Create api/server.py

api/api/server.py

@@ -0,0 +1,188 @@
+# api/server.py
+import os
+import time
+from typing import Dict, List, Optional, Tuple
+from fastapi import FastAPI, UploadFile, File, Form
+from fastapi.responses import FileResponse
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+
+from api.config import DEFAULT_COURSE_TOPICS, DEFAULT_MODEL
+from api.syllabus_utils import extract_course_topics_from_file
+from api.rag_engine import build_rag_chunks_from_file, retrieve_relevant_chunks
+from api.clare_core import (
+    detect_language,
+    chat_with_clare,
+    update_weaknesses_from_message,
+    update_cognitive_state_from_message,
+    render_session_status,
+    export_conversation,
+    summarize_conversation,
+)
+
+MODULE10_PATH = os.path.join(os.path.dirname(__file__), "module10_responsible_ai.pdf")
+MODULE10_DOC_TYPE = "Literature Review / Paper"
+
+app = FastAPI(title="Clare API")
+
+# ---- serve web build ----
+WEB_DIST = os.path.join(os.path.dirname(__file__), "..", "web", "dist")
+app.mount("/assets", StaticFiles(directory=os.path.join(WEB_DIST, "assets")), name="assets")
+
+@app.get("/")
+def index():
+    return FileResponse(os.path.join(WEB_DIST, "index.html"))
+
+# ---- in-memory session store (MVP) ----
+# 生产环境建议 Redis / DB；但你第一阶段完全够用
+SESSIONS: Dict[str, Dict] = {}
+
+def _get_session(user_id: str) -> Dict:
+    if user_id not in SESSIONS:
+        # preload module10
+        course_outline = DEFAULT_COURSE_TOPICS
+        rag_chunks = []
+        if os.path.exists(MODULE10_PATH):
+            rag_chunks = build_rag_chunks_from_file(MODULE10_PATH, MODULE10_DOC_TYPE)
+
+        SESSIONS[user_id] = {
+            "user_id": user_id,
+            "name": "",
+            "history": [],
+            "weaknesses": [],
+            "cognitive_state": {"confusion": 0, "mastery": 0},
+            "course_outline": course_outline,
+            "rag_chunks": rag_chunks,
+            "model_name": DEFAULT_MODEL,
+        }
+    return SESSIONS[user_id]
+
+class LoginReq(BaseModel):
+    name: str
+    user_id: str
+
+@app.post("/api/login")
+def login(req: LoginReq):
+    sess = _get_session(req.user_id)
+    sess["name"] = req.name
+    return {"ok": True, "user": {"name": req.name, "user_id": req.user_id}}
+
+class ChatReq(BaseModel):
+    user_id: str
+    message: str
+    learning_mode: str
+    language_preference: str = "Auto"
+    doc_type: str = "Syllabus"
+
+@app.post("/api/chat")
+def chat(req: ChatReq):
+    sess = _get_session(req.user_id)
+
+    msg = (req.message or "").strip()
+    if not msg:
+        return {"reply": "", "session_status_md": render_session_status(req.learning_mode, sess["weaknesses"], sess["cognitive_state"]), "refs": []}
+
+    resolved_lang = detect_language(msg, req.language_preference)
+
+    sess["weaknesses"] = update_weaknesses_from_message(msg, sess["weaknesses"])
+    sess["cognitive_state"] = update_cognitive_state_from_message(msg, sess["cognitive_state"])
+
+    # academic gating：沿用你 app.py 的 is_academic_query 逻辑（建议后续挪进 api utils）
+    rag_context_text, rag_used_chunks = retrieve_relevant_chunks(msg, sess["rag_chunks"])
+
+    start_ts = time.time()
+    answer, new_history = chat_with_clare(
+        message=msg,
+        history=sess["history"],
+        model_name=sess["model_name"],
+        language_preference=resolved_lang,
+        learning_mode=req.learning_mode,
+        doc_type=req.doc_type,
+        course_outline=sess["course_outline"],
+        weaknesses=sess["weaknesses"],
+        cognitive_state=sess["cognitive_state"],
+        rag_context=rag_context_text,
+    )
+    latency_ms = (time.time() - start_ts) * 1000.0
+
+    sess["history"] = new_history
+
+    refs = [
+        {"source_file": c.get("source_file"), "section": c.get("section")}
+        for c in (rag_used_chunks or [])
+    ]
+
+    return {
+        "reply": answer,
+        "session_status_md": render_session_status(req.learning_mode, sess["weaknesses"], sess["cognitive_state"]),
+        "refs": refs,
+        "latency_ms": latency_ms,
+    }
+
+@app.post("/api/upload")
+async def upload(
+    user_id: str = Form(...),
+    doc_type: str = Form(...),
+    file: UploadFile = File(...),
+):
+    sess = _get_session(user_id)
+
+    # 保存到临时文件
+    tmp_path = f"/tmp/{file.filename}"
+    content = await file.read()
+    with open(tmp_path, "wb") as f:
+        f.write(content)
+
+    # 更新 topics（仅 syllabus）
+    if doc_type == "Syllabus":
+        # 复用你 syllabus_utils 的逻辑：它期待 gradio file_obj，有 .name
+        class _F: pass
+        fo = _F(); fo.name = tmp_path
+        sess["course_outline"] = extract_course_topics_from_file(fo, doc_type)
+
+    # 更新 rag chunks（所有文件都可）
+    new_chunks = build_rag_chunks_from_file(tmp_path, doc_type)
+    sess["rag_chunks"] = (sess["rag_chunks"] or []) + (new_chunks or [])
+
+    status_md = f"✅ Loaded Module 10 base reading + uploaded {doc_type} file."
+    return {"ok": True, "added_chunks": len(new_chunks), "status_md": status_md}
+
+class ExportReq(BaseModel):
+    user_id: str
+    learning_mode: str
+
+@app.post("/api/export")
+def api_export(req: ExportReq):
+    sess = _get_session(req.user_id)
+    md = export_conversation(
+        sess["history"],
+        sess["course_outline"],
+        req.learning_mode,
+        sess["weaknesses"],
+        sess["cognitive_state"],
+    )
+    return {"markdown": md}
+
+class SummaryReq(BaseModel):
+    user_id: str
+    learning_mode: str
+    language_preference: str = "Auto"
+
+@app.post("/api/summary")
+def api_summary(req: SummaryReq):
+    sess = _get_session(req.user_id)
+    md = summarize_conversation(
+        sess["history"],
+        sess["course_outline"],
+        sess["weaknesses"],
+        sess["cognitive_state"],
+        sess["model_name"],
+        req.language_preference,
+    )
+    return {"markdown": md}
+
+@app.get("/api/memoryline")
+def memoryline(user_id: str):
+    # v1 写死也可以；前端只渲染
+    _ = _get_session(user_id)
+    return {"next_review_label": "T+7", "progress_pct": 0.4}