feat: a

agentic-finlit-backend-output.md

@@ -3,8 +3,8 @@
 ## 📊 Project Information
 
 - **Project Name**: `agentic-finlit-backend`
-- **Generated On**: 2025-09-08 05:44:11 (Etc/GMT+5 / GMT-05:00)
-- **Total Files Processed**: 13
+- **Generated On**: 2025-09-08 06:00:37 (Etc/GMT+5 / GMT-05:00)
+- **Total Files Processed**: 14
 - **Export Tool**: Easy Whole Project to Single Text File for LLMs v1.1.0
 - **Tool Author**: Jota / José Guilherme Pandolfi
 
@@ -23,18 +23,19 @@
 ├── 📁 app/
 │   ├── 📁 agents/
 │   │   ├── 📄 graph.py (587 B)
-│   │   ├── 📄 nodes.py (3.37 KB)
+│   │   ├── 📄 nodes.py (3.82 KB)
 │   │   └── 📄 schemas.py (635 B)
 │   ├── 📄 db.py (2.42 KB)
 │   ├── 📄 embeddings.py (578 B)
 │   ├── 📄 hf_client.py (878 B)
 │   ├── 📄 ingest.py (845 B)
-│   ├── 📄 main.py (2.25 KB)
+│   ├── 📄 main.py (2.65 KB)
 │   └── 📄 README.md (107 B)
 ├── 📄 Dockerfile (632 B)
 ├── 📄 elkay.txt
+├── 📄 git
 ├── 📄 README.md (275 B)
-└── 📄 requirements.txt (332 B)
+└── 📄 requirements.txt (345 B)
 ```
 
 ## 📑 Table of Contents
@@ -60,11 +61,11 @@
 
 | Metric | Count |
 |--------|-------|
-| Total Files | 13 |
+| Total Files | 14 |
 | Total Directories | 2 |
 | Text Files | 12 |
-| Binary Files | 1 |
-| Total Size | 12.8 KB |
+| Binary Files | 2 |
+| Total Size | 13.66 KB |
 
 ### 📄 File Types Distribution
 
@@ -72,8 +73,8 @@
 |-----------|-------|
 | `.py` | 8 |
 | `.md` | 2 |
+| `no extension` | 2 |
 | `.txt` | 2 |
-| `no extension` | 1 |
 
 ## 💻 File Code Contents
 
@@ -119,15 +120,15 @@ def build_graph():
 ### <a id="📄-app-agents-nodes-py"></a>📄 `app/agents/nodes.py`
 
 **File Info:**
-- **Size**: 3.37 KB
+- **Size**: 3.82 KB
 - **Extension**: `.py`
 - **Language**: `python`
 - **Location**: `app/agents/nodes.py`
 - **Relative Path**: `app/agents`
 - **Created**: 2025-09-08 04:12:41 (Etc/GMT+5 / GMT-05:00)
-- **Modified**: 2025-09-08 04:17:35 (Etc/GMT+5 / GMT-05:00)
-- **MD5**: `01bf9b0ed40637415e3d43eb3c018d43`
-- **SHA256**: `c8f802e7ee48fe46e70bceef868091fd83123225c166ce72050b7aa3ea1f4f3f`
+- **Modified**: 2025-09-08 05:55:38 (Etc/GMT+5 / GMT-05:00)
+- **MD5**: `98caf6bbfebb550a4a3b9526a8446c4c`
+- **SHA256**: `6fea8331aaef9cef25922369670e2b50c38df7050ad1287c0b865f688a9e4223`
 - **Encoding**: ASCII
 
 **File code content:**
@@ -137,70 +138,71 @@ from .schemas import AgentState
 from ..db import search_chunks, record_submission
 from ..embeddings import embed_query
 from ..hf_client import chat
+import json
 
 def node_rag(state: AgentState) -> AgentState:
-    qvec = embed_query("Key points of this lesson for a Jamaican primary student.")
+    # Vector search for relevant chunks
+    qvec = embed_query(f"Key financial literacy points for Jamaican primary student level {state.level_slug} in lesson {state.lesson_id}.")
