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  1. .gitignore +36 -0
  2. KnowledgeNet.excalidraw +4021 -0
  3. backend/app.py +2 -2
  4. backend/crawl_ai.py +45 -0
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backend/app.py CHANGED
@@ -1,9 +1,9 @@
1
  # pip install flask[async] flask-socketio flask-cors
2
  # pip install google-genai beautifulsoup4 selenium newspaper3k lxml_html_clean eventlet
3
- from flask import Flask, request, jsonify
4
  from flask_cors import CORS
5
  from flask_socketio import SocketIO, emit
6
- import os, json, logging
7
  from knet import KNet
8
  from dotenv import load_dotenv
9
 
 
1
  # pip install flask[async] flask-socketio flask-cors
2
  # pip install google-genai beautifulsoup4 selenium newspaper3k lxml_html_clean eventlet
3
+ from flask import Flask, request
4
  from flask_cors import CORS
5
  from flask_socketio import SocketIO, emit
6
+ import json, logging
7
  from knet import KNet
8
  from dotenv import load_dotenv
9
 
backend/crawl_ai.py ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import asyncio
2
+ from crawl4ai import AsyncWebCrawler, CacheMode, BrowserConfig
3
+ import json, sys
4
+ # from base64 import b64decode
5
+
6
+ async def main():
7
+ base_browser = BrowserConfig(
8
+ browser_type="chromium",
9
+ headless=True,
10
+ viewport_width=1920,
11
+ viewport_height=1080,
12
+ accept_downloads=True,
13
+ )
14
+
15
+ # Create an instance of AsyncWebCrawler
16
+ async with AsyncWebCrawler(config=base_browser) as crawler:
17
+ # Run the crawler on a URL
18
+ result = await crawler.arun(url=sys.argv[1], screenshot=False, cache_mode=CacheMode.BYPASS, wait_for="js:() => window.loaded === true")
19
+ # Print the extracted content
20
+ hr = lambda: print(("-" * 80) * 2)
21
+ print(result.markdown)
22
+ hr()
23
+ print(json.dumps(result.media, indent=2))
24
+ hr()
25
+ print(json.dumps(result.links, indent=2))
26
+ hr()
27
+ print(json.dumps(result.downloaded_files, indent=2))
28
+
29
+ # if result.success:
30
+ # # Save screenshot
31
+ # if result.screenshot:
32
+ # with open("screenshot.png", "wb") as f:
33
+ # f.write(b64decode(result.screenshot))
34
+ #
35
+ # # Save PDF
36
+ # if result.pdf:
37
+ # with open("download.pdf", "wb") as f:
38
+ # f.write(result.pdf)
39
+ #
40
+ # print("[OK] PDF & screenshot captured.")
41
+ # else:
42
+ # print("[ERROR]", result.error_message)
43
+
44
+ if __name__ == "__main__":
45
+ asyncio.run(main())
backend/knet.py CHANGED
@@ -28,8 +28,7 @@ class ResearchProgress:
28
  class KNet:
29
  def __init__(self, api_key: Optional[str] = None):
30
  self.api_key = api_key or os.getenv("GOOGLE_API_KEY")
31
- if not self.api_key:
32
- raise ValueError("Google API key is required")
33
 
34
  # Initialize Google GenAI
35
  genai.configure(api_key=self.api_key)
@@ -49,6 +48,7 @@ class KNet:
49
  self.scraper = WebScraper()
50
  self.logger = logging.getLogger(__name__)
51
  self.max_depth = 3
 
52
  self.min_importance_score = 0.6
53
 
54
  self.search_prompt = """Generate 3-5 specific search queries to research the following topic: {topic}
@@ -74,7 +74,7 @@ class KNet:
74
  3. Depth vs breadth tradeoff
75
  4. Information saturation
76
 
77
- Return only: {"decision": true/false}"""
78
 
79
  # Simplified decision schema for branching
80
  self.branch_schema = {
@@ -156,7 +156,7 @@ class KNet:
156
  root_node = ResearchNode(topic)
157
  to_explore = deque([(root_node, 0)]) # (node, depth) pairs
158
  explored_queries = set()
159
- max_branches = self.max_depth * 3
160
 
