app.py

import os
import json
import uuid
import time
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional
from dotenv import load_dotenv
load_dotenv()

from fastmcp import FastMCP

# -------- Config: data root resolution (robust, works locally & on HF) --------
def resolve_data_root() -> Path:
    # 1) Env var wins if set
    env = os.getenv("DATA_ROOT")
    if env:
        p = Path(env).expanduser().resolve()
        if p.exists():
            return p

    # 2) Try common repo-relative locations (first that exists wins)
    candidates = [
        Path.cwd() / "TRAINING DATA",   # your exact folder with space
        Path.cwd() / "training_data",
        Path.cwd() / "data",
    ]
    for c in candidates:
        if c.exists():
            return c.resolve()

    # 3) Last resort: create ./TRAINING DATA to avoid crashes
    fallback = Path.cwd() / "TRAINING DATA"
    fallback.mkdir(parents=True, exist_ok=True)
    return fallback.resolve()

DATA_ROOT: Path = resolve_data_root()
STUDENTS_DIR: Path = DATA_ROOT / "students"

# -------- Small utilities --------
def _receipt() -> Dict[str, Any]:
    return {
        "tool_used": True,
        "server_time": datetime.now(timezone.utc).isoformat(),
        "request_id": str(uuid.uuid4()),
        "data_root": str(DATA_ROOT),
    }

def _norm(s: Optional[str]) -> str:
    return (s or "").strip()

def _ci_contains(hay: Optional[str], needle: Optional[str]) -> bool:
    return _norm(needle).lower() in _norm(hay).lower()

# -------- Load metadata (from DATA_ROOT/students/*/metadata.json and/or DATA_ROOT/metadata.json) --------
# In-memory index: { student_name_lower: {"name":..., "email":..., ...} }
_METADATA_BY_STUDENT: Dict[str, Dict[str, Any]] = {}

def _load_all_metadata() -> None:
    global _METADATA_BY_STUDENT
    _METADATA_BY_STUDENT = {}

    # A) Per-student folders
    if STUDENTS_DIR.exists():
        for student_dir in sorted(STUDENTS_DIR.iterdir()):
            if not student_dir.is_dir():
                continue
            meta_file = student_dir / "metadata.json"
            if meta_file.exists():
                try:
                    data = json.loads(meta_file.read_text(encoding="utf-8"))
                except Exception:
                    continue
                # Ensure a 'name' field; default to directory name
                name = data.get("name") or student_dir.name
                data["name"] = name
                data["__path"] = str(meta_file)
                _METADATA_BY_STUDENT[_norm(name).lower()] = data

    # B) Optional top-level metadata.json (may contain a list or a dict of students)
    top_meta = DATA_ROOT / "metadata.json"
    if top_meta.exists():
        try:
            blob = json.loads(top_meta.read_text(encoding="utf-8"))
            # Accept either:
            #  - {"students":[{...},{...}]}
            #  - [{"name":..., ...}, ...]
            #  - {"<name>": {...}, ...}
            candidates: List[Dict[str, Any]] = []
            if isinstance(blob, dict) and "students" in blob and isinstance(blob["students"], list):
                candidates = blob["students"]
            elif isinstance(blob, list):
                candidates = blob
            elif isinstance(blob, dict):
                for k, v in blob.items():
                    if isinstance(v, dict):
                        v.setdefault("name", k)
                        candidates.append(v)

            for data in candidates:
                name = data.get("name")
                if not name:
                    continue
                data["__path"] = str(top_meta)
                _METADATA_BY_STUDENT[_norm(name).lower()] = data
        except Exception:
            pass

# Initial load
_load_all_metadata()

# -------- OpenAI embeddings (for Pinecone RAG) --------
from openai import OpenAI
_openai_client: Optional[OpenAI] = None
_EMBED_MODEL = os.getenv("OPENAI_EMBEDDING_MODEL", "text-embedding-3-small")
def _embed(texts: List[str]) -> List[List[float]]:
    """
    Embed a batch of strings using OpenAI embeddings.
    """
    global _openai_client
    if _openai_client is None:
        _openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    # OpenAI Python SDK v1 returns .data with embeddings in order
    resp = _openai_client.embeddings.create(model=_EMBED_MODEL, input=texts)
    return [d.embedding for d in resp.data]

