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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+index/chroma_v3/chroma.sqlite3 filter=lfs diff=lfs merge=lfs -text
+static/img/11.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,10 +1,188 @@
----
-title: Materialmind2
-emoji: 🏃
-colorFrom: gray
-colorTo: indigo
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+MaterialMind
+
+Decisions, not summaries. MaterialMind is an LLM-powered materials-selection assistant that turns engineering requirements into an evidence-backed, ranked shortlist with page-level citations. It runs locally (Flask + Ollama) and searches your own PDF corpus via a lightweight RAG index.
+
+What MaterialMind does
+
+• Retrieves → Ranks → Explains: finds the most relevant pages, scores candidate materials against your constraints, and explains trade-offs with citations.
+• Local and private: your PDFs never leave your machine.
+• Robust ingestion: handles real-world PDFs (papers, standards, reports, textbooks); incremental updates.
+• Friendly UI: clean dark theme, background tiles, live weight controls (accepts 40/30/20/10 or 0.4/0.3/0.2/0.1).
+• Exact math: backend normalizes weights to sum to exactly 1.0000.
+
+Repository layout
+
+materialmind/ (your corpus lives here)
+├─ sources/ (drop your PDFs here)
+└─ index/ (vector DB; auto-created)
+
+rag_mini.py (RAG: index/search/ask/answer/export)
+app_user.py (Flask app for end users)
+templates/ (base.html, index.html, results.html)
+static/ (styles.css and images: 11.png, 22.jpg)
+
+Note: We work with PDFs you drop into materialmind/sources (papers, standards, reports). You don’t need vendor datasheets; if you have them as PDFs, drop them in like any other PDF.
+
+Prerequisites
+
+• Python 3.10+
+• pip and a virtual environment
+• Ollama (for model-generated answers). Example model: qwen2.5:7b-instruct
+• Optional: ocrmypdf + tesseract (only for scanned PDFs with no selectable text)
+
+Installation
+
+Create and activate a virtual environment.
+
+macOS / Linux:
+python3 -m venv .venv
+source .venv/bin/activate
+
+Windows (PowerShell):
+python -m venv .venv
+.venv\Scripts\Activate.ps1
+
+Install dependencies.
+
+Apple Silicon (macOS):
+pip install -U fastembed onnxruntime-silicon chromadb pypdf pymupdf markdown filelock flask flask-cors
+
+Linux/Windows:
+pip install -U fastembed onnxruntime chromadb pypdf pymupdf markdown filelock flask flask-cors
+
+Install and start Ollama; pull a local model.
+
+macOS (example):
+brew install ollama
+ollama serve &
+ollama pull qwen2.5:7b-instruct
+
+Confirm paths.
+
+rag_mini.py uses a folder named “materialmind” next to the script:
+BASE_DIR = Path(file).resolve().parent / "materialmind"
+
+Ensure the folder exists:
+materialmind/sources (create this and drop PDFs here)
+
+Add your PDFs
+
+Drop research papers, standards, reports, and textbooks into:
+materialmind/sources
+
+Tips:
+• Prefer publisher PDFs (selectable text).
+• If a PDF is scanned (no text), OCR it:
+ocrmypdf input.pdf output_ocr.pdf
+then place output_ocr.pdf in materialmind/sources
+
+Build or update the index
+
+First time (full index):
+python rag_mini.py --rebuild
+
+Later (only changed/new PDFs):
+python rag_mini.py --update
+
+Optional backup:
+python rag_mini.py --backup
+
+Optional export to JSONL (for inspection):
+python rag_mini.py --export-json ./materialmind_dump.jsonl
+
+Quick retrieval check (no model required)
+
+Example:
+python rag_mini.py --ask "Which alloys resist pitting in seawater better than 316L?"
+
+You will see top-k snippets with file:page citations from your PDFs.
+
+Run the UI
+
+Start the Flask app:
+python app_user.py
+
+Open:
+http://127.0.0.1:5000/
+
+Usage:
+• Fill environment/temperature/constraints.
+• Set weights as percentages (e.g., 40/30/20/10) or fractions (0.4/0.3/0.2/0.1). The form enables the button only when the sum equals 100% (or 1.0).
+• Click “Get ranked shortlist” to see the ranked table, material cards, and page-level citations.
+
+How it works
+
+• Retrieval: FastEmbed (ONNX) creates embeddings; Chroma stores and retrieves chunks with file + page metadata.
+• Decision layer: your constraints and weights guide the model to produce a structured shortlist JSON (name, score, reasons, trade-offs, citations).
+• Local LLM: by default qwen2.5:7b-instruct via Ollama; you can swap models freely.
+
+CLI reference (rag_mini.py)
+
+--rebuild rebuild the entire index from PDFs in materialmind/sources
+--update incremental index of only changed/new PDFs
+--backup copy the current index to a timestamped folder
+--export-json PATH dump records to JSONL (optional)
+--ask "question" retrieval only with citations
+--answer "question" --model NAME --k N --show
+retrieval plus local LLM answer (Ollama required)
+
+Customization
+
+Change model (Ollama):
+ollama pull mistral:7b-instruct
+then set “Model” in the UI to mistral:7b-instruct
+
+Tune retrieval (rag_mini.py):
+CHUNK_CHARS, CHUNK_OVERLAP, DEFAULT_TOPK
+
+Swap embedding model (rag_mini.py):
+EMB_MODEL = "BAAI/bge-small-en-v1.5" (good default). Other FastEmbed models also work.
+
+Theme colors:
+Edit CSS variables in static/styles.css (:root { … }).