     hits = search_chunks(state.lesson_id, state.level_slug, qvec, k=6)
     state.context_chunks = [h["content"] for h in hits]
-    # Summarize context for the student
-    sys = {"role":"system","content":"You are a Jamaican primary school tutor. Keep it simple and friendly."}
-    usr = {"role":"user","content": "Summarize these notes in 5 short bullet points:\n" + "\n\n".join(state.context_chunks)}
+    # LLM summarize
+    sys = {"role": "system", "content": "You are a friendly Jamaican primary school tutor. Keep explanations simple, use JA$ examples, and make it fun for kids aged 7-12."}
+    usr = {"role": "user", "content": f"Summarize these notes in 5 short, kid-friendly bullet points for {state.level_slug} level:\n" + "\n\n".join(state.context_chunks)}
     state.context_summary = chat([sys, usr], max_new_tokens=180)
     return state
 
 def node_quiz(state: AgentState) -> AgentState:
-    sys = {"role":"system","content":"You are a Jamaican primary school teacher. Make kid-friendly multiple-choice questions (A-D) from the provided notes."}
-    usr = {"role":"user","content": "Create 5 MCQs. JSON only: {\"items\":[{\"question\":\"...\",\"options\":[\"A\",\"B\",\"C\",\"D\"],\"answer_key\":\"A\"}...]}\nNotes:\n" + "\n\n".join(state.context_chunks)}
+    sys = {"role": "system", "content": "You are a Jamaican primary school teacher. Create 4-7 simple multiple-choice questions (A-D options) based on the notes. Use JA$ for money examples. Output JSON only."}
+    usr = {"role": "user", "content": f"Create {4 + len(state.context_chunks) % 3} MCQs for level {state.level_slug}. JSON: {{\"items\":[{{\"question\":\"...\",\"options\":[\"A. ...\",\"B. ...\",\"C. ...\",\"D. ...\"],\"answer_key\":\"A\"}}... ]}}\nNotes:\n" + "\n\n".join(state.context_chunks)}
     raw = chat([sys, usr], max_new_tokens=350)
-    import json
-    data = {}
-    try: data = json.loads(raw)
-    except: data = {"items":[]}
-    items = data.get("items", [])[:7]  # 4–7 in your spec
-    # Normalize
+    try:
+        data = json.loads(raw)
+        items = data.get("items", [])[:7]
+    except:
+        items = []  # Fallback
+    # Normalize items
     fixed = []
     for it in items:
-        q = it.get("question","").strip()
-        opts = it.get("options", [])[:4]
-        while len(opts)<4: opts.append("Option")
-        key = str(it.get("answer_key","A")).strip()[:1].upper()
-        if key not in "ABCD": key="A"
-        if q: fixed.append({"question":q, "options":opts, "answer_key":key, "points":1})
+        q = it.get("question", "").strip()
+        opts = it.get("options", ["A. Unknown", "B. Unknown", "C. Unknown", "D. Unknown"])[:4]
+        key = str(it.get("answer_key", "A")).strip()[:1].upper()
+        if key not in "ABCD": key = "A"
+        if q: fixed.append({"question": q, "options": opts, "answer_key": key, "points": 1})
     state.quiz_items = fixed
     return state
 
 def node_grade(state: AgentState) -> AgentState:
-    # simple grading
     answers = state.student_answers
-    score, total, details = 0, len(state.quiz_items), []
-    for i,(it,ans) in enumerate(zip(state.quiz_items, answers)):
-        correct = (ans.upper()==it["answer_key"])
+    score, total = 0, len(state.quiz_items)
+    details = []
+    for i, (it, ans) in enumerate(zip(state.quiz_items, answers)):
+        correct = (ans.upper() == it["answer_key"])
         if correct: score += 1
-        details.append({"qno":i+1,"user":ans,"key":it["answer_key"],"correct":correct})
-    state.score, state.total = score, total
-    # record
-    if state.assignment_id is None:
-        state.assignment_id = 0  # if not assigned formally
-    record_submission(state.student_id, quiz_id=0, assignment_id=state.assignment_id,
-                      score=score, total=total, details={"items":details})
+        details.append({"qno": i+1, "user": ans, "key": it["answer_key"], "correct": correct})
+    state.score = score
+    state.total = total
+    state.details = details
+    # Record in TiDB
+    record_submission(state.student_id, quiz_id=0, assignment_id=state.assignment_id or 0,
+                      score=score, total=total, details=details)
+    # Update XP in users table (simple: +10 per correct)
+    with get_conn() as conn:
+        with conn.cursor() as cur:
+            cur.execute("UPDATE users SET total_xp = total_xp + %s WHERE user_id = %s", (score * 10, state.student_id))
+            conn.commit()
     return state
 
 def node_coach_or_celebrate(state: AgentState) -> AgentState:
-    if state.score == state.total and state.total is not None and state.total>0:
+    if state.score == state.total and state.total > 0:
         state.route = "celebrate"
         return state
-    # Coach for wrong answers
-    wrong = []
-    for it,ans in zip(state.quiz_items, state.student_answers):
-        if ans.upper()!=it["answer_key"]:
-            wrong.append({"q":it["question"],"your":ans,"key":it["answer_key"]})
-    sys = {"role":"system","content":"You are a kind tutor. Explain simply where the student went wrong."}
-    usr = {"role":"user","content": "Here are the mistakes:\n" + "\n".join([f"Q: {w['q']}\nYour answer: {w['your']}\nCorrect: {w['key']}" for w in wrong]) +
-           "\nGive short, friendly explanations for each."}
-    state.feedback = chat([sys,usr], max_new_tokens=220)
+    # Coach on wrongs
+    wrong = [it for it, ans in zip(state.quiz_items, state.student_answers) if ans.upper() != it["answer_key"]]
+    sys = {"role": "system", "content": "You are a kind Jamaican tutor. Explain mistakes simply and encouragingly for primary kids. Use JA$ examples."}
+    usr = {"role": "user", "content": f"Explain these wrong answers for level {state.level_slug}:\n" + "\n".join([f"Q: {w['question']}\nYour: {w['user_ans']}\nCorrect: {w['key']}" for w in wrong]) + "\nKeep it short and positive."}
+    state.feedback = chat([sys, usr], max_new_tokens=220)
     state.route = "coach"
     return state
-
 ```
 
 ---
@@ -458,33 +460,26 @@ def ingest_lesson(lesson_id:int, level_slug:str, raw_text:str):
 ### <a id="📄-app-main-py"></a>📄 `app/main.py`
 
 **File Info:**
-- **Size**: 2.25 KB
+- **Size**: 2.65 KB
 - **Extension**: `.py`
 - **Language**: `python`
 - **Location**: `app/main.py`
 - **Relative Path**: `app`
 - **Created**: 2025-09-08 03:31:10 (Etc/GMT+5 / GMT-05:00)
-- **Modified**: 2025-09-08 05:33:58 (Etc/GMT+5 / GMT-05:00)
-- **MD5**: `8303a8d4c5e8e664e4ff8962623fec51`
-- **SHA256**: `c8cc9a31cf9c6ff78196de5648108fd37bad8c36995ab41310b46c90b68bb238`
+- **Modified**: 2025-09-08 05:55:14 (Etc/GMT+5 / GMT-05:00)
+- **MD5**: `4e5e0615148bb8700abc8b8c85bf5176`
+- **SHA256**: `8b7fc35e1728bc98d5792883a9c696830da1e39fac84af3c0410a4d036ee193f`
 - **Encoding**: ASCII
 
 **File code content:**
 
 ```python
+from fastapi import FastAPI
 from pydantic import BaseModel
 from .agents.schemas import AgentState
 from .agents.graph import build_graph
 from .ingest import ingest_lesson
-
-
-from fastapi import FastAPI
-
-app = FastAPI()
-
-@app.get("/health")
-def health():
-    return {"ok": True, "service": "agentic-finlit-backend"}
+from .db import get_conn  # For startup checks
 
 api = FastAPI(title="Agentic FinLit Backend")
 _graph = build_graph()
@@ -501,48 +496,58 @@ class SubmitQuiz(BaseModel):
     answers: list[str]
     assignment_id: int | None = None
 
+@api.on_event("startup")
+async def startup_event():
+    # Ingest sample lesson on startup (from frontend's topic_1.txt; adjust path)
+    try:
+        with open("../../phase/Student_view/lessons/lesson_1/topic_1.txt", "r") as f:
+            raw_text = f.read()
+        ingest_lesson(1, "beginner", raw_text)  # Sample: Lesson 1, Beginner
+        print("Sample lesson ingested.")
