Recommended UI Approaches for Azure AI Services Output

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

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