+            ocr_results.append({
+                "box_id": i + 1,
+                "text": text,
+                "confidence": round(score, 4),
+                "bbox": all_bboxes[i]
+            })
+
+    # ✅ ترتيب عربي: فوق → تحت ، يمين → شمال
+    ocr_results.sort(
+        key=lambda x: (
+            x["bbox"][1],     # Y
+            -x["bbox"][0]     # X (RTL)
+        )
+    )
+
+    return ocr_results
+
+
+@app.get("/")
+def root():
+    return {"name": "OCR Scan Vision API", "status": "ok", "pdf_support": PDF_AVAILABLE}
+
+
+@app.get("/health")
+def health():
+    return {"status": "healthy"}
+
+
+@app.post("/ocr")
+async def ocr_image(
+    file: UploadFile = File(...),
+    min_conf: float = Query(default=0.0, ge=0.0, le=1.0),
+):
+    try:
+        contents = await file.read()
+        pil_img = Image.open(BytesIO(contents)).convert("RGB")
+        img = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
+    except:
+        raise HTTPException(status_code=400, detail="Invalid image file")
+
+    detector, recognizer = get_models()
+    ocr_results = process_image(img, detector, recognizer, min_conf)
+
+    full_text = "\n".join([r["text"] for r in ocr_results])
+
+    return {
+        "items": ocr_results,
+        "text": full_text,
+        "total_boxes": len(ocr_results)
+    }
+
+
+@app.post("/ocr-pdf")
+async def ocr_pdf(
+    file: UploadFile = File(...),
+    dpi: int = Query(default=300, ge=72, le=600),
+    min_conf: float = Query(default=0.0, ge=0.0, le=1.0),
+):
+    if not PDF_AVAILABLE:
+        raise HTTPException(status_code=500, detail="PDF support not available")
+
+    try:
+        contents = await file.read()
+        pages = convert_from_bytes(contents, dpi=dpi)
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Invalid PDF file: {e}")
+
+    detector, recognizer = get_models()
+
+    all_results = []
+    all_text = []
+
+    for page_num, pil_img in enumerate(pages, start=1):
+        img = cv2.cvtColor(np.array(pil_img.convert("RGB")), cv2.COLOR_RGB2BGR)
+        page_results = process_image(img, detector, recognizer, min_conf)
+
+        for item in page_results:
+            item["page"] = page_num
+
+        all_results.extend(page_results)
+
+        page_text = "\n".join([r["text"] for r in page_results])
+        if page_text:
+            all_text.append(f"--- Page {page_num} ---\n{page_text}")
+
+    return {
+        "pages": len(pages),
+        "items": all_results,
+        "text": "\n\n".join(all_text),
+        "total_boxes": len(all_results)
+    }
+
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 7860))
+    uvicorn.run("main:app", host="0.0.0.0", port=port)