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  1. # Of course\! Creating a web application from your powerful "Chiron" agent is an excellent next step. Your notebook is well-structured, which makes this process much clearer.
  2.  
  3. # Here is a detailed, step-by-step plan to create a full-stack application with a FastAPI backend and a v0-generated React/Next.js frontend, based *exactly* on the logic in your `.ipynb` file.
  4.  
  5. # -----
  6.  
  7. # ### 🏛️ **High-Level Architecture**
  8.  
  9. # Your final application will have two main parts:
  10.  
  11. # 1.  **Backend (FastAPI):** A Python web server that will host your LangGraph agent. It will expose API endpoints that the frontend can call to start a session, submit answers, and get results. It will manage the state of each user's learning session.
  12. # 2.  **Frontend (Next.js + v0):** A modern, interactive web interface where the user can input their topic, edit checkpoints, answer questions, and see the feedback. This will replace the `ipywidgets` and `input()` calls from your notebook.
  13.  
  14. # -----
  15.  
  16. # ### **Phase 1: 🔧 Backend Development (FastAPI)**
  17.  
  18. # The goal here is to wrap your existing Python code in a web server. FastAPI is perfect for this due to its speed, Pydantic integration (which you already use), and ease of use.
  19.  
  20. # #### **Step 1: Set Up Your Project**
  21.  
  22. # 1.  Create a new project directory.
  23. # 2.  Install necessary libraries:
  24. #     ```bash
  25. #     pip install fastapi "uvicorn[standard]"
  26. #     # You'll also need all the libraries from your notebook
  27. #     pip install langchain-community langchain-openai langgraph pydantic python-dotenv semantic-chunkers semantic-router tavily-python
  28. #     ```
  29. # 3.  Create a file named `main.py`. Copy all the Python code from your notebook into this file (imports, Pydantic models, helper functions, prompt configurations, `ContextStore`, core functions, and the graph definition).
  30.  
  31. # #### **Step 2: Refactor Your Code for an API**
  32.  
  33. # In `main.py`, you'll make these key changes:
  34.  
  35. # 1.  **Initialize FastAPI and the Graph:** At the top of your file, after the imports, initialize FastAPI and your LangGraph agent. This ensures the agent is loaded only once when the server starts.
  36.  
  37. #     ```python
  38. #     # ... your imports
  39. #     from fastapi import FastAPI
  40. #     from fastapi.middleware.cors import CORSMiddleware # To allow frontend requests
  41.  
  42. #     app = FastAPI()
  43.  
  44. #     # Add CORS middleware to allow your frontend to talk to this backend
  45. #     app.add_middleware(
  46. #         CORSMiddleware,
  47. #         allow_origins=["*"],  # For development, you can restrict this later
  48. #         allow_credentials=True,
  49. #         allow_methods=["*"],
  50. #         allow_headers=["*"],
  51. #     )
  52.  
  53. #     # ... all your Pydantic models, helper functions, prompts ...
  54.  
  55. #     # Initialize the graph and its dependencies globally
  56. #     tavily_search = TavilySearchResults(max_results=3)
  57. #     llm = ChatOpenAI(model="gpt-4o", temperature=0)
  58. #     embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
  59. #     memory = MemorySaver()
  60. #     context_store = ContextStore()
  61.  
  62. #     # ... the graph definition code from your notebook ...
  63. #     graph = searcher.compile(interrupt_after=["generate_checkpoints"], interrupt_before=["user_answer"], checkpointer=memory)
  64.  
  65. #     ```
  66.  
  67. # 2.  **Remove Notebook-Specific Code:** Delete the cells related to `ipywidgets`, `display(Image(...))`, and the example usage at the end (the part with `initial_input`, `thread`, and `input()`).
  68.  
  69. # #### **Step 3: Create API Endpoints**
  70.  
  71. # You need to create endpoints that mirror the interactive steps of your notebook. The `thread_id` will be crucial for maintaining each user's session state.
  72.  
  73. # 1.  **Endpoint to Start a Session:** This replaces running the first few cells. It will take the user's topic and notes, run the graph to generate checkpoints, and return them along with a session ID.
  74.  
  75. #     ```python
  76. #     # Add to main.py
  77. #     from pydantic import BaseModel
  78.  
  79. #     class StartRequest(BaseModel):
  80. #         topic: str
  81. #         goals: List[str]
  82. #         context: Optional[str] = ""
  83.  
