690605:1121 ADR-034-134 #10.8 [skip CI]
This commit is contained in:
@@ -71,7 +71,7 @@ TESSERACT_PSM = os.getenv("TESSERACT_PSM", "3")
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# PSM 6 = Assume single column of text (ลด hallucination จาก noise)
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# OEM 1 = LSTM only (ดีกว่า legacy engine)
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TESSERACT_CONFIG = f"--psm {TESSERACT_PSM} --oem 1"
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# Crop margin: ตัด header/footer (บน 5%, ล่าง 2%)
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# Crop margin: ตัด header/afooter (บน 5%, ล่าง 2%)
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CROP_TOP_RATIO = 0.05
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CROP_BOTTOM_RATIO = 0.02
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# Enable aggressive preprocessing (Option 2) สำหรับ Tesseract
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@@ -341,7 +341,27 @@ def process_with_typhoon_ocr(pil_image: Image.Image, options_override: dict = {}
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}
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payload = {
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"model": model_name,
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"prompt": "", # SYSTEM instruction ใน Modelfile จัดการทั้งหมด
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"prompt": """You are an expert in structuring Thai documents
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Task: Extract the information from the image in the most correct and organized format.
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Output Rules:
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- Return ONLY clean Markdown output
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- Include ALL information visible on the page
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- Preserve document structure and hierarchy
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- Do NOT add explanations or interpretations
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- Do NOT include these instructions in your response
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Formatting:
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- Tables: Use HTML <table> tags
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- Math: $inline$ and $$block$$ LaTeX
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- Figures: <figure>Thai description</figure>
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- Pages: <page_number>N</page_number>
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- Boxes: ☐ / ☑
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- Unclear: [unclear: context]
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- Signatures/Stamps: Describe location and context
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Extract all text from this image.""",
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"images": [image_base64],
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"stream": False,
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"options": options,
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+112
-20
@@ -19,6 +19,7 @@
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# - 2026-06-04: ส่ง color image (ไม่ผ่าน preprocess_image) ไปยัง Typhoon OCR — vision model ต้องการ color ไม่ใช่ binarized grayscale
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# - 2026-06-04: เพิ่ม num_gpu:99 ใน Ollama options เพื่อบังคับ GPU layers (แก้ device=CPU ทั้งที่ VRAM พอ)
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# - 2026-06-02: เพิ่มการตรวจสอบ API Key (X-API-Key Header) สำหรับ endpoints หลัก เพื่อความมั่นคงปลอดภัยตามข้อเสนอแนะ Code Review
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# - 2026-06-05: เพิ่ม Option 2 (aggressive preprocessing: deskew + Otsu threshold + morphology) และ Option 3 (smart post-processing: regex-based hallucination removal) เพื่อลด Tesseract noise/hallucination (T025)
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import os
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import logging
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@@ -64,15 +65,21 @@ TYPHOON_OCR_MODEL = os.getenv("TYPHOON_OCR_MODEL", "typhoon-np-dms-ocr:latest")
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TYPHOON_OCR_TIMEOUT = int(os.getenv("TYPHOON_OCR_TIMEOUT", "360")) # รองรับ cold-start ~65s + inference ~30s/page
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# DPI สำหรับ Typhoon OCR — ต่ำกว่า Tesseract เพราะ vision model ใช้ image patches (150 DPI ลด token ~4x)
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TYPHOON_OCR_DPI = int(os.getenv("TYPHOON_OCR_DPI", "150"))
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# PSM mode: 3 (default, fully automatic) หรือ 6 (assume single column, ลด noise)
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TESSERACT_PSM = os.getenv("TESSERACT_PSM", "3")
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# PSM 3 = Fully automatic page segmentation (เหมาะกับเอกสารที่มี layout หลายส่วน เช่น วันที่/เลขที่)
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# PSM 6 = Assume single column of text (ลด hallucination จาก noise)
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# OEM 1 = LSTM only (ดีกว่า legacy engine)
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TESSERACT_CONFIG = f"--psm 3 --oem 1"
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# Crop margin: ตัด header/footer (บน 5%, ล่าง 2%)
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TESSERACT_CONFIG = f"--psm {TESSERACT_PSM} --oem 1"
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# Crop margin: ตัด header/afooter (บน 5%, ล่าง 2%)
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CROP_TOP_RATIO = 0.05
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CROP_BOTTOM_RATIO = 0.02
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# Enable aggressive preprocessing (Option 2) สำหรับ Tesseract
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USE_AGGRESSIVE_PREPROCESSING = os.getenv("TESSERACT_AGGRESSIVE_PREPROCESS", "true").lower() == "true"
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# Enable smart post-processing (Option 3) สำหรับลบ hallucination
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USE_SMART_CLEANING = os.getenv("TESSERACT_SMART_CLEAN", "true").lower() == "true"
