import os
import time
import uuid
import random
import json
from flask import Flask, render_template, request, jsonify, send_file
from openai import OpenAI
from werkzeug.utils import secure_filename

app = Flask(__name__)
app.config['MAX_CONTENT_LENGTH'] = 50 * 1024 * 1024  # Increased to 50MB
app.config['UPLOAD_FOLDER'] = '/tmp/uploads'

if not os.path.exists(app.config['UPLOAD_FOLDER']):
    os.makedirs(app.config['UPLOAD_FOLDER'])

# SiliconFlow Configuration
API_KEY = "sk-vimuseiptfbomzegyuvmebjzooncsqbyjtlddrfodzcdskgi"
BASE_URL = "https://api.siliconflow.cn/v1"

client = OpenAI(
    api_key=API_KEY,
    base_url=BASE_URL
)

# In-memory storage for demo
KNOWLEDGE_VAULT = []

# Mock Data for Fallback
MOCK_INSIGHTS = [
    "市场需求呈现指数级增长，尤其在亚太地区。",
    "当前技术栈存在明显的性能瓶颈，建议迁移至 Rust 或 Go。",
    "用户留存率与响应速度呈正相关，相关系数 0.85。",
]
MOCK_TAGS = ["高增长", "风险警示", "技术债务", "蓝海市场", "自动化"]

@app.route('/')
def index():
    return render_template('index.html')

def analyze_content(content):
    """
    Call SiliconFlow API to analyze content.
    """
    try:
        response = client.chat.completions.create(
            model="Qwen/Qwen2.5-7B-Instruct",  # Using a common model
            messages=[
                {"role": "system", "content": """你是一个专业的商业分析师和知识提炼专家。
请分析用户提供的内容，并返回一个严格的 JSON 格式响应。
JSON 结构必须包含以下字段：
- summary (string): 内容的简明摘要（200字以内）
- insights (list of strings): 3-5个关键洞察
- tags (list of strings): 3-5个相关标签
- metrics (object): 包含 commercial_value (0-100), complexity (0-100), sentiment (0.0-1.0)

只返回 JSON，不要包含 Markdown 格式标记。"""},
                {"role": "user", "content": content}
            ],
            temperature=0.7,
            max_tokens=1000,
             response_format={"type": "json_object"}
        )
        
        result_text = response.choices[0].message.content
        # Try to clean markdown code blocks if present
        if "```json" in result_text:
            result_text = result_text.split("```json")[1].split("```")[0].strip()
        elif "```" in result_text:
            result_text = result_text.split("```")[1].split("```")[0].strip()
            
        return json.loads(result_text)
    except Exception as e:
        print(f"API Error: {e}")
        # Fallback to mock if API fails
        return {
            "summary": "分析服务暂时不可用或超时，已切换至演示模式。原始内容：" + content[:50] + "...",
            "insights": random.sample(MOCK_INSIGHTS, k=min(3, len(MOCK_INSIGHTS))),
            "tags": random.sample(MOCK_TAGS, k=3),
            "metrics": {
                "commercial_value": random.randint(50, 90),
                "complexity": random.randint(30, 70),
                "sentiment": 0.5
            }
        }

@app.route('/api/mine', methods=['POST'])
def mine_knowledge():
    """
    Extracts info from URL, Text, or File.
    """
    content = ""
    source_type = request.form.get('type', 'text') # Changed to form data for file upload support
    
    if source_type == 'file':
        if 'file' not in request.files:
            return jsonify({"error": "No file part"}), 400
        file = request.files['file']
        if file.filename == '':
            return jsonify({"error": "No selected file"}), 400
        if file:
            filename = secure_filename(file.filename)
            filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
            file.save(filepath)
            
            # Read file content (Simple text read for now)
            try:
                with open(filepath, 'r', encoding='utf-8') as f:
                    content = f.read()
            except UnicodeDecodeError:
                 return jsonify({"error": "File encoding not supported. Please upload UTF-8 text files."}), 400
            except Exception as e:
                return jsonify({"error": str(e)}), 500
    else:
        # For JSON requests (text/url), we might need to handle differently or expect form data
        # Let's support both JSON and Form for text/url to be safe, but since we use FormData for file, 
        # let's try to unify or check content-type.
        if request.is_json:
            data = request.json
            content = data.get('content', '')
            source_type = data.get('type', 'text')
        else:
            content = request.form.get('content', '')
    
    if not content and source_type != 'file':
         return jsonify({"error": "No content provided"}), 400

    # API Analysis
    analysis_result = analyze_content(content)
    
    analysis_id = str(uuid.uuid4())
    
    result = {
        "id": analysis_id,
        "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
        "source_type": source_type,
        "summary": analysis_result.get('summary', '无摘要'),
        "insights": analysis_result.get('insights', []),
        "tags": analysis_result.get('tags', []),
        "metrics": analysis_result.get('metrics', {
            "commercial_value": 0,
            "complexity": 0,
            "sentiment": 0.5
        })
    }
    
    # Store in vault
    KNOWLEDGE_VAULT.insert(0, result)
    
    return jsonify(result)

@app.route('/api/vault', methods=['GET'])
def get_vault():
    """
    Returns stored knowledge assets.
    """
    return jsonify(KNOWLEDGE_VAULT)

@app.route('/api/generate_asset', methods=['POST'])
def generate_asset():
    """
    Simulates generating a downloadable asset (PDF/Markdown).
    """
    data = request.json
    asset_id = data.get('id')
    asset_type = data.get('type', 'report')
    
    time.sleep(1)
    
    return jsonify({
        "status": "success",
        "message": f"{asset_type.upper()} 资产已生成",
        "download_url": "#" # Mock URL
    })

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860, debug=True)