预测界面修改,添加模型列表,分页

This commit is contained in:
xz2000 2025-07-22 18:47:35 +08:00
parent 751de9b548
commit e1980b3755
4 changed files with 153 additions and 182 deletions

View File

@ -4,168 +4,147 @@
<template #header>
<div class="card-header">
<span>按药品预测</span>
<el-tooltip content="使用针对特定药品训练的模型进行销售预测">
<el-tooltip content="对系统中的所有药品模型进行批量或单个预测">
<el-icon><QuestionFilled /></el-icon>
</el-tooltip>
</div>
</template>
<div class="model-selection-section">
<h4>🎯 选择预测模型</h4>
<el-form :model="form" label-width="120px">
<el-row :gutter="20">
<el-col :span="8">
<el-form-item label="目标药品">
<ProductSelector
v-model="form.product_id"
@change="handleProductChange"
:show-all-option="false"
/>
</el-form-item>
</el-col>
<el-col :span="8">
<el-form-item label="算法类型">
<el-select
v-model="form.model_type"
placeholder="选择算法"
@change="handleModelTypeChange"
style="width: 100%"
:disabled="!form.product_id"
>
<el-option
v-for="item in modelTypes"
:key="item.id"
:label="item.name"
:value="item.id"
/>
</el-select>
</el-form-item>
</el-col>
</el-row>
<el-row :gutter="20" v-if="form.model_type">
<el-col :span="5">
<el-form-item label="模型版本">
<el-select
v-model="form.version"
placeholder="选择版本"
style="width: 100%"
:disabled="!availableVersions.length"
:loading="versionsLoading"
>
<el-option
v-for="version in availableVersions"
:key="version"
:label="version"
:value="version"
/>
</el-select>
</el-form-item>
</el-col>
<el-col :span="5">
<el-form-item label="预测天数">
<el-input-number
v-model="form.future_days"
:min="1"
:max="365"
style="width: 100%"
/>
</el-form-item>
</el-col>
<el-col :span="5">
<el-form-item label="历史天数">
<el-input-number
v-model="form.history_lookback_days"
:min="7"
:max="365"
style="width: 100%"
/>
</el-form-item>
</el-col>
<el-col :span="5">
<el-form-item label="起始日期">
<el-date-picker
v-model="form.start_date"
type="date"
placeholder="选择日期"
format="YYYY-MM-DD"
value-format="YYYY-MM-DD"
style="width: 100%"
:clearable="false"
/>
</el-form-item>
</el-col>
<el-col :span="4">
<el-form-item label="预测分析">
<el-switch
v-model="form.analyze_result"
active-text="开启"
inactive-text="关闭"
/>
</el-form-item>
</el-col>
</el-row>
<div class="controls-section">
<el-form :model="filters" label-width="80px" inline>
<el-form-item label="目标药品">
<ProductSelector
v-model="filters.product_id"
:show-all-option="true"
all-option-label="所有药品"
/>
</el-form-item>
<el-form-item label="算法类型">
<el-select v-model="filters.model_type" placeholder="所有类型" clearable>
<el-option
v-for="item in modelTypes"
:key="item.id"
:label="item.name"
:value="item.id"
/>
</el-select>
</el-form-item>
<el-form-item label="预测天数">
<el-input-number v-model="form.future_days" :min="1" :max="365" />
</el-form-item>
<el-form-item label="历史天数">
<el-input-number v-model="form.history_lookback_days" :min="7" :max="365" />
</el-form-item>
<el-form-item label="起始日期">
<el-date-picker
v-model="form.start_date"
type="date"
placeholder="选择日期"
format="YYYY-MM-DD"
value-format="YYYY-MM-DD"
:clearable="false"
/>
</el-form-item>
</el-form>
</div>
<div class="prediction-actions">
<el-button
type="primary"
size="large"
@click="startPrediction"
:loading="predicting"
:disabled="!canPredict"
>
<el-icon><TrendCharts /></el-icon>
开始预测
</el-button>
<!-- 模型列表 -->
<div class="model-list-section">
<h4>📦 可用药品模型列表</h4>
<el-table :data="paginatedModelList" style="width: 100%" v-loading="modelsLoading">
<el-table-column prop="product_name" label="药品名称" sortable />
<el-table-column prop="model_type" label="模型类型" sortable />
<el-table-column prop="version" label="版本" />
<el-table-column prop="created_at" label="创建时间" />
<el-table-column label="操作">
<template #default="{ row }">
<el-button
type="primary"
size="small"
@click="startPrediction(row)"
:loading="predicting[row.model_id]"
>
<el-icon><TrendCharts /></el-icon>
开始预测
</el-button>
</template>
</el-table-column>
</el-table>
<el-pagination
background
layout="prev, pager, next"
:total="filteredModelList.length"
:page-size="pagination.pageSize"
@current-change="handlePageChange"
style="margin-top: 20px; justify-content: center;"
/>
</div>
</el-card>
<el-card v-if="predictionResult" style="margin-top: 20px">
<template #header>
<div class="card-header">
<span>📈 预测结果</span>
</div>
</template>
<!-- 预测结果弹窗 -->
<el-dialog v-model="dialogVisible" title="📈 预测结果" width="70%">
<div class="prediction-chart">
<canvas ref="chartCanvas" width="800" height="400"></canvas>
</div>
</el-card>
<template #footer>
<el-button @click="dialogVisible = false">关闭</el-button>
</template>
</el-dialog>
</div>
</template>
<script setup>
import { ref, reactive, onMounted, computed, watch, nextTick } from 'vue'
import { ref, reactive, onMounted, nextTick, computed } from 'vue'
import axios from 'axios'
import { ElMessage } from 'element-plus'
import { ElMessage, ElDialog, ElTable, ElTableColumn, ElButton, ElIcon, ElCard, ElTooltip, ElForm, ElFormItem, ElInputNumber, ElDatePicker, ElSelect, ElOption, ElRow, ElCol, ElPagination } from 'element-plus'
import { QuestionFilled, TrendCharts } from '@element-plus/icons-vue'
import Chart from 'chart.js/auto'
import ProductSelector from '../../components/ProductSelector.vue'
const modelList = ref([])
const modelTypes = ref([])
const availableVersions = ref([])
const versionsLoading = ref(false)
