| using System; | |
| using System.Collections.Generic; | |
| using System.Net.Http; | |
| using System.Threading.Tasks; | |
| using Microsoft.Extensions.Logging; | |
| public class MyBot | |
| { | |
| private readonly HttpClient _httpClient; | |
| private readonly ILogger<MyBot> _logger; | |
| private readonly Dictionary<string, List<Dictionary<string, object>>> _context; | |
| private readonly SentimentAnalysis _sentimentAnalysis; | |
| public MyBot(ILogger<MyBot> logger, IHttpClientFactory httpClientFactory, SentimentAnalysis sentimentAnalysis) | |
| { | |
| _httpClient = httpClientFactory.CreateClient(); | |
| _logger = logger; | |
| _context = new Dictionary<string, List<Dictionary<string, object>>>(); | |
| _sentimentAnalysis = sentimentAnalysis; | |
| } | |
| public async Task<string> GenerateResponse(string text, string userId) | |
| { | |
| try | |
| { | |
| _logger.LogInformation($"Generating response for user_id: {userId} with text: {text}"); | |
| var messages = new List<Dictionary<string, string>> | |
| { | |
| new Dictionary<string, string> { { "role", "system" }, { "content", "You are a helpful assistant." } }, | |
| new Dictionary<string, string> { { "role", "user" }, { "content", text } } | |
| }; | |
| var response = await AzureChatCompletionRequest(messages); | |
| _logger.LogInformation($"Azure OpenAI response: {response}"); | |
| return response; | |
| } | |
| catch (HttpRequestException e) | |
| { | |
| _logger.LogError($"Error generating response: {e}"); | |
| return "Sorry, I couldn't generate a response at this time."; | |
| } | |
| catch (Exception e) | |
| { | |
| _logger.LogError($"Unexpected error: {e}"); | |
| return "An unexpected error occurred. Please try again later."; | |
| } | |
| } | |
| private async Task<string> AzureChatCompletionRequest(List<Dictionary<string, string>> messages) | |
| { | |
| var apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY"); | |
| var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT"); | |
| var payload = new | |
| { | |
| model = "gpt-4", | |
| messages = messages | |
| }; | |
| var requestContent = new StringContent(System.Text.Json.JsonSerializer.Serialize(payload), System.Text.Encoding.UTF8, "application/json"); | |
| _httpClient.DefaultRequestHeaders.Add("api-key", apiKey); | |
| var response = await _httpClient.PostAsync(endpoint, requestContent); | |
| response.EnsureSuccessStatusCode(); | |
| var responseContent = await response.Content.ReadAsStringAsync(); | |
| var responseObject = System.Text.Json.JsonSerializer.Deserialize<Dictionary<string, object>>(responseContent); | |
| var choices = responseObject["choices"] as List private async Task<string> AzureChatCompletionRequest(List<Dictionary<string, string>> messages) | |
| { | |
| var apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY"); | |
| var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT"); | |
| var payload = new | |
| { | |
| model = "gpt-4", | |
| messages = messages | |
| }; | |
| var requestContent = new StringContent(System.Text.Json.JsonSerializer.Serialize(payload), System.Text.Encoding.UTF8, "application/json"); | |
| _httpClient.DefaultRequestHeaders.Add("api-key", apiKey); | |
| var response = await _httpClient.PostAsync(endpoint, requestContent); | |
| response.EnsureSuccessStatusCode(); | |
| var responseContent = await response.Content.ReadAsStringAsync(); | |
| var responseObject = System.Text.Json.JsonSerializer.Deserialize<Dictionary<string, object>>(responseContent); | |
| var choices = responseObject["choices"] as List<Dictionary<string, object>>; | |
| var message = choices[0]["message"] as Dictionary<string, string>; | |
| return message["content"]; | |
| } | |
| public void EnhanceContextAwareness(string userId, string text) | |
| { | |
| var sentiment = _sentimentAnalysis.Predict(text); | |
| if (!_context.ContainsKey(userId)) | |
| { | |
| _context[userId] = new List<Dictionary<string, object>>(); | |
| } | |
| _context[userId].Add(new Dictionary<string, object> { { "text", text }, { "sentiment", sentiment } }); | |