+    except Exception as e:
+        print(f"Ingestion failed: {e}")
+
 @api.post("/ingest")
-def ingest(lesson_id:int, level_slug:str, raw_text:str):
+def ingest_endpoint(lesson_id: int, level_slug: str, raw_text: str):
     ingest_lesson(lesson_id, level_slug, raw_text)
-    return {"ok": True}
+    return {"ok": True, "message": f"Ingested lesson {lesson_id} for level {level_slug}"}
 
 @api.post("/agent/start")
 def agent_start(payload: StartRun):
     state = AgentState(**payload.model_dump())
-    out = _graph.invoke(state, start_at="rag")
+    out = _graph.invoke(state)
     return {"summary": out.context_summary, "ok": True}
 
 @api.post("/agent/quiz")
 def agent_quiz(payload: StartRun):
     state = AgentState(**payload.model_dump())
-    out = _graph.invoke(state, start_at="quiz")
+    out = _graph.invoke(state, {"quiz": {}})  # Partial invoke for quiz gen
     return {"items": out.quiz_items}
 
 @api.post("/agent/grade")
 def agent_grade(payload: SubmitQuiz):
-    st = AgentState(student_id=payload.student_id, lesson_id=payload.lesson_id,
-                    level_slug=payload.level_slug, assignment_id=payload.assignment_id)
+    st = AgentState(**{k: v for k, v in payload.model_dump().items() if k != 'answers'})
     st.student_answers = payload.answers
-    out = _graph.invoke(st, start_at="grade")
-    return {"score": out.score, "total": out.total}
+    out = _graph.invoke(st, {"grade": {}})
+    return {"score": out.score, "total": out.total, "details": out.details if hasattr(out, 'details') else {}}
 
 @api.post("/agent/coach_or_celebrate")
 def agent_next(payload: SubmitQuiz):
-    st = AgentState(student_id=payload.student_id, lesson_id=payload.lesson_id,
-                    level_slug=payload.level_slug, assignment_id=payload.assignment_id)
+    st = AgentState(**{k: v for k, v in payload.model_dump().items() if k != 'answers'})
     st.student_answers = payload.answers
-    out = _graph.invoke(st, start_at="coach_or_celebrate")
+    out = _graph.invoke(st, {"coach_or_celebrate": {}})
     data = {"route": out.route}
     if out.route == "coach":
         data["feedback"] = out.feedback
-    else:
+    elif out.route == "celebrate":
         data["message"] = "Great job! Want to play Money Match?"