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logger.info(f"Tesseract OCR Sidecar initialized (lang={OCR_LANG}, config={TESSERACT_CONFIG})")
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logger.info(f"Tesseract OCR Sidecar initialized (lang={OCR_LANG}, config={TESSERACT_CONFIG}, aggressive={USE_AGGRESSIVE_PREPROCESSING}, smart_clean={USE_SMART_CLEANING})")
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def filter_ocr_noise(text: str) -> str:
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"""Filter ขยะ OCR เช่น บรรทัดสั้น/สัญลักษณ์ที่ไม่มีความหมาย"""
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@@ -112,9 +119,8 @@ def crop_header_footer(pil_image: Image.Image, top_ratio: float = 0.10, bottom_r
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cropped = pil_image.crop((0, top_crop, width, height - bottom_crop))
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return cropped
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def preprocess_image(pil_image: Image.Image) -> Image.Image:
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"""Preprocess image ด้วย OpenCV เพื่อเพิ่มความแม่นยำ OCR"""
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"""Preprocess image ด้วย OpenCV เพื่อเพิ่มความแม่นยำ OCR (แบบธรรมชาติ)"""
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# แปลง PIL Image เป็น numpy array (OpenCV format)
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img_array = np.array(pil_image)
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@@ -128,13 +134,93 @@ def preprocess_image(pil_image: Image.Image) -> Image.Image:
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# แปลงกลับเป็น PIL Image
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return Image.fromarray(denoised)
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def preprocess_image_aggressive(pil_image: Image.Image) -> Image.Image:
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"""
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Aggressive preprocessing (Option 2) — ลด hallucination โดย:
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1. Deskew ถ้าหน้าเอียง
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2. Denoise ด้วย bilateral filter
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3. Otsu adaptive threshold
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4. Morphological operations
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"""
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img_array = np.array(pil_image)
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gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
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# 1. Deskew ถ้าหน้าเอียง (detect angle จาก Canny edges + Hough lines)
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try:
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edges = cv2.Canny(gray, 100, 200)
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lines = cv2.HoughLinesP(edges, 1, np.pi/180, 100, minLineLength=100, maxLineGap=10)
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if lines is not None and len(lines) > 0:
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angles = [np.arctan2(y2-y1, x2-x1) for x1,y1,x2,y2 in lines[:min(10, len(lines))]]
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angle = np.median(angles) * 180 / np.pi
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if abs(angle) > 0.5: # มุมเอียงน้อย ≥ 0.5 องศา
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h, w = gray.shape
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M = cv2.getRotationMatrix2D((w/2, h/2), angle, 1.0)
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gray = cv2.warpAffine(gray, M, (w, h), borderMode=cv2.BORDER_REFLECT)
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logger.info(f"[PREPROCESS] Deskewed {angle:.1f}°")
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except Exception as e:
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logger.warning(f"[PREPROCESS] Deskew failed: {e}")
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# 2. Denoise — median blur + bilateral filter
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denoised = cv2.medianBlur(gray, 3)
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denoised = cv2.bilateralFilter(denoised, 9, 75, 75)
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# 3. Otsu threshold (adaptive, ไม่ fixed value)
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_, thresh = cv2.threshold(denoised, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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# 4. Morphological operations — ลบ line noise ขนาดเล็ก (ต้าน speckle artifacts)
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2, 2))
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morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel) # ลบ small white noise
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morph = cv2.morphologyEx(morph, cv2.MORPH_CLOSE, kernel) # ลบ small black hole
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logger.info(f"[PREPROCESS] Aggressive: Otsu threshold + morphology applied")
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return Image.fromarray(morph)
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def clean_ocr_output(text: str) -> str:
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"""
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Smart post-processing (Option 3) — ลบ Tesseract hallucination โดย:
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1. ลบ line ที่เป็นแค่สัญลักษณ์ repeated
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2. ลบ line ที่เป็นแค่สัญลักษณ์แปลก
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3. ลบ line ที่ซ้ำตัวอักษรเดียว (artifact noise)
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"""
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lines = text.split("\n")
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cleaned = []
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for line in lines:
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line = line.strip()
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if not line:
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continue
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# ✗ ลบ line ที่เป็นแค่สัญลักษณ์/punctuation เดี่ยวๆ ไม่มีตัวอักษร
