import requests

API_URL = "https://ollama_pjapi.theaken.com"
API_KEY = "YOUR_API_KEY"

headers = {"Authorization": f"Bearer {API_KEY}"}

def ocr_recognize(file_path, model, language="auto", output_format="text"):
    """通用 OCR 辨識函數"""
    with open(file_path, "rb") as f:
        response = requests.post(
            f"{API_URL}/v1/ocr/recognize",
            headers=headers,
            files={"file": f},
            data={
                "model": model,
                "language": language,
                "output_format": output_format
            }
        )
    return response.json()

# DeepSeek OCR - 複雜文件、手寫辨識、多語言混排
result = ocr_recognize(
    file_path="document.png",
    model="deepseek-ocr",
    language="auto"  # 支援: auto, zh, en, ja, ko
)
print("辨識結果:", result["text"])
print("信心度:", result["confidence"])

# DeepSeek OCR 辨識 PDF（自動偵測內嵌文字或轉圖片 OCR）
result = ocr_recognize(
    file_path="document.pdf",
    model="deepseek-ocr",
    language="zh"
)
print(result["text"])

# PP-OCRv5 - 高精度需求、80+ 語言支援、表格辨識
result = ocr_recognize(
    file_path="multilang_doc.png",
    model="pp-ocrv5",
    language="auto",
    output_format="markdown"  # 支援: text, json, markdown, all
)
print("辨識結果:", result["text"])

# PP-OCR 辨識多頁 PDF
result = ocr_recognize(
    file_path="report.pdf",
    model="pp-ocrv5",
    output_format="all"
)
print(result["text"])

# LLaVA OCR - AI 智能辨識、理解上下文、複雜版面
result = ocr_recognize(
    file_path="handwritten_note.jpg",
    model="llava-ocr",
    language="auto"
)
print("辨識結果:", result["text"])

# LLaVA 適合處理手寫筆記、複雜排版
result = ocr_recognize(
    file_path="complex_layout.png",
    model="llava-ocr"
)
print(result["text"])

# 通用 OCR - 印刷文件、清晰圖片、快速處理
result = ocr_recognize(
    file_path="printed_doc.png",
    model="general-ocr",
    language="zh"
)
print("辨識結果:", result["text"])

# 批量處理多個文件
import os
for filename in os.listdir("documents/"):
    if filename.endswith((".png", ".jpg")):
        result = ocr_recognize(f"documents/{filename}", model="general-ocr")
        print(f"{filename}: {result['text'][:100]}...")

# 表格 OCR - 表格結構保留、欄位對齊、Excel 匯出
result = ocr_recognize(
    file_path="spreadsheet.png",
    model="table-ocr",
    output_format="json"  # 推薦使用 json 格式取得結構化資料
)
print("辨識結果:", result["text"])

# 處理財務報表
result = ocr_recognize(
    file_path="financial_report.pdf",
    model="table-ocr",
    output_format="markdown"
)
print(result["text"])

# 發票 OCR - 發票格式、金額辨識、稅號提取
result = ocr_recognize(
    file_path="invoice.jpg",
    model="invoice-ocr",
    output_format="json"  # 推薦 json 格式取得結構化發票資訊
)
print("辨識結果:", result["text"])

# 處理收據
result = ocr_recognize(
    file_path="receipt.png",
    model="invoice-ocr"
)
print(result["text"])

# 查詢所有可用 OCR 模型
response = requests.get(f"{API_URL}/v1/ocr/models", headers=headers)
models = response.json()["models"]
for model in models:
    status = "✅" if model["available"] else "❌"
    print(f"{status} {model['id']}: {model['name']} - {model['best_for']}")

# 檢查 OCR 服務健康狀態
response = requests.get(f"{API_URL}/v1/ocr/health", headers=headers)
health = response.json()
print(f"服務狀態: {health['status']}")
for service, status in health["services"].items():
    print(f"  {service}: {status}")