161
  progress.update(10, "Starting research...")
162
 
@@ -167,10 +167,7 @@ class KNet:
167
  continue
168
 
169
  self.logger.info(f"Exploring: {current_node.query} (Depth: {current_depth})")
170
- progress.update(
171
- 30 + (len(explored_queries) * 50 / max_branches),
172
- f"Exploring: {current_node.query}",
173
- )
174
 
175
  # Search and scrape
176
  current_node.data = self.scraper.search_and_scrape(current_node.query)
@@ -182,9 +179,7 @@ class KNet:
182
  new_branches = self._analyze_and_branch(current_node)
183
  for branch in new_branches:
184
  to_explore.append((branch, current_depth + 1))
185
- self.logger.info(
186
- f"Added {len(new_branches)} new branches at depth {current_depth + 1}"
187
- )
188
 
189
  # Generate final report
190
  progress.update(80, "Generating comprehensive report...")
@@ -334,9 +329,8 @@ Part 2: Detailed Analysis and References
334
  "media": media_content,
335
  "research_tree": build_tree_structure(root_node),
336
  "metadata": {
337
- "total_sources": len(all_research_data),
338
- "max_depth_reached": root_node.depth,
339
- "total_branches": len(root_node.children),
340
  "total_tokens": self.token_count,
341
  },
342
  }
 
28
  class KNet:
29
  def __init__(self, api_key: Optional[str] = None):
30
  self.api_key = api_key or os.getenv("GOOGLE_API_KEY")
31
+ assert self.api_key, "Google API key is required"
 
32
 
33
  # Initialize Google GenAI
34
  genai.configure(api_key=self.api_key)
 
48
  self.scraper = WebScraper()
49
  self.logger = logging.getLogger(__name__)
50
  self.max_depth = 3
51
+ self.max_breadth = 3
52
  self.min_importance_score = 0.6
53
 
54
  self.search_prompt = """Generate 3-5 specific search queries to research the following topic: {topic}
 
74
  3. Depth vs breadth tradeoff
75
  4. Information saturation
76
 
77
+ Return only: decision: true/false"""
78
 
79
  # Simplified decision schema for branching
80
  self.branch_schema = {
 
156
  root_node = ResearchNode(topic)
157
  to_explore = deque([(root_node, 0)]) # (node, depth) pairs
158
  explored_queries = set()
159
+ max_branches = self.max_breadth
160
 
161
  progress.update(10, "Starting research...")
162
 
 
167
  continue
168
 
169
  self.logger.info(f"Exploring: {current_node.query} (Depth: {current_depth})")
170
+ progress.update(30 + int(len(explored_queries) * 50 / max_branches), f"Exploring: {current_node.query}")
 
 
 
171
 
172
  # Search and scrape
173
  current_node.data = self.scraper.search_and_scrape(current_node.query)
 
179
  new_branches = self._analyze_and_branch(current_node)
180
  for branch in new_branches:
181
  to_explore.append((branch, current_depth + 1))
182
+ self.logger.info(f"Added {len(new_branches)} new branches at depth {current_depth + 1}")
 
 
183
 
184
  # Generate final report
185
  progress.update(80, "Generating comprehensive report...")
 
329
  "media": media_content,
330
  "research_tree": build_tree_structure(root_node),
331
  "metadata": {
332
+ "total_sources": root_node.total_children(),
333
+ "max_depth_reached": root_node.max_depth(),
 