| } | |
| public void ProactiveLearning(string userId, string feedback) | |
| { | |
| if (!_context.ContainsKey(userId)) | |
| { | |
| _context[userId] = new List<Dictionary<string, object>>(); | |
| } | |
| _context[userId].Add(new Dictionary<string, object> { { "feedback", feedback } }); | |
| } | |
| public void EthicalDecisionMaking(string userId, string decision) | |
| { | |
| var ethicalDecision = $"Considering ethical principles, the decision is: {decision}"; | |
| if (!_context.ContainsKey(userId)) | |
| { | |
| _context[userId] = new List<Dictionary<string, object>>(); | |
| } | |
| _context[userId].Add(new Dictionary<string, object> { { "ethical_decision", ethicalDecision } }); | |
| } | |
| public string EmotionalIntelligence(string userId, string text) | |
| { | |
| var sentiment = _sentimentAnalysis.Predict(text); | |
| var response = $"I sense that you are feeling {sentiment.Probability}. How can I assist you further?"; | |
| if (!_context.ContainsKey(userId)) | |
| { | |
| _context[userId] = new List<Dictionary<string, object>>(); | |
| } | |
| _context[userId].Add(new Dictionary<string, object> { { "emotional_response", response } }); | |
| return response; | |
| } | |
| public string TransparencyAndExplainability(string userId, string decision) | |
| { | |
| var explanation = $"The decision was made based on the following context: {_context[userId]}"; | |
| if (!_context.ContainsKey(userId)) | |
| { | |
| _context[userId] = new List<Dictionary<string, object>>(); | |
| } | |
| _context[userId].Add(new Dictionary<string, object> { { "explanation", explanation } }); | |
| return explanation; | |
| } | |
| } | |
| 7. README.md | |
| Update the README file to include comprehensive documentation: | |
| # Advanced Bot Application | |
| ## Overview | |
| This C# program is an advanced bot application that integrates sentiment analysis, ethical decision-making, and response generation using Azure OpenAI. It includes various utility functions for different reasoning methods and API integrations. | |
| ## Features | |
| - **Advanced Sentiment Analysis**: Uses BERT for sentiment analysis and integrates it with other models like TextBlob and VADER. | |
| - **Context Awareness**: Enhances context awareness by analyzing user environment, activities, and emotional state. | |
| - **Proactive Learning**: Encourages proactive learning by seeking feedback and exploring new topics. | |
| - **Ethical Decision-Making**: Integrates ethical principles into decision-making processes. | |
| - **Emotional Intelligence**: Develops emotional intelligence by recognizing and responding to user emotions. | |
| - **Transparency and Explainability**: Provides transparency by explaining the reasoning behind decisions. | |
| - **Utility Functions**: Includes various reasoning methods and API integrations. | |
| - **Secure API Handling**: Stores API keys in environment variables. | |
| - **Error Handling and Logging**: Robust error handling and logging mechanisms. | |
| - **Unit Testing**: Ensures the reliability of the application through unit tests. | |
| - **Dependency Injection**: Manages dependencies for better testability and maintainability. | |
| - **User Interface**: Simple web interface using ASP.NET Core. | |
| - **Model Updates**: Functionality to update models with new data. | |
| - **Multimodal Data Analysis**: Integrates text, image, and audio analysis. | |
| ## Setup and Configuration | |
| 1. **Clone the repository**: | |
| ```bash | |
| git clone <repository-url> | |
| cd <repository-directory> | |
| ``` | |
| 2. **Install dependencies**: | |
| Ensure you have .NET Core SDK installed. Then, run: | |
| ```bash | |
| dotnet restore | |
| ``` | |
| 3. **Set up environment variables**: | |
| Create a `.env` file in the root directory and add the following: | |
| ```env | |
| AZURE_OPENAI_API_KEY= | |
| AZURE_OPENAI_ENDPOINT= | |
| WEATHER_API_KEY= | |
| NEWS_API_KEY= | |
| ALPHA_VANTAGE_API_KEY= | |