{
    "success": true,
    "text": "辨識出的文字內容...",
    "confidence": 0.95,
    "model": "deepseek-ocr",
    "processing_time": 1.234,
    "details": {
        "pages": 1,
        "method": "direct_extraction"  // 或 "ocr"
    }
}

import requests

API_URL = "https://ollama_pjapi.theaken.com"
API_KEY = "YOUR_API_KEY"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

def chat(model, messages, stream=False):
    """通用對話函數"""
    response = requests.post(
        f"{API_URL}/v1/chat/completions",
        headers=headers,
        json={
            "model": model,
            "messages": messages,
            "stream": stream
        }
    )
    return response.json()

# 使用地端 GPT-OSS 120B 模型
result = chat(
    model="gpt-oss:120b",
    messages=[
        {"role": "system", "content": "你是一個專業的助手"},
        {"role": "user", "content": "請解釋什麼是機器學習"}
    ]
)
print(result["choices"][0]["message"]["content"])

# 使用地端 Qwen 2.5 72B 模型
result = chat(
    model="qwen2.5:72b",
    messages=[{"role": "user", "content": "用中文寫一首詩"}]
)
print(result["choices"][0]["message"]["content"])

# 使用 DeepSeek V3（通用對話）
result = chat(
    model="deepseek-ai/DeepSeek-V3",
    messages=[{"role": "user", "content": "今天天氣如何？"}]
)
print(result["choices"][0]["message"]["content"])

# 使用 DeepSeek R1（深度推理）
result = chat(
    model="deepseek-ai/DeepSeek-R1",
    messages=[{"role": "user", "content": "證明根號2是無理數"}]
)
print(result["choices"][0]["message"]["content"])

# 使用 Qwen 3 235B（頂尖性能）
result = chat(
    model="Qwen/Qwen3-235B-A22B",
    messages=[{"role": "user", "content": "分析人工智能的未來發展趨勢"}]
)
print(result["choices"][0]["message"]["content"])

# 使用 Qwen Coder（程式開發）
result = chat(
    model="Qwen/Qwen2.5-Coder-32B-Instruct",
    messages=[{"role": "user", "content": "寫一個 Python 快速排序演算法"}]
)
print(result["choices"][0]["message"]["content"])

import requests
import json

def chat_stream(model, messages):
    """串流對話函數"""
    response = requests.post(
        f"{API_URL}/v1/chat/completions",
        headers=headers,
        json={
            "model": model,
            "messages": messages,
            "stream": True
        },
        stream=True
    )

    for line in response.iter_lines():
        if line:
            line = line.decode('utf-8')
            if line.startswith('data: ') and line != 'data: [DONE]':
                data = json.loads(line[6:])
                if 'choices' in data and data['choices']:
                    delta = data['choices'][0].get('delta', {})
                    if 'content' in delta:
                        print(delta['content'], end='', flush=True)

# 使用串流回應
chat_stream(
    model="deepseek-ai/DeepSeek-V3",
    messages=[{"role": "user", "content": "寫一篇關於AI的文章"}]
)

# 查詢所有可用模型
response = requests.get(f"{API_URL}/v1/models", headers=headers)
models = response.json()["data"]

# 按部署類型分類顯示
local_models = [m for m in models if m.get("deployment_type") == "local"]
cloud_models = [m for m in models if m.get("deployment_type") == "cloud"]

print("=== 地端模型 ===")
for m in local_models:
    print(f"  {m['id']} ({m.get('provider', 'unknown')})")

print("\n=== 雲端模型 ===")
for m in cloud_models:
    provider = m.get('provider', 'unknown')
    print(f"  {m['id']} ({provider})")

{
    "id": "chatcmpl-xxx",
    "object": "chat.completion",
    "created": 1700000000,
    "model": "deepseek-ai/DeepSeek-V3",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "回應內容..."
            },
            "finish_reason": "stop"
        }
    ],
    "usage": {
        "prompt_tokens": 10,
        "completion_tokens": 50,
        "total_tokens": 60
    }
}