334
  "total_tokens": self.token_count,
335
  },
336
  }
backend/output.json ADDED
@@ -0,0 +1,278 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "topic": "What is CUDA programming. How do student's learn it?",
3
+ "timestamp": "2025-02-19T01:42:00.397613",
4
+ "content": "# Research Report: What is CUDA programming. How do student's learn it?
5
+
6
+ Part 1: Overview and Key Findings
7
+ --------------------------------
8
+ ## Part 1: Overview and Key Findings - CUDA Programming and Student Learning
9
+
10
+ **1. Executive Summary**
11
+
12
+ This research report investigates CUDA (Compute Unified Device Architecture) programming, a parallel computing platform and programming model developed by NVIDIA, and explores how students learn this complex technology. CUDA allows developers to leverage the power of Graphics Processing Units (GPUs) for general-purpose computing, significantly accelerating computationally intensive tasks. This report examines the core concepts of CUDA, the challenges students face in learning it, and identifies key findings regarding effective learning strategies and common misconceptions.
13
+
14
+ **2. Key Findings**
15
+
16
+ * **Steep Learning Curve:** Students consistently report a steep learning curve associated with CUDA programming. This is primarily due to the need to understand parallel programming concepts, GPU architecture, and the CUDA programming model itself, which introduces new syntax and memory management complexities.
17
+ * **Conceptual Understanding is Crucial:** A strong foundation in parallel computing principles, including threads, blocks, grids, and memory hierarchies, is essential for successful CUDA programming. Students who struggle with these concepts often face significant difficulties in writing and debugging CUDA code.
18
+ * **Practical Application is Key:** Hands-on experience and practical application are critical for mastering CUDA. Students benefit significantly from working on real-world projects and examples that demonstrate the benefits of GPU acceleration.
19
+ * **Debugging is a Major Challenge:** Debugging CUDA code is often more challenging than debugging traditional CPU code. The parallel nature of CUDA programs introduces complexities in identifying and resolving errors. Effective debugging tools and techniques are crucial for student success.
20
+ * **Resource Availability Matters:** The availability of high-quality learning resources, including tutorials, documentation, example code, and online communities, significantly impacts student learning. Access to these resources can help students overcome challenges and accelerate their learning process.
21
+
22
+
23
+ Part 2: Detailed Analysis and References
24
+ --------------------------------------
25
+ ## Part 2: Detailed Analysis
26
+
27
+ **1. Detailed Analysis (Expanding on Key Findings)**
28
+
29
+ This section delves deeper into the key findings outlined in Part 1, providing a more nuanced understanding of the challenges and opportunities associated with CUDA programming and student learning.
30
+
31
+ * **Steep Learning Curve: Deconstructing the Complexity:** The perceived steep learning curve isn't solely attributable to the novelty of CUDA; it's a confluence of several factors. Firstly, students often lack a solid understanding of parallel processing fundamentals. This includes concepts like Amdahl's Law, which highlights the limitations of parallelization, and the different types of parallelism (data parallelism vs. task parallelism). Secondly, the CUDA programming model itself introduces new abstractions and terminology. The concepts of threads, blocks, and grids, while conceptually related to parallel processing, require students to think about code execution in a fundamentally different way. Furthermore, the CUDA runtime API, with its functions for device initialization, memory allocation (host-to-device and device-to-host transfers), kernel launch configuration, and synchronization, presents a significant learning hurdle. The need to manage memory explicitly, a departure from the more abstracted memory management in many modern programming languages, adds another layer of complexity. Finally, the debugging process, as mentioned later, contributes to the perceived difficulty. Students often struggle to pinpoint the source of errors in parallel code, leading to frustration and prolonged debugging sessions.
32
+
33
+ * **Conceptual Understanding is Crucial: Bridging the Theory-Practice Gap:** While practical application is essential (as discussed below), a robust understanding of the underlying concepts is paramount. Students who grasp the principles of thread synchronization (e.g., using `__syncthreads()`), memory access patterns (e.g., coalesced memory access), and the impact of shared memory on performance are better equipped to write efficient and correct CUDA code. Conversely, students who treat CUDA as a black box, blindly applying code snippets without understanding the underlying mechanisms, often encounter performance bottlenecks and subtle bugs. Effective teaching strategies should emphasize the theoretical underpinnings of parallel computing, using visual aids (e.g., diagrams illustrating thread execution and memory hierarchies) and analogies to help students grasp these abstract concepts. Furthermore, assignments should encourage students to analyze the performance of their CUDA kernels and to experiment with different optimization techniques, thereby reinforcing the connection between theory and practice.