-        data["game_slug"] = "money_match"
+        data["game_slug"] = "money_match"  # Links to game in frontend
     return data
 
-if __name__ == "__main__":
-    import os, uvicorn
-    uvicorn.run("app.main:app", host="0.0.0.0", port=int(os.getenv("PORT", "7860")))
+# Health check for frontend
+@api.get("/")
+def health():
+    return {"status": "Backend running", "agentic_flow": "ready"}
 ```
 
 ---
@@ -630,15 +635,15 @@ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-
 ### <a id="📄-requirements-txt"></a>📄 `requirements.txt`
 
 **File Info:**
-- **Size**: 332 B
+- **Size**: 345 B
 - **Extension**: `.txt`
 - **Language**: `text`
 - **Location**: `requirements.txt`
 - **Relative Path**: `root`
 - **Created**: 2025-09-08 03:23:55 (Etc/GMT+5 / GMT-05:00)
-- **Modified**: 2025-09-08 05:23:44 (Etc/GMT+5 / GMT-05:00)
-- **MD5**: `011711e3a3a8373749286cd4918b9079`
-- **SHA256**: `6d05e356cd5b401fee707c7613b7b1b0623ef3d160ae0d54fdd43b1442bb69e8`
+- **Modified**: 2025-09-08 06:00:37 (Etc/GMT+5 / GMT-05:00)
+- **MD5**: `fa57386573787bc9628b5527758d3a3a`
+- **SHA256**: `845818e5fa794614226cd9b08ad209c359843b0f485f8f3c5a6ac24c26be0609`
 - **Encoding**: ASCII
 
 **File code content:**
@@ -661,7 +666,7 @@ python-dotenv==1.0.1
 tenacity>=8.2.3
 certifi>=2024.6.2
 mysql-connector-python
-
+bcrypt==4.0.1
 ```
 
 ---
@@ -671,4 +676,5 @@ mysql-connector-python
 The following files were not included in the text content:
 
 - `Dockerfile`
+- `git`
 

agentic-finlit-backend-output.txt

@@ -5,8 +5,8 @@ PROJECT EXPORT FOR LLMs
 PROJECT INFORMATION:
 --------------------------------------------------
 Project Name: agentic-finlit-backend
-Generated On: 2025-09-08 05:44:11 (Etc/GMT+5 / GMT-05:00)
-Total Files Processed: 13
+Generated On: 2025-09-08 06:00:37 (Etc/GMT+5 / GMT-05:00)
+Total Files Processed: 14
 Export Tool: Easy Whole Project to Single Text File for LLMs v1.1.0
 Tool Author: Jota / José Guilherme Pandolfi
 
@@ -25,34 +25,35 @@ PROJECT STRUCTURE
 ├── 📁 app/
 │   ├── 📁 agents/
 │   │   ├── 📄 graph.py (587 B)
-│   │   ├── 📄 nodes.py (3.37 KB)
+│   │   ├── 📄 nodes.py (3.82 KB)
 │   │   └── 📄 schemas.py (635 B)
 │   ├── 📄 db.py (2.42 KB)
 │   ├── 📄 embeddings.py (578 B)
 │   ├── 📄 hf_client.py (878 B)
 │   ├── 📄 ingest.py (845 B)
-│   ├── 📄 main.py (2.25 KB)
+│   ├── 📄 main.py (2.65 KB)
 │   └── 📄 README.md (107 B)
 ├── 📄 Dockerfile (632 B)
 ├── 📄 elkay.txt
+├── 📄 git
 ├── 📄 README.md (275 B)
-└── 📄 requirements.txt (332 B)
+└── 📄 requirements.txt (345 B)
 
 ================================================================================
 PROJECT STATISTICS
 ================================================================================
-Total Files: 13
+Total Files: 14
 Total Directories: 2
 Text Files: 12
-Binary Files: 1
-Total Size: 12.8 KB
+Binary Files: 2
+Total Size: 13.66 KB
 
 FILE TYPES DISTRIBUTION:
 ------------------------------
 .py             : 8
 .md             : 2
+no extension    : 2
 .txt            : 2
-no extension    : 1
 
 ================================================================================
 FILE CODE CONTENTS
@@ -105,15 +106,15 @@ FILE: app/agents/nodes.py
 
 FILE INFORMATION:
 ----------------------------------------
-Size: 3.37 KB
+Size: 3.82 KB
 Extension: .py
 Language: python
 Location: app/agents/nodes.py
 Relative Path: app/agents
 Created: 2025-09-08 04:12:41 (Etc/GMT+5 / GMT-05:00)
-Modified: 2025-09-08 04:17:35 (Etc/GMT+5 / GMT-05:00)
-MD5: 01bf9b0ed40637415e3d43eb3c018d43
-SHA256: c8f802e7ee48fe46e70bceef868091fd83123225c166ce72050b7aa3ea1f4f3f
+Modified: 2025-09-08 05:55:38 (Etc/GMT+5 / GMT-05:00)
+MD5: 98caf6bbfebb550a4a3b9526a8446c4c
+SHA256: 6fea8331aaef9cef25922369670e2b50c38df7050ad1287c0b865f688a9e4223
 Encoding: ASCII
 
 FILE CONTENT:
@@ -122,70 +123,71 @@ from .schemas import AgentState
 from ..db import search_chunks, record_submission
 from ..embeddings import embed_query
 from ..hf_client import chat
+import json
 
 def node_rag(state: AgentState) -> AgentState:
-    qvec = embed_query("Key points of this lesson for a Jamaican primary student.")
+    # Vector search for relevant chunks
+    qvec = embed_query(f"Key financial literacy points for Jamaican primary student level {state.level_slug} in lesson {state.lesson_id}.")