# -------- Pinecone client --------
from pinecone import Pinecone as _Pinecone

_pine: Optional[_Pinecone] = None
def _pc() -> _Pinecone:
    global _pine
    if _pine is None:
        api_key = os.getenv("PINECONE_API_KEY")
        if not api_key:
            raise RuntimeError("PINECONE_API_KEY not set")
        _pine = _Pinecone(api_key=api_key)
    return _pine

def _pine_index():
    pc = _pc()
    index_name = os.getenv("PINECONE_INDEX_NAME")
    if not index_name:
        raise RuntimeError("PINECONE_INDEX_NAME not set")
    return pc.Index(index_name)

# -------- MCP server --------
mcp = FastMCP("ProjectRAGServer")

@mcp.tool
def add(a: int, b: int) -> Dict[str, Any]:
    """Add two numbers."""
    return {"result": int(a) + int(b), "_receipt": _receipt()}

@mcp.tool
def list_students() -> Dict[str, Any]:
    """Return all known student names."""
    names = sorted(v.get("name") for v in _METADATA_BY_STUDENT.values() if v.get("name"))
    return {"students": names, "count": len(names), "_receipt": _receipt()}

@mcp.tool
def get_student_metadata(name: str) -> Dict[str, Any]:
    """Return full metadata for a student by name (case-insensitive)."""
    key = _norm(name).lower()
    data = _METADATA_BY_STUDENT.get(key)
    if not data:
        return {"error": f"Student '{name}' not found.", "_receipt": _receipt()}
    return {"metadata": data, "_receipt": _receipt()}

@mcp.tool
def get_student_email(name: str) -> Dict[str, Any]:
    """Return the email address for a student by name."""
    key = _norm(name).lower()
    data = _METADATA_BY_STUDENT.get(key)
    if not data:
        return {"error": f"Student '{name}' not found.", "_receipt": _receipt()}
    email = data.get("email")
    if not email:
        return {"error": f"No email in metadata for '{data.get('name')}'.", "_receipt": _receipt()}
    return {"name": data.get("name"), "email": email, "_receipt": _receipt()}

@mcp.tool
def search_student_by_field(field: str, value: str) -> Dict[str, Any]:
    """
    Case-insensitive contains() search across any metadata field.
    Example: field='department', value='Computer'
    """
    f = _norm(field)
    val = _norm(value)
    if not f:
        return {"error": "Field must be provided.", "_receipt": _receipt()}
    matches: List[Dict[str, Any]] = []
    for meta in _METADATA_BY_STUDENT.values():
        if f not in meta:
            continue
        v = meta.get(f)
        # Allow both strings and list-of-strings
        if isinstance(v, str) and _ci_contains(v, val):
            matches.append({"name": meta.get("name"), "match_value": v, "metadata": meta})
        elif isinstance(v, list) and any(_ci_contains(x, val) for x in v if isinstance(x, str)):
            matches.append({"name": meta.get("name"), "match_value": v, "metadata": meta})
    return {"matches": matches, "count": len(matches), "_receipt": _receipt()}

@mcp.tool
def reload_metadata() -> Dict[str, Any]:
    """Reload metadata from disk (useful after updating files)."""
    t0 = time.time()
    _load_all_metadata()
    dt = round((time.time() - t0) * 1000.0, 2)
    return {"ok": True, "students": len(_METADATA_BY_STUDENT), "ms": dt, "_receipt": _receipt()}

@mcp.tool
def search_rag(query: str, top_k: int = 3, namespace: Optional[str] = None) -> Dict[str, Any]:
    """
    Semantic search over your Pinecone index using OpenAI embeddings.
    Returns top_k matches with metadata.
    Env required: OPENAI_API_KEY, PINECONE_API_KEY, PINECONE_INDEX_NAME
    Optional: OPENAI_EMBEDDING_MODEL (default: text-embedding-3-small)
    """
    q = _norm(query)
    if not q:
        return {"error": "Query must not be empty.", "_receipt": _receipt()}

    try:
        vec = _embed([q])[0]
    except Exception as e:
        return {"error": f"Embedding failed: {e}", "_receipt": _receipt()}