34
+
35
+ * **Practical Application is Key: Beyond Toy Examples:** The importance of hands-on experience cannot be overstated. However, the nature of these practical applications is crucial. While introductory tutorials often focus on simple examples like vector addition or matrix multiplication, these examples, while helpful for introducing basic syntax, may not fully capture the complexities of real-world CUDA programming. Students benefit from working on projects that involve more complex algorithms and data structures, such as image processing, scientific simulations, or machine learning applications. These projects expose students to the challenges of optimizing CUDA code for performance, dealing with large datasets, and handling complex data dependencies. Furthermore, projects that require students to integrate CUDA code with existing CPU-based applications provide valuable experience in heterogeneous computing environments. The use of profiling tools (e.g., NVIDIA Nsight Systems, NVIDIA Nsight Compute) is also crucial to allow students to analyze the performance of their CUDA code and identify areas for optimization.
36
+
37
+ * **Debugging is a Major Challenge: Tools and Techniques for Parallel Code:** Debugging CUDA code is inherently more complex than debugging sequential code. The non-deterministic nature of parallel execution, the potential for race conditions and deadlocks, and the lack of readily available debugging tools for all aspects of CUDA code (e.g., kernel execution) contribute to this difficulty. While NVIDIA provides debugging tools like `cuda-gdb`, these tools can be challenging to use, especially for beginners. Effective debugging strategies include: (1) **Careful code design:** minimizing the potential for race conditions and deadlocks through proper thread synchronization and memory access patterns; (2) **Strategic use of print statements:** strategically placing print statements within the kernel to track the values of variables and the execution flow (although this can impact performance); (3) **Verification of host-side code:** ensuring that the host-side code correctly sets up the data and launches the kernels; (4) **Use of memory checking tools:** utilizing tools like `cuda-memcheck` to detect memory errors and race conditions; (5) **Profiling to identify performance bottlenecks:** using profiling tools to identify areas of the code that are consuming the most time. Training students in these debugging techniques is essential for their success in CUDA programming.
38
+
39
+ * **Resource Availability Matters: Curating a Learning Ecosystem:** The availability of high-quality learning resources is critical for student success. Beyond the official NVIDIA documentation, which can be dense and technical, students benefit from access to a variety of resources, including: (1) **Tutorials and online courses:** well-structured tutorials that cover the core concepts of CUDA programming, with clear explanations, code examples, and exercises; (2) **Example code:** a library of well-documented example code that demonstrates various CUDA programming techniques and algorithms; (3) **Online communities and forums:** active online communities where students can ask questions, share their experiences, and get help from experienced CUDA programmers; (4) **Books and academic papers:** textbooks and research papers that provide a deeper understanding of the theoretical foundations of parallel computing and CUDA programming; (5) **Integrated Development Environments (IDEs):** IDEs that provide features such as code completion, debugging support, and profiling tools. The quality and accessibility of these resources can significantly impact student learning outcomes. Instructors should curate a list of recommended resources and provide guidance on how to use them effectively.
40
+
41
+ **2. Related Topics and Branches**
42
+
43
+ CUDA programming is intricately linked to several related topics and branches of computer science, including:
44
+
45
+ * **Parallel Computing Fundamentals:** This encompasses the core principles of parallel processing, including Amdahl's Law, Gustafson's Law, Flynn's Taxonomy, and the different models of parallel computation (e.g., shared-memory, distributed-memory). A strong understanding of these concepts is essential for designing and optimizing CUDA applications.
46
+ * **GPU Architecture and Organization:** Understanding the architecture of GPUs, including the organization of streaming multiprocessors (SMs), CUDA cores, memory hierarchies (global, shared, local, registers), and the memory access patterns, is crucial for writing efficient CUDA code. This knowledge allows developers to optimize their code for the specific hardware characteristics of the GPU.
47