     hits = search_chunks(state.lesson_id, state.level_slug, qvec, k=6)
     state.context_chunks = [h["content"] for h in hits]
-    # Summarize context for the student
-    sys = {"role":"system","content":"You are a Jamaican primary school tutor. Keep it simple and friendly."}
-    usr = {"role":"user","content": "Summarize these notes in 5 short bullet points:\n" + "\n\n".join(state.context_chunks)}
+    # LLM summarize
+    sys = {"role": "system", "content": "You are a friendly Jamaican primary school tutor. Keep explanations simple, use JA$ examples, and make it fun for kids aged 7-12."}
+    usr = {"role": "user", "content": f"Summarize these notes in 5 short, kid-friendly bullet points for {state.level_slug} level:\n" + "\n\n".join(state.context_chunks)}
     state.context_summary = chat([sys, usr], max_new_tokens=180)
     return state
 
 def node_quiz(state: AgentState) -> AgentState:
-    sys = {"role":"system","content":"You are a Jamaican primary school teacher. Make kid-friendly multiple-choice questions (A-D) from the provided notes."}
-    usr = {"role":"user","content": "Create 5 MCQs. JSON only: {\"items\":[{\"question\":\"...\",\"options\":[\"A\",\"B\",\"C\",\"D\"],\"answer_key\":\"A\"}...]}\nNotes:\n" + "\n\n".join(state.context_chunks)}
+    sys = {"role": "system", "content": "You are a Jamaican primary school teacher. Create 4-7 simple multiple-choice questions (A-D options) based on the notes. Use JA$ for money examples. Output JSON only."}
+    usr = {"role": "user", "content": f"Create {4 + len(state.context_chunks) % 3} MCQs for level {state.level_slug}. JSON: {{\"items\":[{{\"question\":\"...\",\"options\":[\"A. ...\",\"B. ...\",\"C. ...\",\"D. ...\"],\"answer_key\":\"A\"}}... ]}}\nNotes:\n" + "\n\n".join(state.context_chunks)}
     raw = chat([sys, usr], max_new_tokens=350)
-    import json
-    data = {}
-    try: data = json.loads(raw)
-    except: data = {"items":[]}
-    items = data.get("items", [])[:7]  # 4–7 in your spec
-    # Normalize
+    try:
+        data = json.loads(raw)
+        items = data.get("items", [])[:7]
+    except:
+        items = []  # Fallback
+    # Normalize items
     fixed = []
     for it in items:
-        q = it.get("question","").strip()
-        opts = it.get("options", [])[:4]
-        while len(opts)<4: opts.append("Option")
-        key = str(it.get("answer_key","A")).strip()[:1].upper()
-        if key not in "ABCD": key="A"
-        if q: fixed.append({"question":q, "options":opts, "answer_key":key, "points":1})
+        q = it.get("question", "").strip()
+        opts = it.get("options", ["A. Unknown", "B. Unknown", "C. Unknown", "D. Unknown"])[:4]
+        key = str(it.get("answer_key", "A")).strip()[:1].upper()
+        if key not in "ABCD": key = "A"
+        if q: fixed.append({"question": q, "options": opts, "answer_key": key, "points": 1})
     state.quiz_items = fixed
     return state
 
 def node_grade(state: AgentState) -> AgentState:
-    # simple grading
     answers = state.student_answers
-    score, total, details = 0, len(state.quiz_items), []
-    for i,(it,ans) in enumerate(zip(state.quiz_items, answers)):
-        correct = (ans.upper()==it["answer_key"])
+    score, total = 0, len(state.quiz_items)
+    details = []
+    for i, (it, ans) in enumerate(zip(state.quiz_items, answers)):
+        correct = (ans.upper() == it["answer_key"])
         if correct: score += 1
-        details.append({"qno":i+1,"user":ans,"key":it["answer_key"],"correct":correct})
-    state.score, state.total = score, total
-    # record
-    if state.assignment_id is None:
-        state.assignment_id = 0  # if not assigned formally
-    record_submission(state.student_id, quiz_id=0, assignment_id=state.assignment_id,
-                      score=score, total=total, details={"items":details})
+        details.append({"qno": i+1, "user": ans, "key": it["answer_key"], "correct": correct})
+    state.score = score
+    state.total = total
+    state.details = details
+    # Record in TiDB
+    record_submission(state.student_id, quiz_id=0, assignment_id=state.assignment_id or 0,
+                      score=score, total=total, details=details)
+    # Update XP in users table (simple: +10 per correct)
+    with get_conn() as conn:
+        with conn.cursor() as cur:
+            cur.execute("UPDATE users SET total_xp = total_xp + %s WHERE user_id = %s", (score * 10, state.student_id))
+            conn.commit()
     return state
 
 def node_coach_or_celebrate(state: AgentState) -> AgentState:
-    if state.score == state.total and state.total is not None and state.total>0:
+    if state.score == state.total and state.total > 0:
         state.route = "celebrate"
         return state
-    # Coach for wrong answers
-    wrong = []
-    for it,ans in zip(state.quiz_items, state.student_answers):
-        if ans.upper()!=it["answer_key"]:
-            wrong.append({"q":it["question"],"your":ans,"key":it["answer_key"]})
-    sys = {"role":"system","content":"You are a kind tutor. Explain simply where the student went wrong."}
-    usr = {"role":"user","content": "Here are the mistakes:\n" + "\n".join([f"Q: {w['q']}\nYour answer: {w['your']}\nCorrect: {w['key']}" for w in wrong]) +
-           "\nGive short, friendly explanations for each."}
-    state.feedback = chat([sys,usr], max_new_tokens=220)
+    # Coach on wrongs
+    wrong = [it for it, ans in zip(state.quiz_items, state.student_answers) if ans.upper() != it["answer_key"]]
+    sys = {"role": "system", "content": "You are a kind Jamaican tutor. Explain mistakes simply and encouragingly for primary kids. Use JA$ examples."}
+    usr = {"role": "user", "content": f"Explain these wrong answers for level {state.level_slug}:\n" + "\n".join([f"Q: {w['question']}\nYour: {w['user_ans']}\nCorrect: {w['key']}" for w in wrong]) + "\nKeep it short and positive."}
+    state.feedback = chat([sys, usr], max_new_tokens=220)
     state.route = "coach"
     return state
-
 ================================================================================
 
 
@@ -450,32 +452,25 @@ FILE: app/main.py
 
 FILE INFORMATION:
 ----------------------------------------
-Size: 2.25 KB
+Size: 2.65 KB
 Extension: .py
 Language: python
 Location: app/main.py
 Relative Path: app
 Created: 2025-09-08 03:31:10 (Etc/GMT+5 / GMT-05:00)
-Modified: 2025-09-08 05:33:58 (Etc/GMT+5 / GMT-05:00)
-MD5: 8303a8d4c5e8e664e4ff8962623fec51
-SHA256: c8cc9a31cf9c6ff78196de5648108fd37bad8c36995ab41310b46c90b68bb238
+Modified: 2025-09-08 05:55:14 (Etc/GMT+5 / GMT-05:00)
+MD5: 4e5e0615148bb8700abc8b8c85bf5176
+SHA256: 8b7fc35e1728bc98d5792883a9c696830da1e39fac84af3c0410a4d036ee193f
 Encoding: ASCII
 
 FILE CONTENT:
 ----------------------------------------
+from fastapi import FastAPI
 from pydantic import BaseModel
 from .agents.schemas import AgentState
 from .agents.graph import build_graph
 from .ingest import ingest_lesson
-
-
-from fastapi import FastAPI
-
-app = FastAPI()
-
-@app.get("/health")
-def health():
-    return {"ok": True, "service": "agentic-finlit-backend"}
+from .db import get_conn  # For startup checks
 
 api = FastAPI(title="Agentic FinLit Backend")
 _graph = build_graph()
@@ -492,48 +487,58 @@ class SubmitQuiz(BaseModel):
     answers: list[str]
     assignment_id: int | None = None
 
+@api.on_event("startup")
+async def startup_event():
+    # Ingest sample lesson on startup (from frontend's topic_1.txt; adjust path)
+    try:
+        with open("../../phase/Student_view/lessons/lesson_1/topic_1.txt", "r") as f:
+            raw_text = f.read()
+        ingest_lesson(1, "beginner", raw_text)  # Sample: Lesson 1, Beginner
+        print("Sample lesson ingested.")
