Tag: mudblazor

When displaying output from Azure AI services (like Cognitive Services, OpenAI, etc.), the UI should be tailored to the specific service and use case. Here are recommended approaches:

1. Text-Based AI Services (Language, Translation, etc.)

Recommended UI Components:

MudBlazor (for Blazor apps):

<MudPaper Elevation="3" Class="pa-4 my-4">
    <MudText Typo="Typo.h6">AI Analysis</MudText>
    <MudText>@_aiResponse</MudText>
    @if (!string.IsNullOrEmpty(_sentiment))
    {
        <MudChip Color="@(_sentiment == "Positive" ? Color.Success : 
                       _sentiment == "Negative" ? Color.Error : Color.Warning)"
                Class="mt-2">
            @_sentiment Sentiment
        </MudChip>
    }
</MudPaper>

For key phrases extraction:

<MudChipSet>
    @foreach (var phrase in _keyPhrases)
    {
        <MudChip>@phrase</MudChip>
    }
</MudChipSet>

2. Computer Vision/Image Analysis

Recommended UI:

<div style="position: relative;">
    <img src="@_imageUrl" style="max-width: 100%;" />
    @foreach (var obj in _detectedObjects)
    {
        <div style="position: absolute; 
                   left: @(obj.BoundingBox.Left * 100)%; 
                   top: @(obj.BoundingBox.Top * 100)%;
                   width: @(obj.BoundingBox.Width * 100)%;
                   height: @(obj.BoundingBox.Height * 100)%;
                   border: 2px solid red;">
            <span style="background: white; padding: 2px;">@obj.ObjectProperty</span>
        </div>
    }
</div>

3. Chat/Conversational AI (Azure OpenAI)

Recommended UI:

<MudContainer MaxWidth="MaxWidth.Medium">
    <MudPaper Elevation="3" Class="pa-4" Style="height: 60vh; overflow-y: auto;">
        @foreach (var message in _chatHistory)
        {
            <MudCard Class="my-2" Elevation="1">
                <MudCardHeader>
                    <MudAvatar>@(message.Role == "user" ? "U" : "AI")</MudAvatar>
                    <MudText Typo="Typo.subtitle2">@message.Role</MudText>
                </MudCardHeader>
                <MudCardContent>
                    <MarkdownString Value="@message.Content" />
                </MudCardContent>
            </MudCard>
        }
    </MudPaper>
    
    <MudTextField @bind-Value="_userMessage" 
                 Label="Type your message" 
                 Variant="Variant.Outlined"
                 FullWidth
                 Class="mt-4">
        <Adornment>
            <MudButton OnClick="SendMessage" 
                      Icon="@Icons.Material.Filled.Send"
                      Disabled="@_isProcessing" />
        </Adornment>
    </MudTextField>
</MudContainer>

4. Form Recognizer/Data Extraction

Recommended UI:

<MudTable Items="@_extractedData" Hover="true">
    <HeaderContent>
        <MudTh>Field</MudTh>
        <MudTh>Value</MudTh>
        <MudTh>Confidence</MudTh>
    </HeaderContent>
    <RowTemplate>
        <MudTd>@context.FieldName</MudTd>
        <MudTd>@context.Value</MudTd>
        <MudTd>
            <MudProgressLinear Value="@(context.Confidence * 100)" 
                              Color="@(context.Confidence > 0.9 ? Color.Success : 
                                     context.Confidence > 0.7 ? Color.Warning : Color.Error)"/>
        </MudTd>
    </RowTemplate>
</MudTable>

5. Custom Decision/Recommendation Services

Recommended UI:

<MudGrid>
    @foreach (var recommendation in _recommendations)
    {
        <MudItem xs="12" sm="6" md="4">
            <MudCard Elevation="5" Class="h-100">
                <MudCardHeader>
                    <MudAvatar Color="Color.Primary">@recommendation.Score.ToString("P0")</MudAvatar>
                    <MudText Typo="Typo.h6">@recommendation.Title</MudText>
                </MudCardHeader>
                <MudCardContent>
                    @recommendation.Description
                </MudCardContent>
                <MudCardActions>
                    <MudButton Variant="Variant.Text" Color="Color.Primary">View Details</MudButton>
                </MudCardActions>
            </MudCard>
        </MudItem>
    }
</MudGrid>

Best Practices for Azure AI UI

Visual Feedback:

Show loading states during API calls

<MudProgressCircular Indeterminate="true" Color="Color.Primary" 
                    Visible="@_isLoading" Class="my-4" />

Error Handling:

@if (!string.IsNullOrEmpty(_errorMessage))
{
    <MudAlert Severity="Severity.Error" Variant="Variant.Filled">
        @_errorMessage
    </MudAlert>
}

Confidence Indicators:

Visualize confidence scores for uncertain predictions

<MudTooltip Text="@($"Confidence: {_confidence:P2}")">
    <MudIcon Icon="@(_confidence > 0.9 ? Icons.Material.Filled.CheckCircle : 
                    _confidence > 0.7 ? Icons.Material.Filled.Warning : 
                    Icons.Material.Filled.Error)"
            Color="@(_confidence > 0.9 ? Color.Success : 
                   _confidence > 0.7 ? Color.Warning : Color.Error)" />
</MudTooltip>

Interactive Exploration:

Allow users to refine/correct AI outputs

<MudTextField @bind-Value="_correctedText" 
             Label="Correct the AI output"
             Visible="@_showCorrectionField" />

Responsive Design:

Ensure UI works across devices

<MudGrid>
    <MudItem xs="12" md="6">
        <!-- Input controls -->
    </MudItem>
    <MudItem xs="12" md="6">
        <!-- AI output -->
    </MudItem>
</MudGrid>

For enterprise applications, consider adding:

• Export capabilities (PDF, CSV)
• Audit trails of AI interactions
• User feedback mechanisms (“Was this helpful?”)
• Explanation components for AI decisions