+ * **Computer Architecture:** CUDA programming benefits from a foundational understanding of computer architecture, including topics like memory organization, cache coherence, and instruction-level parallelism. This knowledge helps developers understand the performance implications of their code and make informed decisions about optimization strategies.
48
+ * **High-Performance Computing (HPC):** CUDA is a key technology in HPC, enabling the acceleration of computationally intensive tasks in scientific simulations, data analysis, and other domains. Understanding the principles of HPC, including parallel algorithms, numerical methods, and performance optimization techniques, is essential for developing effective CUDA applications in this field.
49
+ * **Machine Learning and Deep Learning:** GPUs, and CUDA in particular, have revolutionized the field of machine learning and deep learning. CUDA provides the computational power needed to train complex neural networks and accelerate the inference process. Understanding the principles of machine learning, including the different types of models, training algorithms, and evaluation metrics, is essential for developing CUDA applications in this domain.
50
+ * **Computer Graphics:** GPUs were originally designed for computer graphics, and CUDA can be used to accelerate various graphics-related tasks, such as ray tracing, rendering, and image processing. Understanding the principles of computer graphics, including the rendering pipeline, shading models, and texture mapping, is beneficial for developing CUDA applications in this area.
51
+ * **Heterogeneous Computing:** CUDA is a key technology for heterogeneous computing, which involves using a combination of CPUs and GPUs to accelerate applications. Understanding the principles of heterogeneous computing, including the challenges of data transfer and synchronization between CPUs and GPUs, is essential for developing effective CUDA applications in this environment.
52
+
53
+ **3. Sources and References**
54
+
55
+ This section lists the sources and references used in this report. The list is not exhaustive but represents key resources consulted during the research and writing process.
56
+
57
+ * **NVIDIA CUDA Documentation:**
58
+ * NVIDIA. *CUDA C Programming Guide*. [https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html) (Accessed October 26, 2023).
59
+ * NVIDIA. *CUDA Toolkit Documentation*. [https://docs.nvidia.com/cuda/index.html](https://docs.nvidia.com/cuda/index.html) (Accessed October 26, 2023).
60
+ * **Books:**
61
+ * Farber, R. (2013). *CUDA Application Design and Development*. Morgan Kaufmann.
62
+ * Sanders, J., & Kandrot, E. (2010). *CUDA by Example: An Introduction to General-Purpose GPU Programming*. Addison-Wesley Professional.
63
+ * Kirk, D. B., & Hwu, W. W. (2010). *Programming Massively Parallel Processors: A Hands-on Approach*. Morgan Kaufmann.
64
+ * **Academic Papers and Research Articles:**
65
+ * (Example: Insert relevant academic papers here. These would be papers discussing CUDA learning, student challenges, and effective teaching strategies. These would need to be found through a literature search.)
66
+ * **Online Resources:**
67
+ * Stack Overflow (search for CUDA-related questions and answers)
68
+ * NVIDIA Developer Forums
69
+ * Online CUDA tutorials and courses (e.g., Coursera, Udacity, edX)
70
+ * **Industry Reports:**
71
+ * (Example: Insert relevant industry reports here, if any, discussing GPU adoption, market trends, etc.)
72
+
73
+ **(Note: The list of academic papers and industry reports is intentionally left incomplete. A thorough literature search would be required to populate this section with relevant and specific citations. The URLs for the NVIDIA documentation are provided as examples, and the \"Accessed\" dates should be updated to reflect the actual date of access.)**
74
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+ "title": "An Even Easier Introduction to CUDA",
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216
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220
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221
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225
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226
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232
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+ "query": "What are the common challenges faced by students when learning CUDA programming, and how can these be addressed through improved teaching methodologies, tools, or resources?",
241
+ "importance": 0.8,
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+ "depth": 1,
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+ "children": [
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+ "query": "Investigate the effectiveness of different debugging and profiling tools in addressing common CUDA programming challenges, comparing their usability and impact on student learning outcomes.",
246
+ "importance": 0.9,
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+ "importance": 0.8,
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backend/research_node.py CHANGED
@@ -24,3 +24,13 @@ class ResearchNode:
24
  current = current.parent
25
  path.append(current.query)
26
  return list(reversed(path))
 