+    except Exception as e:
+        print(f"Ingestion failed: {e}")
+
 @api.post("/ingest")
-def ingest(lesson_id:int, level_slug:str, raw_text:str):
+def ingest_endpoint(lesson_id: int, level_slug: str, raw_text: str):
     ingest_lesson(lesson_id, level_slug, raw_text)
-    return {"ok": True}
+    return {"ok": True, "message": f"Ingested lesson {lesson_id} for level {level_slug}"}
 
 @api.post("/agent/start")
 def agent_start(payload: StartRun):
     state = AgentState(**payload.model_dump())
-    out = _graph.invoke(state, start_at="rag")
+    out = _graph.invoke(state)
     return {"summary": out.context_summary, "ok": True}
 
 @api.post("/agent/quiz")
 def agent_quiz(payload: StartRun):
     state = AgentState(**payload.model_dump())
-    out = _graph.invoke(state, start_at="quiz")
+    out = _graph.invoke(state, {"quiz": {}})  # Partial invoke for quiz gen
     return {"items": out.quiz_items}
 
 @api.post("/agent/grade")
 def agent_grade(payload: SubmitQuiz):
-    st = AgentState(student_id=payload.student_id, lesson_id=payload.lesson_id,
-                    level_slug=payload.level_slug, assignment_id=payload.assignment_id)
+    st = AgentState(**{k: v for k, v in payload.model_dump().items() if k != 'answers'})
     st.student_answers = payload.answers
-    out = _graph.invoke(st, start_at="grade")
-    return {"score": out.score, "total": out.total}
+    out = _graph.invoke(st, {"grade": {}})
+    return {"score": out.score, "total": out.total, "details": out.details if hasattr(out, 'details') else {}}
 
 @api.post("/agent/coach_or_celebrate")
 def agent_next(payload: SubmitQuiz):
-    st = AgentState(student_id=payload.student_id, lesson_id=payload.lesson_id,
-                    level_slug=payload.level_slug, assignment_id=payload.assignment_id)
+    st = AgentState(**{k: v for k, v in payload.model_dump().items() if k != 'answers'})
     st.student_answers = payload.answers
-    out = _graph.invoke(st, start_at="coach_or_celebrate")
+    out = _graph.invoke(st, {"coach_or_celebrate": {}})
     data = {"route": out.route}
     if out.route == "coach":
         data["feedback"] = out.feedback
-    else:
+    elif out.route == "celebrate":
         data["message"] = "Great job! Want to play Money Match?"
-        data["game_slug"] = "money_match"
+        data["game_slug"] = "money_match"  # Links to game in frontend
     return data
 
-if __name__ == "__main__":
-    import os, uvicorn
-    uvicorn.run("app.main:app", host="0.0.0.0", port=int(os.getenv("PORT", "7860")))
+# Health check for frontend
+@api.get("/")
+def health():
+    return {"status": "Backend running", "agentic_flow": "ready"}
 ================================================================================
 
 
@@ -626,15 +631,15 @@ FILE: requirements.txt
 
 FILE INFORMATION:
 ----------------------------------------
-Size: 332 B
+Size: 345 B
 Extension: .txt
 Language: text
 Location: requirements.txt
 Relative Path: root
 Created: 2025-09-08 03:23:55 (Etc/GMT+5 / GMT-05:00)
-Modified: 2025-09-08 05:23:44 (Etc/GMT+5 / GMT-05:00)
-MD5: 011711e3a3a8373749286cd4918b9079
-SHA256: 6d05e356cd5b401fee707c7613b7b1b0623ef3d160ae0d54fdd43b1442bb69e8
+Modified: 2025-09-08 06:00:37 (Etc/GMT+5 / GMT-05:00)
+MD5: fa57386573787bc9628b5527758d3a3a
+SHA256: 845818e5fa794614226cd9b08ad209c359843b0f485f8f3c5a6ac24c26be0609
 Encoding: ASCII
 
 FILE CONTENT:
@@ -656,7 +661,7 @@ python-dotenv==1.0.1
 tenacity>=8.2.3
 certifi>=2024.6.2
 mysql-connector-python
-
+bcrypt==4.0.1
 ================================================================================
 
 
@@ -664,3 +669,4 @@ mysql-connector-python
 BINARY/EXCLUDED FILES (not included in text content)
 ================================================================================
 - Dockerfile
+- git

app/agents/nodes.py

@@ -2,66 +2,68 @@ from .schemas import AgentState
 from ..db import search_chunks, record_submission
 from ..embeddings import embed_query
 from ..hf_client import chat
+import json
 
 def node_rag(state: AgentState) -> AgentState:
-    qvec = embed_query("Key points of this lesson for a Jamaican primary student.")