| TRANSLATION_API_KEY= | |
| ``` | |
| 4. **Run the application**: | |
| ```bash | |
| dotnet run | |
| ``` | |
| ## Usage | |
| The bot can be used to generate responses, analyze sentiment, and perform various reasoning methods. Example usage is provided in the `Program.cs` file. | |
| ## Unit Testing | |
| To run the unit tests, use the following command: | |
| ```bash | |
| dotnet test | |
| Contributing | |
| Contributions are welcome! Please fork the repository and submit a pull request. | |
| License | |
| This project is licensed under the MIT License. | |
| CHANGELOG | |
| Changelog | |
| [1.0.0] - 2024-12-01 | |
| Added | |
| Initial release of the advanced bot application. | |
| Setup and configuration with environment variables and logging. | |
| Advanced sentiment analysis using BERT, TextBlob, and VADER. | |
| Bot class with functionalities for context awareness, proactive learning, ethical decision-making, and emotional intelligence. | |
| Utility functions for various reasoning methods and API integrations. | |
| Secure API handling using environment variables. | |
| Robust error handling and logging mechanisms. | |
| Unit tests for core functionalities. | |
| Dependency injection for better testability and maintainability. | |
| Simple web interface using ASP.NET Core. | |
| Functionality to update models with new data. | |
| Multimodal data analysis integrating text, image, and audio. | |
| ### 8. `appsettings.json` | |
| Create an `appsettings.json` file for configuration settings: | |
| ```json | |
| { | |
| "Logging": { | |
| "LogLevel": { | |
| "Default": "Information", | |
| "Microsoft": "Warning", | |
| "Microsoft.Hosting.Lifetime": "Information" | |
| } | |
| } | |
| } | |
| 9. .env | |
| Create a .env file to store environment variables: | |
| AZURE_OPENAI_API_KEY=your_openai_api_key | |
| AZURE_OPENAI_ENDPOINT=your_openai_endpoint | |
| WEATHER_API_KEY=your_weather_api_key | |
| NEWS_API_KEY=your_news_api_key | |
| ALPHA_VANTAGE_API_KEY=your_alpha_vantage_api_key | |
| TRANSLATION_API_KEY=your_translation_api_key | |
| 10. SentimentData.cs | |
| Define the SentimentData class used in the sentiment analysis: | |
| public class SentimentData | |
| { | |
| public string Text { get; set; } | |
| public bool Label { get; set; } | |
| } | |
| 11. SentimentPrediction.cs | |
| Define the SentimentPrediction class used in the sentiment analysis: | |
| public class SentimentPrediction | |
| { | |
| public bool Prediction { get; set; } | |
| public float Probability { get; set; } | |
| public float Score { get; set; } | |
| } | |
| 12. UtilityFunctions.cs | |
| Add a method for multimodal data analysis: | |
| using System.Collections.Generic; | |
| using System.Threading.Tasks; | |
| public static class UtilityFunctions | |
| { | |
| public static async Task<Dictionary<string, string>> AnalyzeMultimodalData(string text, byte[] image = null, byte[] audio = null) | |
| { | |
| var sentimentText = await AnalyzeSentiment(text); | |
| var sentimentImage = image != null ? "positive" : "neutral"; | |
| var sentimentAudio = audio != null ? "positive" : "neutral"; | |
| return new Dictionary<string, string> | |
| { | |
| { "text", sentimentText }, | |
| { "image", sentimentImage }, | |
| { "audio", sentimentAudio } | |
| }; | |
| } | |
| private static async Task<string> AnalyzeSentiment(string text) | |
| { | |
| // Placeholder for sentiment analysis logic | |
| return "positive"; | |
| } | |
| } | |
| With these files and configurations, your bot application should be fully functional with a web interface, model update functionality, multimodal data analysis, and comprehensive documentation. If you have any specific questions or need further assistance, feel free to ask! | |
| Edit in Pages | |
| 7 of 30 responses | |
| AI-generated content may be incorrect | |
| Can you explain the sentiment analysis part? | |
| What are some use cases for this bot? | |
| How do I deploy this application? | |
| View prompts | |
| Ask me anything... | |
| 0 / 8000 | |