 
 
 
 
 
 
 
 
 
 
24
  current = current.parent
25
  path.append(current.query)
26
  return list(reversed(path))
27
+
28
+ def max_depth(self) -> int:
29
+ if not self.children:
30
+ return self.depth
31
+ return max([child.max_depth() for child in self.children])
32
+
33
+ def total_children(self) -> int:
34
+ if not self.children:
35
+ return 0
36
+ return len(self.children) + sum([child.total_children() for child in self.children])
backend/scraper.py CHANGED
@@ -27,9 +27,7 @@ class WebScraper:
27
  def cleanup(self):
28
  pass
29
 
30
- def search_and_scrape(
31
- self, query: str, num_sites: int = 3
32
- ) -> List[Dict[str, Any]]:
33
  self.logger.info(f"Starting search for: {query}")
34
  search_results = self._google_search(query, num_sites)
35
  self.logger.info(f"Found {len(search_results)} search results")
@@ -120,9 +118,7 @@ class WebScraper:
120
  self.logger.error(f"Scraping error for {url}: {str(e)}")
121
  return None
122
 
123
- def _merge_extraction_results(
124
- self, news_data: Dict, selenium_data: Dict
125
- ) -> Dict[str, Any]:
126
  merged = selenium_data.copy()
127
 
128
  if news_data:
 
27
  def cleanup(self):
28
  pass
29
 
30
+ def search_and_scrape(self, query: str, num_sites: int = 3) -> List[Dict[str, Any]]:
 
 
31
  self.logger.info(f"Starting search for: {query}")
32
  search_results = self._google_search(query, num_sites)
33
  self.logger.info(f"Found {len(search_results)} search results")
 
118
  self.logger.error(f"Scraping error for {url}: {str(e)}")
119
  return None
120
 
121
+ def _merge_extraction_results(self, news_data: Dict, selenium_data: Dict) -> Dict[str, Any]:
 
 
122
  merged = selenium_data.copy()
123
 
124
  if news_data:
frontend/README.md ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app).
2
+
3
+ ## Getting Started
4
+
5
+ First, run the development server:
6
+
7
+ ```bash
8
+ npm run dev
9
+ # or
10
+ yarn dev
11
+ # or
12
+ pnpm dev
13
+ # or
14
+ bun dev
15
+ ```
16
+
17
+ Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
18
+
19
+ You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
20
+
21
+ This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel.
22
+
23
+ ## Learn More
24
+
25
+ To learn more about Next.js, take a look at the following resources:
26
+
27
+ - [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
28
+ - [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
29
+
30
+ You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome!
31
+
32
+ ## Deploy on Vercel
33
+
34
+ The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
35
+
36
+ Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details.
frontend/bun.lock ADDED
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1
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frontend/next.config.mjs ADDED
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1
+ /** @type {import('next').NextConfig} */
2
+ const nextConfig = {};
3
+
4
+ export default nextConfig;
frontend/package.json ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
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3
+ "version": "0.1.0",
4
+ "private": true,
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+ "scripts": {
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+ "dev": "next dev",
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+ "build": "next build",
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+ "start": "next start",
9
+ "lint": "next lint"
10
+ },
11
+ "dependencies": {
12
+ "react": "^18",
13
+ "react-dom": "^18",
14
+ "next": "14.2.24"
15
+ },
16
+ "devDependencies": {
17
+ "typescript": "^5",
18
+ "@types/node": "^20",
19
+ "@types/react": "^18",
20
+ "@types/react-dom": "^18",
21
+ "postcss": "^8",
22
+ "tailwindcss": "^3.4.1"
23
+ }
24
+ }
frontend/postcss.config.mjs ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ /** @type {import('postcss-load-config').Config} */
2
+ const config = {
3
+ plugins: {
4
+ tailwindcss: {},
5
+ },
6
+ };
7
+
8
+ export default config;
frontend/src/app/favicon.ico ADDED
frontend/src/app/fonts/GeistMonoVF.woff ADDED
Binary file (67.9 kB). View file
 
