摆设人工智能财务分析 API：使用百度文心一言的实践指南（二） - IT评测·应用市场-qidao123.com

payload = json.dumps({
"messages": [
{
"role": "user",
"content": prompt
}
],
"system": "你是一位专业的财务分析师，精通各类财务分析方法和指标，能够根据提供的财务数据生成详细、准确、具有实用价值的财务分析报告。你善于用简洁明了的语言阐述复杂的财务概念和分析结果，注重数据的准确性和逻辑的严密性。",
"temperature": 0.5,
"top_p": 0.75,
"penalty_score": 1.5,
"disable_search": True,
"enable_citation": True,
"max_output_tokens": 1800
})

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@app.route('/', methods=['GET'])
def index():
return render_template('index.html', report='')

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>财务分析报告生成器</title>
<style>
body {
font-family: Arial, sans-serif;
margin: 0;
padding: 0;
background-color: #f9f9f9;
}
.container {
max-width: 600px;
margin: 50px auto;
padding: 20px;
background: #ffffff;
border-radius: 8px;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
}
h1 {
text-align: center;
color: #333;
}
form {
margin-top: 20px;
}
input[type="file"] {
display: block;
margin: 10px 0 20px 0;
font-size: 16px;
}
button {
display: block;
width: 100%;
padding: 10px;
font-size: 16px;
background-color: #007BFF;
color: white;
border: none;
border-radius: 4px;
cursor: pointer;
}
button:hover {
background-color: #0056b3;
}
.report {
margin-top: 20px;
padding: 15px;
background-color: #f4f4f4;
border: 1px solid #ddd;
border-radius: 4px;
white-space: pre-wrap;
}
</style>
</head>
<body>
<div class="container">
<h1>财务分析报告生成器</h1>
<form action="/upload" method="POST" enctype="multipart/form-data">
<label for="file">选择CSV文件：</label>
<input type="file" id="file" name="file" accept=".csv" required>
<button type="submit">生成报告</button>
</form>
{% if report %}
<div class="report">
<h2>生成的报告：</h2>
<p>{{ report }}</p>
</div>
{% endif %}
</div>
</body>
</html>

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python app.py

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import jsonfrom flask import Flask, request, jsonify, render_templateimport pandas as pdimport requestsapp = Flask(__name__)API_URL = "https://aip.baidubce.com/oauth/2.0/token?client_id=cL8yyc5SALxrJJENhASs9ReF&client_secret=Y3QPkGO8r8wSi3IdnFOPzyNNThgKexUo&grant_type=client_credentials" # 替换为你的模子调用地址def get_access_token(): """ 使用 API Key，Secret Key 获取access_token，替换下列示例中的应用API Key、应用Secret Key """ url = "https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=cL8yyc5SALxrJJENhASs9ReF&client_secret=Y3QPkGO8r8wSi3IdnFOPzyNNThgKexUo" payload = json.dumps("") headers = { 'Content-Type': 'application/json', 'Accept': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) return response.json().get("access_token")from statsmodels.tsa.arima.model import ARIMAdef analyze_revenue_trend(data): try: # 提取收入数据 revenue_series = data['Revenue'] # 拟合ARIMA模子 model = ARIMA(revenue_series, order=(1, 1, 1)) model_fit = model.fit() # 预测未来3期收入 forecast = model_fit.forecast(steps=3) return { "current_revenue": int(revenue_series.iloc[-1]), "forecast": [int(f) for f in forecast] } except Exception as e: return {'error': f'Revenue trend analysis failed: {str(e)}'}def analyze_cost(data): """ 成本优化发起：计算已往12个月的均匀成本比例，提出优化发起。 """ try: data['Cost_Percentage'] = data['Cost'] / data['Revenue'] avg_cost_percentage = data['Cost_Percentage'].mean() suggestion = "Reduce operational costs by 5%" if avg_cost_percentage > 0.5 else "Cost structure is optimal" return { 'average_cost_percentage': float(avg_cost_percentage), # 假如这里可能出现int64也转换为float 'suggestion': suggestion } except Exception as e: return {'error': f'Cost analysis failed: {str(e)}'}def predict_debt_risk(data): """ 债务风险预测：根据债务与收入比例，评估风险品级。 """ try: debt_to_revenue_ratio = data['Debt'] / data['Revenue'] avg_ratio = debt_to_revenue_ratio.mean() risk_level = ( "High Risk" if avg_ratio > 0.8 else "Moderate Risk" if avg_ratio > 0.5 else "Low Risk" ) return { 'average_debt_to_revenue_ratio': float(avg_ratio), # 假如这里可能出现int64也转换为float 'risk_level': risk_level } except Exception as e: return {'error': f'Debt risk prediction failed: {str(e)}'}def generate_report(revenue_analysis, cost_analysis, debt_analysis): """ 使用百度千帆 ModelBuilder 生成智能分析报告。 """ url = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro?access_token=" + get_access_token() try: # 构造哀求数据 prompt = f""" 基于以下财务分析结果： 1. 收入分析：当前收入为 {revenue_analysis['current_revenue']}，未来三期收入预测为 {revenue_analysis['forecast']}。 2. 成本分析：均匀成本比例为 {cost_analysis['average_cost_percentage']:.2f}，发起为：{cost_analysis['suggestion']}。 3. 债务分析：均匀债务收入比为 {debt_analysis['average_debt_to_revenue_ratio']:.2f}，风险品级为：{debt_analysis['risk_level']}。请生成一份简洁的财务分析报告，包含总结和可实行发起。 """ payload = json.dumps({
"messages": [
{
"role": "user",
"content": prompt
}
],
"system": "你是一位专业的财务分析师，精通各类财务分析方法和指标，能够根据提供的财务数据生成详细、准确、具有实用价值的财务分析报告。你善于用简洁明了的语言阐述复杂的财务概念和分析结果，注重数据的准确性和逻辑的严密性。",
"temperature": 0.5,
"top_p": 0.75,
"penalty_score": 1.5,
"disable_search": True,
"enable_citation": True,
"max_output_tokens": 1800
}) headers = { 'Content-Type': 'application/json' } # 调用千帆 ModelBuilder API response = requests.request("POST", url, headers=headers, data=payload) if response.status_code == 200: data = json.loads(response.text) report_text = data.get('result', '') return report_text else: return f"Failed to generate report. Status code: {response.status_code}, Message: {response.text}" except Exception as e: return f"AI report generation failed: {str(e)}"@app.route('/', methods=['GET'])
def index():
return render_template('index.html', report='')@app.route('/upload', methods=['POST'])def upload_file(): if 'file' not in request.files: return jsonify({'error': 'No file uploaded'}), 400 file = request.files['file'] if not file.filename.endswith('.csv'): return jsonify({'error': 'Only CSV files are supported'}), 400 try: # 读取 CSV 数据 data = pd.read_csv(file) if not {'Date', 'Revenue', 'Cost', 'Debt'}.issubset(data.columns): return jsonify({'error': 'File must contain Date, Revenue, Cost, Debt columns'}), 400 # 数据预处理 data['Date'] = pd.to_datetime(data['Date']) data = data.sort_values('Date') # 调用分析函数 revenue_analysis = analyze_revenue_trend(data) cost_analysis = analyze_cost(data) debt_analysis = predict_debt_risk(data) # 结合千帆生成分析报告 AIreport = generate_report(revenue_analysis, cost_analysis, debt_analysis) return render_template('index.html', report=AIreport) except Exception as e: return jsonify({'error': str(e)}), 500if __name__ == '__main__': app.run(debug=True, port=5000)

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