  84. #     @app.post("/start_session")
  85. #     def start_session(request: StartRequest):
  86. #         thread_id = str(uuid.uuid4())
  87. #         thread = {"configurable": {"thread_id": thread_id}}
  88.  
  89. #         initial_input = {
  90. #             "topic": request.topic,
  91. #             "goals": request.goals,
  92. #             "context": request.context,
  93. #             "current_checkpoint": 0
  94. #         }
  95.  
  96. #         # Stream up to the first interrupt (generate_checkpoints)
  97. #         for event in graph.stream(initial_input, thread, stream_mode="values"):
  98. #             checkpoints = event.get("checkpoints")
  99. #             if checkpoints:
  100. #                 return {"thread_id": thread_id, "checkpoints": checkpoints}
  101. #         return {"error": "Could not generate checkpoints"}, 500
  102. #     ```
  103.  
  104. # 2.  **Endpoint to Update Checkpoints & Get First Question:** This replaces the `graph.update_state` call after the widget. It takes the (potentially edited) checkpoints, updates the state, and runs the graph to the *next* interrupt (`user_answer`) to get the first question.
  105.  
  106. #     ```python
  107. #     # Add to main.py
  108. #     class UpdateRequest(BaseModel):
  109. #         thread_id: str
  110. #         checkpoints: Checkpoints # The Pydantic model you already defined
  111.  
  112. #     @app.post("/update_checkpoints")
  113. #     def update_checkpoints(request: UpdateRequest):
  114. #         thread = {"configurable": {"thread_id": request.thread_id}}
  115.  
  116. #         # Update the state with the user-approved checkpoints
  117. #         graph.update_state(thread, {"checkpoints": request.checkpoints}, as_node="generate_checkpoints")
  118.  
  119. #         # Continue the graph stream to get the first question
  120. #         for event in graph.stream(None, thread, stream_mode="values"):
  121. #             question = event.get("current_question")
  122. #             if question:
  123. #                 return {"question": question}
  124. #         return {"error": "Could not generate a question"}, 500
  125. #     ```
  126.  
  127. # 3.  **Endpoint to Submit an Answer:** This is the main interactive loop. It takes an answer, runs the graph, and returns the verification, any teaching material, and the next question.
  128.  
  129. #     ```python
  130. #     # Add to main.py
  131. #     class AnswerRequest(BaseModel):
  132. #         thread_id: str
  133. #         answer: str
  134.  
  135. #     @app.post("/submit_answer")
  136. #     def submit_answer(request: AnswerRequest):
  137. #         thread = {"configurable": {"thread_id": request.thread_id}}
  138.  
  139. #         # Provide the user's answer to the graph
  140. #         graph.update_state(thread, {"current_answer": request.answer}, as_node="user_answer")
  141.  
  142. #         response = {}
  143. #         # Stream the graph to its next conclusion or interrupt
  144. #         for event in graph.stream(None, thread, stream_mode="values"):
  145. #             if "verifications" in event:
  146. #                 response["verifications"] = event["verifications"]
  147. #             if "teachings" in event:
  148. #                 response["teachings"] = event["teachings"]
  149. #             if "current_question" in event:
  150. #                 response["question"] = event["current_question"]
  151.  
  152. #         # Check if the graph has finished
  153. #         if graph.get_state(thread).next == ():
  154. #             response["is_complete"] = True
  155.  
  156. #         return response
  157. #     ```
  158.  
  159. # #### **Step 4: Run the Backend Server**
  160.  
  161. # From your terminal, run:
  162.  
  163. # ```bash
  164. # uvicorn main:app --reload
  165. # ```
  166.  
  167. # Your backend is now running, likely at `http://127.0.0.1:8000`.
  168.  
  169. # -----
  170.  
  171. # ### **Phase 2: 🎨 Frontend Design & Generation (v0.dev)**
  172.  
  173. # Now, let's design the UI components you'll need. You can use these prompts in [v0.dev](https://content-available-to-author-only.dev) to generate the React/Next.js code.
  174.  
  175. # #### **Component 1: The Initial Setup Screen**
  176.  