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alphanumeric_part = re.sub(r'[^\w\u0E00-\u0E7F]', '', line)
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if len(alphanumeric_part) < 2:
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logger.debug(f"[CLEAN] Reject (no alphanum): {line[:50]}")
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continue
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# ✗ ลบ line ที่เป็น repeated pattern — ถ้า unique char ≤ 20% (e.g., "-----", ">>>>>>>")
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unique_chars = len(set(line))
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if unique_chars < max(2, len(line) // 5):
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logger.debug(f"[CLEAN] Reject (repeated pattern): {line[:50]}")
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continue
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# ✗ ลบ line ที่เป็นสัญลักษณ์แปลก (< 20% Thai/English alphanumeric)
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thai_chars = sum(1 for c in line if '\u0E00' <= c <= '\u0E7F')
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eng_chars = sum(1 for c in line if c.isascii() and c.isalnum())
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if len(line) > 0 and (thai_chars + eng_chars) / len(line) < 0.2:
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logger.debug(f"[CLEAN] Reject (low language content): {line[:50]}")
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continue
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# ✓ ปล่อยผ่าน
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cleaned.append(line)
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result = "\n".join(cleaned)
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logger.info(f"[CLEAN] Input {len(lines)} lines → {len(cleaned)} lines")
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return result
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class OcrRequest(BaseModel):
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pdfPath: str
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maxPages: Optional[int] = None
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engine: Optional[str] = None
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class OcrResponse(BaseModel):
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text: str
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ocrUsed: bool
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@@ -142,16 +228,17 @@ class OcrResponse(BaseModel):
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charCount: int
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engineUsed: str
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@app.get("/health")
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def health():
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return {
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"status": "ok",
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"engines": ["tesseract", "typhoon-np-dms-ocr"],
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"typhoonModel": TYPHOON_OCR_MODEL,
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"tesseractConfig": TESSERACT_CONFIG,
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"aggressivePreprocess": USE_AGGRESSIVE_PREPROCESSING,
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"smartCleaning": USE_SMART_CLEANING,
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}
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# alias map สำหรับ engine name เก่า → canonical name
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_ENGINE_ALIASES: dict[str, str] = {
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"typhoon-ocr1.5-3b": "typhoon-np-dms-ocr",
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@@ -159,7 +246,6 @@ _ENGINE_ALIASES: dict[str, str] = {
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"typhoon_ocr": "typhoon-np-dms-ocr",
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}
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def _process_pdf_doc(doc: fitz.Document, selected_engine: str, max_pages: int, typhoon_options: dict = {}) -> OcrResponse:
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"""ประมวลผล fitz.Document ด้วย engine ที่เลือก — shared logic สำหรับ /ocr และ /ocr-upload"""
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selected_engine = _ENGINE_ALIASES.get(selected_engine, selected_engine)
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@@ -211,11 +297,24 @@ def _process_pdf_doc(doc: fitz.Document, selected_engine: str, max_pages: int, t
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img_bytes = pix.tobytes("png")
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img = Image.open(io.BytesIO(img_bytes))
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cropped_img = crop_header_footer(img, CROP_TOP_RATIO, CROP_BOTTOM_RATIO)
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processed_img = preprocess_image(cropped_img)
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# Option 2: Choose preprocessing strategy
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if USE_AGGRESSIVE_PREPROCESSING:
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processed_img = preprocess_image_aggressive(cropped_img)
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else:
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processed_img = preprocess_image(cropped_img)
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text = pytesseract.image_to_string(processed_img, lang=OCR_LANG, config=TESSERACT_CONFIG)
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ocr_text_parts.append(text.strip())
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ocr_text = filter_ocr_noise("\n".join(ocr_text_parts).strip())
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ocr_text = "\n".join(ocr_text_parts).strip()
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# Option 3: Apply smart post-processing
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if USE_SMART_CLEANING:
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ocr_text = clean_ocr_output(ocr_text)
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else:
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ocr_text = filter_ocr_noise(ocr_text)
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logger.info(f"Tesseract extracted {len(ocr_text)} chars")