+    # Vector search for relevant chunks
+    qvec = embed_query(f"Key financial literacy points for Jamaican primary student level {state.level_slug} in lesson {state.lesson_id}.")
     hits = search_chunks(state.lesson_id, state.level_slug, qvec, k=6)
     state.context_chunks = [h["content"] for h in hits]
-    # Summarize context for the student
-    sys = {"role":"system","content":"You are a Jamaican primary school tutor. Keep it simple and friendly."}
-    usr = {"role":"user","content": "Summarize these notes in 5 short bullet points:\n" + "\n\n".join(state.context_chunks)}
+    # LLM summarize
+    sys = {"role": "system", "content": "You are a friendly Jamaican primary school tutor. Keep explanations simple, use JA$ examples, and make it fun for kids aged 7-12."}
+    usr = {"role": "user", "content": f"Summarize these notes in 5 short, kid-friendly bullet points for {state.level_slug} level:\n" + "\n\n".join(state.context_chunks)}
     state.context_summary = chat([sys, usr], max_new_tokens=180)
     return state
 
 def node_quiz(state: AgentState) -> AgentState:
-    sys = {"role":"system","content":"You are a Jamaican primary school teacher. Make kid-friendly multiple-choice questions (A-D) from the provided notes."}
-    usr = {"role":"user","content": "Create 5 MCQs. JSON only: {\"items\":[{\"question\":\"...\",\"options\":[\"A\",\"B\",\"C\",\"D\"],\"answer_key\":\"A\"}...]}\nNotes:\n" + "\n\n".join(state.context_chunks)}
+    sys = {"role": "system", "content": "You are a Jamaican primary school teacher. Create 4-7 simple multiple-choice questions (A-D options) based on the notes. Use JA$ for money examples. Output JSON only."}
+    usr = {"role": "user", "content": f"Create {4 + len(state.context_chunks) % 3} MCQs for level {state.level_slug}. JSON: {{\"items\":[{{\"question\":\"...\",\"options\":[\"A. ...\",\"B. ...\",\"C. ...\",\"D. ...\"],\"answer_key\":\"A\"}}... ]}}\nNotes:\n" + "\n\n".join(state.context_chunks)}
     raw = chat([sys, usr], max_new_tokens=350)
-    import json
-    data = {}
-    try: data = json.loads(raw)
-    except: data = {"items":[]}
-    items = data.get("items", [])[:7]  # 4–7 in your spec
-    # Normalize
+    try:
+        data = json.loads(raw)
+        items = data.get("items", [])[:7]
+    except:
+        items = []  # Fallback
+    # Normalize items
     fixed = []
     for it in items:
-        q = it.get("question","").strip()
-        opts = it.get("options", [])[:4]
-        while len(opts)<4: opts.append("Option")
-        key = str(it.get("answer_key","A")).strip()[:1].upper()
-        if key not in "ABCD": key="A"
-        if q: fixed.append({"question":q, "options":opts, "answer_key":key, "points":1})
+        q = it.get("question", "").strip()
+        opts = it.get("options", ["A. Unknown", "B. Unknown", "C. Unknown", "D. Unknown"])[:4]
+        key = str(it.get("answer_key", "A")).strip()[:1].upper()
+        if key not in "ABCD": key = "A"
+        if q: fixed.append({"question": q, "options": opts, "answer_key": key, "points": 1})
     state.quiz_items = fixed
     return state
 
 def node_grade(state: AgentState) -> AgentState:
-    # simple grading
     answers = state.student_answers
-    score, total, details = 0, len(state.quiz_items), []
-    for i,(it,ans) in enumerate(zip(state.quiz_items, answers)):
-        correct = (ans.upper()==it["answer_key"])
+    score, total = 0, len(state.quiz_items)
+    details = []
+    for i, (it, ans) in enumerate(zip(state.quiz_items, answers)):
+        correct = (ans.upper() == it["answer_key"])
         if correct: score += 1
-        details.append({"qno":i+1,"user":ans,"key":it["answer_key"],"correct":correct})
-    state.score, state.total = score, total
-    # record
-    if state.assignment_id is None:
-        state.assignment_id = 0  # if not assigned formally
-    record_submission(state.student_id, quiz_id=0, assignment_id=state.assignment_id,
-                      score=score, total=total, details={"items":details})
+        details.append({"qno": i+1, "user": ans, "key": it["answer_key"], "correct": correct})
+    state.score = score
+    state.total = total
+    state.details = details
+    # Record in TiDB
+    record_submission(state.student_id, quiz_id=0, assignment_id=state.assignment_id or 0,
+                      score=score, total=total, details=details)
+    # Update XP in users table (simple: +10 per correct)
+    with get_conn() as conn:
+        with conn.cursor() as cur:
+            cur.execute("UPDATE users SET total_xp = total_xp + %s WHERE user_id = %s", (score * 10, state.student_id))
+            conn.commit()
     return state
 
 def node_coach_or_celebrate(state: AgentState) -> AgentState:
-    if state.score == state.total and state.total is not None and state.total>0:
+    if state.score == state.total and state.total > 0:
         state.route = "celebrate"
         return state
-    # Coach for wrong answers
-    wrong = []