const predicting = ref(false)
const modelsLoading = ref(false)
const predicting = reactive({})
const dialogVisible = ref(false)
const predictionResult = ref(null)
const chartCanvas = ref(null)
let chart = null
const form = reactive({
training_mode: 'product',
product_id: '',
model_type: '',
version: '',
future_days: 7,
history_lookback_days: 30,
start_date: '',
analyze_result: true
analyze_result: true // UI
})
const canPredict = computed(() => {
return form.product_id && form.model_type && form.version
const filters = reactive({
product_id: '',
model_type: ''
})
const pagination = reactive({
currentPage: 1,
pageSize: 8
})
const filteredModelList = computed(() => {
return modelList.value.filter(model => {
const productMatch = !filters.product_id || model.product_id === filters.product_id
const modelTypeMatch = !filters.model_type || model.model_type === filters.model_type
return productMatch && modelTypeMatch
})
})
const paginatedModelList = computed(() => {
const start = (pagination.currentPage - 1) * pagination.pageSize
const end = start + pagination.pageSize
return filteredModelList.value.slice(start, end)
})
const handlePageChange = (page) => {
pagination.currentPage = page
}
const fetchModelTypes = async () => {
try {
const response = await axios.get('/api/model_types')
@ -177,67 +156,48 @@ const fetchModelTypes = async () => {
}
}
const fetchAvailableVersions = async () => {
if (!form.product_id || !form.model_type) {
availableVersions.value = []
return
}
const fetchModels = async () => {
modelsLoading.value = true
try {
versionsLoading.value = true
const url = `/api/models/${form.product_id}/${form.model_type}/versions`
const response = await axios.get(url)
const response = await axios.get('/api/models', { params: { training_mode: 'product' } })
if (response.data.status === 'success') {
availableVersions.value = response.data.data.versions || []
if (response.data.data.latest_version) {
form.version = response.data.data.latest_version
}
modelList.value = response.data.data
} else {
ElMessage.error('获取模型列表失败')
}
} catch (error) {
availableVersions.value = []
ElMessage.error('获取模型列表失败')
} finally {
versionsLoading.value = false
modelsLoading.value = false
}
}
const handleProductChange = () => {
form.model_type = ''
form.version = ''
availableVersions.value = []
}
const handleModelTypeChange = () => {
form.version = ''
fetchAvailableVersions()
}
const startPrediction = async () => {
const startPrediction = async (model) => {
predicting[model.model_id] = true
try {
predicting.value = true
const payload = {
product_id: form.product_id,
model_type: form.model_type,
version: form.version,
product_id: model.product_id,
model_type: model.model_type,
version: model.version,
future_days: form.future_days,
history_lookback_days: form.history_lookback_days,
start_date: form.start_date,
include_visualization: form.analyze_result,
include_visualization: true, //
}
// Corrected API endpoint from /api/predict to /api/prediction
const response = await axios.post('/api/prediction', payload)
if (response.data.status === 'success') {
// The backend response may have history_data and prediction_data at the top level
predictionResult.value = response.data.data
ElMessage.success('预测完成!')
dialogVisible.value = true
await nextTick()
renderChart()
} else {
ElMessage.error(response.data.error || '预测失败')
}
} catch (error)
{
} catch (error) {
ElMessage.error(error.response?.data?.error || '预测请求失败')
} finally {
predicting.value = false
predicting[model.model_id] = false
}
}
@ -258,7 +218,7 @@ const renderChart = () => {
}
const allLabels = [...new Set([...historyData.map(p => p.date), ...predictionData.map(p => p.date)])].sort()
const simplifiedLabels = allLabels.map(date => date.split('-')[2]);
const simplifiedLabels = allLabels.map(date => date.split('-').slice(1).join('/'));
const historyMap = new Map(historyData.map(p => [p.date, p.sales]))
const predictionMap = new Map(predictionData.map(p => [p.date, p.predicted_sales]))
@ -315,7 +275,7 @@ const renderChart = () => {
title: {
display: true,
text: `${predictionResult.value.product_name} - 销量预测趋势图`,
color: '#ffffff',
color: '#303133',
font: {
size: 20,
weight: 'bold',
@ -324,7 +284,7 @@ const renderChart = () => {
subtitle: {
display: true,
text: subtitleText,
color: '#6c757d',
color: '#606266',
font: {
size: 14,
},
@ -337,7 +297,7 @@ const renderChart = () => {
x: {
title: {
display: true,
text: '日期 (日)'
text: '日期'
},
grid: {
display: false
@ -360,14 +320,11 @@ const renderChart = () => {
}
onMounted(() => {
fetchModels()
fetchModelTypes()
const today = new Date()
form.start_date = today.toISOString().split('T')[0]
})
watch([() => form.product_id, () => form.model_type], () => {
fetchAvailableVersions()
})
</script>
<style scoped>
@ -379,15 +336,11 @@ watch([() => form.product_id, () => form.model_type], () => {
justify-content: space-between;
align-items: center;
}
.model-selection-section h4 {
margin-bottom: 16px;
}
.prediction-actions {
display: flex;
justify-content: center;
.filters-section, .global-settings-section, .model-list-section {
margin-top: 20px;
padding-top: 20px;
border-top: 1px solid #ebeef5;
}
.filters-section h4, .global-settings-section h4, .model-list-section h4 {
margin-bottom: 16px;
}
.prediction-chart {
margin-top: 20px;

