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Create application_Prashant Nayak_Dashbot.md
Dashbot project application submission. I have researched and understood the project requirements well then only i am submitting this file.
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doc/proposals/2025/gsoc/application_Prashant Nayak_Dashbot.md
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# GSoC Proposal: Dashbot For APIDash
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## About
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- FULL NAME:PRASHANT NAYAK
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- EMAIL : hydraprashant8@gmail.com
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- PHONE:+917394060751
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- Discord Handle : prashant_1n_80322
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- Github Profile : https://github.com/Prashant1git
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- Time zone : Asia /Jhansi ( IST)
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- Resume Link :
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https://drive.google.com/file/d/1Dt08bxtUQdnUL9UxTiOSt74zE1iwaAxg/view?usp=drivesdk
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## University Info
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- University Name: Bundelkhad university
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-Program: information and technology
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-Year: 2nd Year (2025 Batch)
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-Expected Graduation Date: 2027
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-Motivation & Past Experience
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## 1. FOSS Contributions
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I haven't contributed to FOSS projects yet, but I recently downloaded the APIDash codebase to
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mylocal machine and started exploring it to understand its structure and functionality.
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## 2. Proud Achievement
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One of myproudest achievements was winning a university-level hackathon where I built a fully
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functional Ai supported mobile application within just 24 hours. The event challenged
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participants to solve real-world problems under intense time pressure, and I took it as an
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opportunity to push my limits. Using Python, Flutter and Dart ,I developed a complete app—from
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UI design to backend integration—that impressed the judges with its functionality, performance,
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GSoCProposal: DashbotForAPIDash
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and user experience. This experience not only boosted my confidence as a developer but also
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reinforced my ability to work efficiently under pressure, think creatively, and deliver high-quality
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results within tight deadlines.
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## 3. Challenges that Motivate Me
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Challenges that push me to step out of my comfort zone and learn something new are what
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truly motivate me. Whether it's solving a complex coding bug, building a feature I've never tried
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before, or working under tight deadlines—I see these situations as opportunities to grow. I enjoy
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the process of breaking down problems, finding solutions, and seeing the impact of my work.
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The feeling of overcoming a tough challenge and turning it into a success keeps me driven and
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passionate about what I do as a developer.
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## 4. GSoC Commitment
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I will be working part-time on GSoC, as I am a 2nd-year student and need to balance my studies
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alongside the project.
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## 5. Syncing with Mentors
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Yes, I am open to regular sync-ups with project mentors to ensure steady progress.
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## 6. Interest in APIDash
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APIDash stands out because of its lightweight, Flutter-based architecture, making it a
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highly efficient alternative to tools like Postman. I am particularly excited about the
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potential of expanding its modular design, enhancing API discovery, and integrating AI
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based automation for better API management.
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## 7. Project Improvements
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While APIDash provides a great developer experience, some areas for improvement include:
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Improving UI responsiveness on lower-end devices.
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Expanding API import/export options for better interoperability.
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Enhancing API security validation and error handling mechanisms.