    try:
        idx = _pine_index()
    except Exception as e:
        return {"error": f"Pinecone init failed: {e}", "_receipt": _receipt()}

    try:
        resp = idx.query(
            vector=vec,
            top_k=max(1, min(int(top_k), 50)),
            include_metadata=True,
            namespace=namespace or None,
        )
    except Exception as e:
        return {"error": f"Pinecone query failed: {e}", "_receipt": _receipt()}

    results = []
    for m in getattr(resp, "matches", []) or []:
        results.append({
            "id": getattr(m, "id", None),
            "score": getattr(m, "score", None),
            "metadata": getattr(m, "metadata", None),
        })

    return {
        "query": q,
        "model": _EMBED_MODEL,
        "top_k": top_k,
        "results": results,
        "_receipt": _receipt(),
    }


@mcp.tool
def find_candidates(
    topics: List[str] | str,
    n: int = 2,
    prefer_fields: Optional[List[str]] = None,
    ensure_distinct_projects: bool = True,
    top_k_per_topic: int = 5,
    namespace: Optional[str] = None,
) -> Dict[str, Any]:
    """
    Find N student candidates for the given topic(s).
    1) Try metadata search first across prefer_fields (default sensible set).
    2) If still short, fallback to Pinecone RAG per topic.
    3) Aggregate by student with evidence; enforce distinct projects if requested.

    Args:
      topics: A string or list of topic strings (e.g., "reinforcement learning" or ["RL","traffic"]).
      n: number of distinct students to return.
      prefer_fields: metadata fields to search (case-insensitive contains). Defaults to
                     ["research_interests", "skills", "keywords", "department", "project_title", "summary"].
      ensure_distinct_projects: if True, avoid picking >1 student with the same project_title.
      top_k_per_topic: RAG candidates to pull per topic.
      namespace: optional Pinecone namespace.

    Returns:
      {
        "requested_topics": [ ... ],
        "candidates": [
          {
            "student": "Full Name",
            "email": "name@example.com",
            "project_title": "...",
            "coverage": 2,               # how many topics this student matched
            "score": 0.74,               # aggregate/max score from RAG/metadata (metadata hits use 1.0)
            "evidence": [
              { "topic":"...", "source":"metadata|rag", "field":"...", "snippet":"...", "score":1.0 },
              ...
            ],
            "metadata": {...}            # full student metadata
          },
          ...
        ],
        "_receipt": {...}
      }
    """
    # --- normalize topics ---
    if isinstance(topics, str):
        topic_list = [_norm(topics)]
    else:
        topic_list = [_norm(t) for t in topics if _norm(t)]
    topic_list = [t for t in topic_list if t]
    if not topic_list:
        return {"error": "No non-empty topics provided.", "_receipt": _receipt()}

    # --- fields to prefer in metadata search ---
    fields = prefer_fields or [
        "research_interests", "skills", "keywords", "department",
        "project_title", "summary", "areas", "tags"
    ]

    # --- accumulator keyed by student key (lowercased name) ---
    by_student: Dict[str, Dict[str, Any]] = {}

    def _add_hit(student_key: str, display_name: str, meta: Dict[str, Any], topic: str,
                 source: str, score: float, field: Optional[str] = None, snippet: Optional[str] = None):
        row = by_student.setdefault(student_key, {
            "student": display_name,
            "email": meta.get("email"),
            "project_title": meta.get("project_title") or meta.get("title") or meta.get("project"),
            "metadata": meta,
            "coverage": 0,
            "score": 0.0,
            "evidence": [],
        })
        # add evidence
        row["evidence"].append({
            "topic": topic,
            "source": source,
            "field": field,
            "snippet": snippet,
            "score": score
        })
        # recompute coverage & score
        covered = {ev["topic"] for ev in row["evidence"]}
        row["coverage"] = len(covered)
        # use max score as a simple aggregate; you can switch to average if you prefer
        row["score"] = max([ev.get("score") or 0.0 for ev in row["evidence"]])