frontend/src/app/fonts/GeistVF.woff ADDED
Binary file (66.3 kB). View file
 
frontend/src/app/globals.css ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ @tailwind base;
2
+ @tailwind components;
3
+ @tailwind utilities;
4
+
5
+ :root {
6
+ --background: #ffffff;
7
+ --foreground: #171717;
8
+ }
9
+
10
+ @media (prefers-color-scheme: dark) {
11
+ :root {
12
+ --background: #0a0a0a;
13
+ --foreground: #ededed;
14
+ }
15
+ }
16
+
17
+ body {
18
+ color: var(--foreground);
19
+ background: var(--background);
20
+ font-family: Arial, Helvetica, sans-serif;
21
+ }
22
+
23
+ @layer utilities {
24
+ .text-balance {
25
+ text-wrap: balance;
26
+ }
27
+ }
frontend/src/app/layout.tsx ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import type { Metadata } from "next";
2
+ import localFont from "next/font/local";
3
+ import "./globals.css";
4
+
5
+ const geistSans = localFont({
6
+ src: "./fonts/GeistVF.woff",
7
+ variable: "--font-geist-sans",
8
+ weight: "100 900",
9
+ });
10
+ const geistMono = localFont({
11
+ src: "./fonts/GeistMonoVF.woff",
12
+ variable: "--font-geist-mono",
13
+ weight: "100 900",
14
+ });
15
+
16
+ export const metadata: Metadata = {
17
+ title: "Create Next App",
18
+ description: "Generated by create next app",
19
+ };
20
+
21
+ export default function RootLayout({
22
+ children,
23
+ }: Readonly<{
24
+ children: React.ReactNode;
25
+ }>) {
26
+ return (
27
+ <html lang="en">
28
+ <body
29
+ className={`${geistSans.variable} ${geistMono.variable} antialiased`}
30
+ >
31
+ {children}
32
+ </body>
33
+ </html>
34
+ );
35
+ }
frontend/src/app/page.tsx ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import Image from "next/image";
2
+
3
+ export default function Home() {
4
+ return (
5
+ <div className="grid grid-rows-[20px_1fr_20px] items-center justify-items-center min-h-screen p-8 pb-20 gap-16 sm:p-20 font-[family-name:var(--font-geist-sans)]">
6
+ <main className="flex flex-col gap-8 row-start-2 items-center sm:items-start">
7
+ <Image
8
+ className="dark:invert"
9
+ src="https://nextjs.org/icons/next.svg"
10
+ alt="Next.js logo"
11
+ width={180}
12
+ height={38}
13
+ priority
14
+ />
15
+ <ol className="list-inside list-decimal text-sm text-center sm:text-left font-[family-name:var(--font-geist-mono)]">
16
+ <li className="mb-2">
17
+ Get started by editing{" "}
18
+ <code className="bg-black/[.05] dark:bg-white/[.06] px-1 py-0.5 rounded font-semibold">
19
+ src/app/page.tsx
20
+ </code>
21
+ .
22
+ </li>
23
+ <li>Save and see your changes instantly.</li>
24
+ </ol>
25
+
26
+ <div className="flex gap-4 items-center flex-col sm:flex-row">
27
+ <a
28
+ className="rounded-full border border-solid border-transparent transition-colors flex items-center justify-center bg-foreground text-background gap-2 hover:bg-[#383838] dark:hover:bg-[#ccc] text-sm sm:text-base h-10 sm:h-12 px-4 sm:px-5"
29
+ href="https://vercel.com/new?utm_source=create-next-app&utm_medium=appdir-template-tw&utm_campaign=create-next-app"
30
+ target="_blank"
31
+ rel="noopener noreferrer"
32
+ >
33
+ <Image
34
+ className="dark:invert"
35
+ src="https://nextjs.org/icons/vercel.svg"
36
+ alt="Vercel logomark"
37
+ width={20}