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##GSoCProposal: DashbotForAPIDash
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Project Proposal Information
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Proposal Title: Dashbot for APIDash
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## Conceptual ;
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DashBot is an intelligent assistant built for API Dash that helps developers save time and boost
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productivity by handling common API tasks through natural language. From explaining
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responses and debugging errors to generating documentation, tests, visualizations, and
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frontend integration code (like React or Flutter), DashBot is designed to be both powerful and
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flexible. It features a modular architecture and includes benchmarking tools to help users
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choose the best-performing LLM backend for their needs. The project brings together AI, Python,
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Dart, and Flutter to create a seamless, developer-friendly experience
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# Weekly Timeline ;
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### 4.Weekly Timeline
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| Week | Goals/Activities | Deliverables |
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|------|-------------------------------------------------------------|-----------------------------------------------------------------|
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| 1 | Planning & Setup.| Set up project structure & repo integration Create evaluation metrics for LLM benchmarking. |
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| 2 | Natural Language Input Parsing. | Design prompt engineering systemCreate evaluation dataset for parsing accuracy. |
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| 3 |Explain & Identify Discrepancie. | Implement module to explain API responses Add detection for discrepancies between response & expected schemaBenchmark explanation accuracy across LLMs. |
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| 4 | Debug Based on Status/Error. |Build module to debug based on response codes & messages Integrate context (headers, payload, previous requests)Evaluate LLMs on debugging accuracy with predefined errorsGSoCProposal: DashbotForAPIDash. |
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| 5 | Generate API Documentation. | Design prompt & output template for docs Support OpenAPI + natural descriptions Compare LLMoutput with real-world. |
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| 6 | Generate Tests from API . | Build functionality to create tests (e.g., unit/integration tests)Target frameworks: Postman, pytest, etc.Validate test coverage & LLM consistency. |
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| 7 |Visualizations of API Responses . | Implement module to convert JSON to charts (Bar, Line, Pie)Use plotting libraries like Plotly, Chart.jsd options for user customization. |
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| 8 |Generate Frontend Integration Code . | Generate API integration snippets (React, Flutter Include authentication, headers, error handlingEvaluate code quality and syntactic correctness. |
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| 9 |Modular Agent & Plugin System. |Design modular architecture for DashBot (plug-and-play Each module works independently with shared context/state Add agent loop with memory/context switchingLLMEvaluation FrameworkBuild evaluation UI/CLI to compare model outputs . |
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| 10 |LLMEvaluation Framework. | Build evaluation UI/CLI to compare model outputs Define metrics: accuracy, coherence, latency, token usageDocument how to test with different backends. |
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| 11 | Testing & Documentation. | Unit + integration testing across modules Create usage guide for developers Document each module’s LLM prompt structure. |
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| 12 |Polish, Deploy & Community Feedback . | Polish UI/UX Create demo videos & example use cases Gather feedback from community & iterate TechnicalFlowchart. |
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;
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#FlowChart
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- USERINTERFACE
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Flutter (Desktop)|
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###
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-
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NATURALLANGUAGEINPUT│
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(Flutter ➜ Python API)
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COREPYTHONBACKEND(AGENTSYSTEM) │
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-Intent Parser (LangChain/OpenAI) │
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-Context Manager & Memory
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-TaskRouter (Decides module)
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-EXPLAIN -DEBUGMODULE -DOCGEN
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│RESPONSE -Status/Error -OpenAPI/NL
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-
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TESTGEN PLOTS/VIS FRONTEND
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(pytest) (Plotly) SNIPPETS
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- LLMINTEGRATIONLAYER
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-
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-OpenAI / Claude / Local (via LangChain)
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-Prompts + Output Parsers + Benchmarks
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-EVALUATIONFRAMEWORK
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- Accuracy, Speed, Cost
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- Compare LLMBackends
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- CLI/GUI Benchmarks
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-TESTING LAYER
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- Unit + Integration Tests │
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- Test LLMdeterminism
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- CI/CD Integration
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## Key Technologies by Layer
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Frontend (Flutter/Dart): UI to input natural language and display results.
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Backend (Python): Handles AI agent logic, modular routing, and LLM
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interaction.
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Agent System: Parses intent, routes to correct module (e.g., debugging, doc
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gen).
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LLMs: Handles understanding, generation, explanation tasks.
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Evaluation Framework: Benchmarks LLM outputs across different
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providers.
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GSoCProposal: DashbotForAPIDash
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Testing: Ensures correctness of outputs, stability of agents/modules.
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## Conclusion
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DashBot makes API Dash smarter by using AI to help developers with tasks like
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debugging, writing docs, and generating code using natural language. It's built with
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Python and Flutter, and designed to be flexible and easy to improve. It also helps
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compare different AI models, making it a helpful open-source tool for developers.
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Thisprojectaligns
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wellwithmyskillsandinterests,andIa
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meagertocontributetothe
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APIDashecosyste
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mthroughthisproject
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