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return OcrResponse(
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text=ocr_text,
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@@ -225,7 +324,6 @@ def _process_pdf_doc(doc: fitz.Document, selected_engine: str, max_pages: int, t
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engineUsed="tesseract",
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)
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def process_with_typhoon_ocr(pil_image: Image.Image, options_override: dict = {}) -> str:
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"""เรียก Typhoon OCR ผ่าน Ollama — ใช้ SYSTEM ใน Modelfile เป็น instruction หลัก; options_override ยัง override ค่า Modelfile ได้"""
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model_name = TYPHOON_OCR_MODEL
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@@ -243,7 +341,7 @@ def process_with_typhoon_ocr(pil_image: Image.Image, options_override: dict = {}
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}
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payload = {
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"model": model_name,
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"prompt": "Extract all text from this image.",
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"prompt": "", # SYSTEM instruction ใน Modelfile จัดการทั้งหมด
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"images": [image_base64],
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"stream": False,
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"options": options,
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@@ -267,7 +365,6 @@ def process_with_typhoon_ocr(pil_image: Image.Image, options_override: dict = {}
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)
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return result_text
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@app.post("/ocr", response_model=OcrResponse, dependencies=[Depends(get_api_key)])
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def ocr_extract(req: OcrRequest):
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"""OCR จาก path (legacy — ใช้เมื่อ sidecar และ backend เข้าถึง storage เดียวกัน)"""
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@@ -282,7 +379,6 @@ def ocr_extract(req: OcrRequest):
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raise HTTPException(status_code=422, detail=f"เปิดไฟล์ PDF ล้มเหลว: {e}")
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return _process_pdf_doc(doc, selected_engine, max_pages)
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@app.post("/ocr-upload", response_model=OcrResponse, dependencies=[Depends(get_api_key)])
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def ocr_upload(
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file: UploadFile = File(...),
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@@ -311,15 +407,12 @@ def ocr_upload(
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logger.info(f"OCR upload: {file.filename} engine={selected_engine} options={typhoon_options or 'modelfile-defaults'}")
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return _process_pdf_doc(doc, selected_engine, max_pages, typhoon_options)
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class NormalizeRequest(BaseModel):
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text: str
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class NormalizeResponse(BaseModel):
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normalized: str
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@app.post("/normalize", response_model=NormalizeResponse, dependencies=[Depends(get_api_key)])
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def normalize_text(req: NormalizeRequest):
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"""Normalize Thai text ด้วย PyThaiNLP สำหรับ rag-thai-preprocess queue"""
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@@ -333,7 +426,6 @@ def normalize_text(req: NormalizeRequest):
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logger.warning(f"Thai normalize failed, returning raw text: {e}")
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return NormalizeResponse(normalized=req.text)
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if __name__ == "__main__":
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import uvicorn
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port = int(os.getenv("OCR_PORT", "8765"))
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-20
@@ -5,23 +5,3 @@ PARAMETER num_predict 4096
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PARAMETER temperature 0.1
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PARAMETER top_p 0.1
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PARAMETER repeat_penalty 1.1
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PARAMETER stop "\n\n\n"
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SYSTEM """You are an expert in structuring Thai documents
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Task: Extract the information from the image in the most correct and organized format
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Output Rules:
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- Return ONLY clean Markdown output
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- Include ALL information visible on the page
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- Preserve document structure and hierarchy
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- Do NOT add explanations or interpretations
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Formatting:
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- Tables: Use HTML <table> tags
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- Math: $inline$ and $$block$$ LaTeX
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- Figures: <figure>Thai description</figure>
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- Pages: <page_number>N</page_number>
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- Boxes: ☐ / ☑
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- Unclear: [unclear: context]
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- Signatures/Stamps: Describe location and context"""
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Reference in New Issue
Block a user