-    for it,ans in zip(state.quiz_items, state.student_answers):
-        if ans.upper()!=it["answer_key"]:
-            wrong.append({"q":it["question"],"your":ans,"key":it["answer_key"]})
-    sys = {"role":"system","content":"You are a kind tutor. Explain simply where the student went wrong."}
-    usr = {"role":"user","content": "Here are the mistakes:\n" + "\n".join([f"Q: {w['q']}\nYour answer: {w['your']}\nCorrect: {w['key']}" for w in wrong]) +
-           "\nGive short, friendly explanations for each."}
-    state.feedback = chat([sys,usr], max_new_tokens=220)
+    # Coach on wrongs
+    wrong = [it for it, ans in zip(state.quiz_items, state.student_answers) if ans.upper() != it["answer_key"]]
+    sys = {"role": "system", "content": "You are a kind Jamaican tutor. Explain mistakes simply and encouragingly for primary kids. Use JA$ examples."}
+    usr = {"role": "user", "content": f"Explain these wrong answers for level {state.level_slug}:\n" + "\n".join([f"Q: {w['question']}\nYour: {w['user_ans']}\nCorrect: {w['key']}" for w in wrong]) + "\nKeep it short and positive."}
+    state.feedback = chat([sys, usr], max_new_tokens=220)
     state.route = "coach"
-    return state
+    return state

app/main.py

@@ -1,16 +1,9 @@
+from fastapi import FastAPI
 from pydantic import BaseModel
 from .agents.schemas import AgentState
 from .agents.graph import build_graph
 from .ingest import ingest_lesson
-
-
-from fastapi import FastAPI
-
-app = FastAPI()
-
-@app.get("/health")
-def health():
-    return {"ok": True, "service": "agentic-finlit-backend"}
+from .db import get_conn  # For startup checks
 
 api = FastAPI(title="Agentic FinLit Backend")
 _graph = build_graph()
@@ -27,45 +20,55 @@ class SubmitQuiz(BaseModel):
     answers: list[str]
     assignment_id: int | None = None
 
+@api.on_event("startup")
+async def startup_event():
+    # Ingest sample lesson on startup (from frontend's topic_1.txt; adjust path)
+    try:
+        with open("../../phase/Student_view/lessons/lesson_1/topic_1.txt", "r") as f:
+            raw_text = f.read()
+        ingest_lesson(1, "beginner", raw_text)  # Sample: Lesson 1, Beginner
+        print("Sample lesson ingested.")
+    except Exception as e:
+        print(f"Ingestion failed: {e}")
+
 @api.post("/ingest")
-def ingest(lesson_id:int, level_slug:str, raw_text:str):
+def ingest_endpoint(lesson_id: int, level_slug: str, raw_text: str):
     ingest_lesson(lesson_id, level_slug, raw_text)
-    return {"ok": True}
+    return {"ok": True, "message": f"Ingested lesson {lesson_id} for level {level_slug}"}
 
 @api.post("/agent/start")
 def agent_start(payload: StartRun):
     state = AgentState(**payload.model_dump())
-    out = _graph.invoke(state, start_at="rag")
+    out = _graph.invoke(state)
     return {"summary": out.context_summary, "ok": True}
 
 @api.post("/agent/quiz")
 def agent_quiz(payload: StartRun):
     state = AgentState(**payload.model_dump())
-    out = _graph.invoke(state, start_at="quiz")
+    out = _graph.invoke(state, {"quiz": {}})  # Partial invoke for quiz gen
     return {"items": out.quiz_items}
 
 @api.post("/agent/grade")
 def agent_grade(payload: SubmitQuiz):
-    st = AgentState(student_id=payload.student_id, lesson_id=payload.lesson_id,
-                    level_slug=payload.level_slug, assignment_id=payload.assignment_id)
+    st = AgentState(**{k: v for k, v in payload.model_dump().items() if k != 'answers'})
     st.student_answers = payload.answers
-    out = _graph.invoke(st, start_at="grade")
-    return {"score": out.score, "total": out.total}
+    out = _graph.invoke(st, {"grade": {}})
+    return {"score": out.score, "total": out.total, "details": out.details if hasattr(out, 'details') else {}}
 
 @api.post("/agent/coach_or_celebrate")
 def agent_next(payload: SubmitQuiz):
-    st = AgentState(student_id=payload.student_id, lesson_id=payload.lesson_id,
-                    level_slug=payload.level_slug, assignment_id=payload.assignment_id)
+    st = AgentState(**{k: v for k, v in payload.model_dump().items() if k != 'answers'})
     st.student_answers = payload.answers
-    out = _graph.invoke(st, start_at="coach_or_celebrate")
+    out = _graph.invoke(st, {"coach_or_celebrate": {}})
     data = {"route": out.route}
     if out.route == "coach":
         data["feedback"] = out.feedback
-    else:
+    elif out.route == "celebrate":
         data["message"] = "Great job! Want to play Money Match?"
-        data["game_slug"] = "money_match"
+        data["game_slug"] = "money_match"  # Links to game in frontend
     return data
 
-if __name__ == "__main__":
-    import os, uvicorn
-    uvicorn.run("app.main:app", host="0.0.0.0", port=int(os.getenv("PORT", "7860")))
+# Health check for frontend
+@api.get("/")
+def health():
+    return {"status": "Backend running", "agentic_flow": "ready"}

requirements.txt

@@ -15,3 +15,4 @@ python-dotenv==1.0.1
 tenacity>=8.2.3
 certifi>=2024.6.2
 mysql-connector-python
+bcrypt==4.0.1