  177. #   * **v0 Prompt:** *"Create a clean, centered form for a learning agent. It needs a prominent heading 'Chiron: Your Personal AI Tutor'. Below that, an input field for 'Learning Topic', another for 'Learning Goals', and a large textarea for 'Your Notes (Optional)'. Finish with a primary-colored button that says 'Generate Learning Plan'."*
  178.  
  179. # #### **Component 2: The Checkpoint Editor**
  180.  
  181. # This is the most complex component, mirroring your `ipywidget`.
  182.  
  183. #   * **v0 Prompt:** *"Design an interface to edit a learning plan. Show a list of 'Checkpoints'. Each checkpoint is a card with a 'Description' textarea, a list of 'Success Criteria' (these should be editable text inputs, with add/remove buttons for each criterion), and a 'Verification Method' textarea. At the bottom of the page, have a button 'Lock In Plan and Start Learning'."*
  184. #       * **Note:** The dynamic "add/remove" functionality will likely require you to manually wire it up in the React code after generation, but v0 will give you a great visual and structural starting point.
  185.  
  186. # #### **Component 3: The Main Learning Interface**
  187.  
  188. # This is where the user answers questions and gets feedback.
  189.  
  190. #   * **v0 Prompt:** *"Create a two-column layout for an AI tutor. The left column is a chat interface. It should display the tutor's questions and the user's answers. At the bottom is a text input for the user to type their answer and a 'Submit' button. The right column is for feedback. It should have a card for 'Verification Results' with a progress bar for understanding level, a section for feedback, and a list of suggestions. Below that, show another card for 'Feynman Explanation' with sections for 'Simplified Explanation', 'Key Concepts', and 'Analogies'."*
  191.  
  192. # -----
  193.  
  194. # ### **Phase 3: 🔗 Frontend-Backend Integration**
  195.  
  196. # This is where you connect the v0-generated React components to your FastAPI backend.
  197.  
  198. # 1.  **State Management:** In your Next.js app, use React's `useState` hook to manage the application state, including `thread_id`, `checkpoints`, `currentQuestion`, `chatHistory`, `verificationResult`, etc.
  199.  
  200. # 2.  **API Calls:** Use `fetch` or a library like `axios` to make HTTP requests to your FastAPI endpoints.
  201.  
  202. #       * **Flow 1 (Setup):**
  203.  
  204. #           * User fills the setup form and clicks "Generate Learning Plan".
  205. #           * Call `POST /start_session` with the topic, goals, and context.
  206. #           * On success, store the returned `thread_id` and `checkpoints` in your component's state.
  207. #           * Hide the setup screen and show the Checkpoint Editor, populated with the data you just received.
  208.  
  209. #       * **Flow 2 (Checkpoint Approval):**
  210.  
  211. #           * User reviews/edits the checkpoints and clicks "Lock In Plan and Start Learning".
  212. #           * Call `POST /update_checkpoints` with the current `thread_id` and the (potentially modified) `checkpoints` data.
  213. #           * On success, take the returned `question`, display it in the chat interface, and switch to the main learning view.
  214.  
  215. #       * **Flow 3 (The Learning Loop):**
  216.  
  217. #           * User types an answer and clicks "Submit".
  218. #           * Call `POST /submit_answer` with the `thread_id` and the answer.
  219. #           * The backend will return a JSON object containing verification, optional teaching, and the next question.
  220. #           * Update your UI state:
  221. #               * Display the `verifications` data in the right-hand card.
  222. #               * If `teachings` data is present, display it.
  223. #               * If `question` is present, add it to the chat history.
  224. #               * If `is_complete` is true, show a "Congratulations\!" message.
  225.  
  226. # -----
  227.  
  228. # ### **Phase 4: 🚀 Deployment**
  229.  
  230. # 1.  **Backend (FastAPI):** Deploy your `main.py` application to a service like Vercel (which now supports Python), Heroku, or Google Cloud Run. Remember to set your `OPENAI_API_KEY` and `TAVILY_API_KEY` as environment variables in the deployment service.
  231. # 2.  **Frontend (Next.js):** Deploy your v0-generated Next.js application to Vercel. It's the most seamless option. Ensure your frontend code points to your deployed backend URL, not `localhost`.
  232.  
  233. print("hi")
  234.  
  235. # By following this plan, you will have successfully transformed your sophisticated Jupyter Notebook agent into a fully functional, user-friendly web application.
Success #stdin #stdout 0.08s 14076KB
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https://ideone.com/2061Rt
language:
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