    # --- 1) METADATA PASS ---
    for topic in topic_list:
        for skey, meta in _METADATA_BY_STUDENT.items():
            disp = meta.get("name") or skey
            matched = False
            for f in fields:
                v = meta.get(f)
                if isinstance(v, str) and _ci_contains(v, topic):
                    _add_hit(skey, disp, meta, topic, source="metadata", score=1.0, field=f, snippet=v[:240])
                    matched = True
                    break
                elif isinstance(v, list) and any(isinstance(x, str) and _ci_contains(x, topic) for x in v):
                    # join just for snippet preview
                    joined = ", ".join([x for x in v if isinstance(x, str)])[:240]
                    _add_hit(skey, disp, meta, topic, source="metadata", score=1.0, field=f, snippet=joined)
                    matched = True
                    break
            # if matched, we already recorded; continue to next student

    # If we already have enough diverse candidates, great—but still do RAG to improve coverage if some topics lack hits
    have_students = set(by_student.keys())

    # --- Which topics are still weak in coverage from metadata alone? ---
    def _topic_covered(t: str) -> bool:
        for row in by_student.values():
            if any(ev["topic"] == t for ev in row["evidence"] if ev["source"] == "metadata"):
                return True
        return False

    topics_needing_rag = [t for t in topic_list if not _topic_covered(t)]

    # --- 2) RAG PASS (only for topics that missed in metadata) ---
    if topics_needing_rag:
        try:
            idx = _pine_index()
        except Exception as e:
            # If Pinecone not configured, return what we have
            return {
                "requested_topics": topic_list,
                "candidates": sorted(
                    list(by_student.values()),
                    key=lambda r: (-r["coverage"], -r["score"], (r["student"] or ""))),
                "_receipt": _receipt() | {"warning": f"Skipping RAG: {e}"}
            }

        for topic in topics_needing_rag:
            try:
                vec = _embed([topic])[0]
                resp = idx.query(
                    vector=vec,
                    top_k=max(1, min(int(top_k_per_topic), 50)),
                    include_metadata=True,
                    namespace=namespace or None,
                )
            except Exception as e:
                # continue with others
                continue

            for m in getattr(resp, "matches", []) or []:
                md = getattr(m, "metadata", None) or {}
                score = float(getattr(m, "score", 0.0) or 0.0)

                # Try to resolve student name from metadata keys commonly used
                cand_name = md.get("student") or md.get("student_name") or md.get("name") or md.get("author")
                # Else attempt to infer student from a source path like ".../students/Ahmed/summary.txt"
                if not cand_name:
                    src = md.get("source_path") or md.get("path") or md.get("file") or ""
                    # heuristic: find a segment after "students"
                    parts = [p for p in str(src).replace("\\", "/").split("/") if p]
                    if "students" in parts:
                        idx_students = parts.index("students")
                        if idx_students + 1 < len(parts):
                            cand_name = parts[idx_students + 1]

                if not cand_name:
                    # cannot attribute to a student; skip
                    continue

                skey = _norm(cand_name).lower()
                # prefer known metadata if we already have it
                meta = _METADATA_BY_STUDENT.get(skey, {"name": cand_name})
                snippet = md.get("text") or md.get("chunk") or md.get("content") or ""
                if isinstance(snippet, str):
                    snippet = snippet.strip()[:240]
                else:
                    snippet = str(snippet)[:240]
                _add_hit(skey, meta.get("name") or cand_name, meta, topic, source="rag", score=score, field=None, snippet=snippet)

    # --- RANK & SELECT ---
    pool = list(by_student.values())
    # primary: more topic coverage; secondary: higher score; tertiary: name
    pool.sort(key=lambda r: (-r["coverage"], -float(r.get("score") or 0.0), (r.get("student") or "")))

    selected: List[Dict[str, Any]] = []
    used_projects: set[str] = set()

    for row in pool:
        if ensure_distinct_projects:
            ptitle = (row.get("project_title") or "").strip().lower()
            if ptitle and ptitle in used_projects:
                # same project title already present → skip to keep them distinct
                continue
            if ptitle:
                used_projects.add(ptitle)
        selected.append(row)
        if len(selected) >= max(1, int(n)):
            break

    return {
        "requested_topics": topic_list,
        "candidates": selected,
        "_receipt": _receipt()
    }
# ---- HTTP runner for HF Space / local run ----
if __name__ == "__main__":
    import uvicorn
    port = int(os.environ.get("PORT", 7860))
    # HTTP, path /mcp
    mcp.run(transport="http", host="0.0.0.0", port=port, path="/mcp")