38
+ height={20}
39
+ />
40
+ Deploy now
41
+ </a>
42
+ <a
43
+ className="rounded-full border border-solid border-black/[.08] dark:border-white/[.145] transition-colors flex items-center justify-center hover:bg-[#f2f2f2] dark:hover:bg-[#1a1a1a] hover:border-transparent text-sm sm:text-base h-10 sm:h-12 px-4 sm:px-5 sm:min-w-44"
44
+ href="https://nextjs.org/docs?utm_source=create-next-app&utm_medium=appdir-template-tw&utm_campaign=create-next-app"
45
+ target="_blank"
46
+ rel="noopener noreferrer"
47
+ >
48
+ Read our docs
49
+ </a>
50
+ </div>
51
+ </main>
52
+ <footer className="row-start-3 flex gap-6 flex-wrap items-center justify-center">
53
+ <a
54
+ className="flex items-center gap-2 hover:underline hover:underline-offset-4"
55
+ href="https://nextjs.org/learn?utm_source=create-next-app&utm_medium=appdir-template-tw&utm_campaign=create-next-app"
56
+ target="_blank"
57
+ rel="noopener noreferrer"
58
+ >
59
+ <Image
60
+ aria-hidden
61
+ src="https://nextjs.org/icons/file.svg"
62
+ alt="File icon"
63
+ width={16}
64
+ height={16}
65
+ />
66
+ Learn
67
+ </a>
68
+ <a
69
+ className="flex items-center gap-2 hover:underline hover:underline-offset-4"
70
+ href="https://vercel.com/templates?framework=next.js&utm_source=create-next-app&utm_medium=appdir-template-tw&utm_campaign=create-next-app"
71
+ target="_blank"
72
+ rel="noopener noreferrer"
73
+ >
74
+ <Image
75
+ aria-hidden
76
+ src="https://nextjs.org/icons/window.svg"
77
+ alt="Window icon"
78
+ width={16}
79
+ height={16}
80
+ />
81
+ Examples
82
+ </a>
83
+ <a
84
+ className="flex items-center gap-2 hover:underline hover:underline-offset-4"
85
+ href="https://nextjs.org?utm_source=create-next-app&utm_medium=appdir-template-tw&utm_campaign=create-next-app"
86
+ target="_blank"
87
+ rel="noopener noreferrer"
88
+ >
89
+ <Image
90
+ aria-hidden
91
+ src="https://nextjs.org/icons/globe.svg"
92
+ alt="Globe icon"
93
+ width={16}
94
+ height={16}
95
+ />
96
+ Go to nextjs.org →
97
+ </a>
98
+ </footer>
99
+ </div>
100
+ );
101
+ }
frontend/tailwind.config.ts ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import type { Config } from "tailwindcss";
2
+
3
+ const config: Config = {
4
+ content: [
5
+ "./src/pages/**/*.{js,ts,jsx,tsx,mdx}",
6
+ "./src/components/**/*.{js,ts,jsx,tsx,mdx}",
7
+ "./src/app/**/*.{js,ts,jsx,tsx,mdx}",
8
+ ],
9
+ theme: {
10
+ extend: {
11
+ colors: {
12
+ background: "var(--background)",
13
+ foreground: "var(--foreground)",
14
+ },
15
+ },
16
+ },
17
+ plugins: [],
18
+ };
19
+ export default config;
frontend/tsconfig.json ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "compilerOptions": {
3
+ "lib": ["dom", "dom.iterable", "esnext"],
4
+ "allowJs": true,
5
+ "skipLibCheck": true,
6
+ "strict": true,
7
+ "noEmit": true,
8
+ "esModuleInterop": true,
9
+ "module": "esnext",
10
+ "moduleResolution": "bundler",
11
+ "resolveJsonModule": true,
12
+ "isolatedModules": true,
13
+ "jsx": "preserve",
14
+ "incremental": true,
15
+ "plugins": [
16
+ {
17
+ "name": "next"
18
+ }
19
+ ],
20
+ "paths": {
21
+ "@/*": ["./src/*"]
22
+ }
23
+ },
24
+ "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
25
+ "exclude": ["node_modules"]
26
+ }