Binary file not shown.

View File

@ -203,6 +203,21 @@ class CustomJSONEncoder(json.JSONEncoder):
return obj.isoformat()
return super(CustomJSONEncoder, self).default(obj)
# Helper function to convert numpy types to native python types for JSON serialization
def convert_numpy_types_for_json(obj):
if isinstance(obj, dict):
return {k: convert_numpy_types_for_json(v) for k, v in obj.items()}
elif isinstance(obj, list):
return [convert_numpy_types_for_json(item) for item in obj]
elif isinstance(obj, (np.generic, np.floating, np.integer)):
return obj.item()
elif isinstance(obj, np.ndarray):
return obj.tolist()
elif pd.isna(obj):
return None
else:
return obj
app = Flask(__name__)
# 设置自定义JSON编码器
app.json_encoder = CustomJSONEncoder
@ -1464,7 +1479,7 @@ def predict():
# 调用辅助函数保存结果
save_prediction_result(
prediction_result=prediction_result,
prediction_result=prediction_result.copy(),
product_id=product_id or store_id or 'global',
product_name=product_name_to_save,
model_type=model_type,
@ -2165,9 +2180,12 @@ def list_models():
for i, model in enumerate(formatted_models):
logger.info(f"[API] 模型 {i+1}: id='{model.get('model_id', 'EMPTY')}', filename='{model.get('filename', 'MISSING')}'")
# Manually convert numpy types to prevent JSON serialization errors
processed_models = convert_numpy_types_for_json(formatted_models)
return jsonify({
"status": "success",
"data": formatted_models,
"status": "success",
"data": processed_models,
"pagination": pagination
})
except Exception as e:

View File

@ -1,5 +1,5 @@
### 根目录启动
`uv pip install loguru numpy pandas torch matplotlib flask flask_cors flask_socketio flasgger scikit-learn tqdm pytorch_tcn pyarrow`
`uv pip install loguru numpy pandas torch matplotlib flask flask_cors flask_socketio flasgger scikit-learn tqdm pytorch_tcn pyarrow xgboost`
### UI
`npm install` `npm run dev`