+
+Troubleshooting
+
+“Get ranked shortlist” button disabled:
+Enter weights as 40/30/20/10 or 0.4/0.3/0.2/0.1 until the sum reads 100%.
+
+Ollama not found / model errors:
+Install/start/pull as above. Retrieval still works without Ollama; only model-generated answers require it.
+
+Chroma lock / mutex errors:
+Close other processes using the index. If stuck:
+python rag_mini.py --backup
+rm -rf materialmind/index/chroma_v3
+python rag_mini.py --rebuild
+
+No text in a PDF:
+OCR it with ocrmypdf, then re-index.
+
+Port already in use:
+Change the host/port in app_user.py.
+
+Privacy
+
+Everything runs locally by default. Your PDFs are indexed on disk and never uploaded. Remove PDFs from materialmind/sources and run --update to de-index.
+
+Roadmap
+
+Team/on-prem sharing, rule/property packs (e.g., PREN/oxidation windows/standards checks), uploads & tagging UI, LoRA adapter for stricter JSON, evaluation harness for grounding and JSON validity.
+
+License
+
+MIT (or your chosen permissive license). Add a LICENSE file to the repository.
+
+One-minute demo
+
+Drop a few corrosion or seawater PDFs into materialmind/sources.
+
+Build: python rag_mini.py --rebuild
+
+Run UI: python app_user.py
+
+Query seawater at 20–25 °C, UTS ≥ 600 MPa, weights 50/30/10/10.
+
+Show ranked shortlist, open a citation (file:page).
+
+Add another PDF, run --update, rerun the query, and show the new citation.

app_user.py

@@ -0,0 +1,291 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+MaterialMind – Flask app (form page + results page)
+- Priorities via dropdowns (no numbers shown to user)
+- Each criterion weight is independent (0..100); NOT normalized
+- RAG retrieval + optional Ollama LLM for ranked shortlist with citations
+"""
+import shutil
+from decimal import Decimal
+import re, json, textwrap, subprocess
+from typing import List, Tuple, Any, Dict
+from flask import Flask, request, render_template, redirect, url_for, flash
+from flask_cors import CORS
+from filelock import FileLock
+
+# RAG helpers (your existing file)
+from rag_mini import search, ensure_dirs, DATA_DIR, DEFAULT_TOPK, DEFAULT_MODEL
+
+app = Flask(__name__)
+app.secret_key = "change-me"   # set a strong secret for production
+CORS(app)
+
+BASE_DIR = DATA_DIR.parent
+LOCK_PATH = BASE_DIR / ".rag_lock"
+
+# ---------- Utilities ----------
+def has_ollama() -> bool:
+    return shutil.which("ollama") is not None
+
+def call_ollama(model: str, prompt: str) -> str:
+    try:
+        out = subprocess.run(["ollama", "run", model, prompt],
+                             check=True, capture_output=True, text=True)
+        return out.stdout.strip()
+    except FileNotFoundError:
+        return ("[Error] Ollama not found. Install: brew install ollama\n"
+                "Run: ollama serve &\n"
+                f"Pull: ollama pull {model}")
+    except subprocess.CalledProcessError as e:
+        return f"[Error] ollama run failed: {e.stderr.strip() or e.stdout.strip()}"
+
+def to_dec(x, default: int) -> Decimal:
+    try:
+        s = (x or "").strip()
+        return Decimal(s if s else str(default))
+    except Exception:
+        return Decimal(default)
+
+def _to_float(x):
+    try:
+        return float(x)
+    except Exception:
+        return None
+
+def normalize_candidates_for_display(cands: List[Dict[str, Any]], max_total: float = 400.0) -> List[Dict[str, Any]]:
+    """
+    Ensure each candidate has:
+      - score_raw (0..400)
+      - score_pct (0..100) for UI
+    Accepts:
+      - c["score"] as number (0..400) OR fraction (0..1) OR string "350 / 400" or "87%"
+      - or c["score_pct"]
+      - or c["subscores"] dict (sums 4× [0..100] = 0..400)
+    """
+    for c in cands:
+        # If model already supplied a percent, trust it (and clamp)
+        if "score_pct" in c and c["score_pct"] is not None:
+            try:
+                pct = float(c["score_pct"])
+                c["score_pct"] = max(0.0, min(100.0, pct))
+                # derive a raw for sorting if not provided
+                c.setdefault("score_raw", c["score_pct"] * 4.0)
+                continue
+            except Exception:
+                pass
+
+        raw = None
+        v = c.get("score")
+
+        # direct numeric
+        if isinstance(v, (int, float)):
+            f = float(v)
+            if 0.0 <= f <= 1.5:
+                raw = max(0.0, min(max_total, f * max_total))  # treat <=1.5 as fraction
+            else:
+                raw = max(0.0, min(max_total, f))
+
+        # string patterns
+        if raw is None and isinstance(v, str):
+            s = v.strip()
+            m = re.search(r"^\s*([\d.]+)\s*/\s*([\d.]+)\s*$", s)
+            if m:
+                num, den = _to_float(m.group(1)), _to_float(m.group(2))
+                if num is not None and den and den > 0:
+                    raw = max_total * (num / den)
+            if raw is None:
+                m2 = re.search(r"^\s*([\d.]+)\s*%\s*$", s)
+                if m2:
+                    p = _to_float(m2.group(1))
+                    if p is not None:
+                        raw = max_total * (p / 100.0)
+            if raw is None:
+                f = _to_float(s)
+                if f is not None:
+                    if 0.0 <= f <= 1.5:
+                        raw = max_total * f
+                    else:
+                        raw = f
+
+        # sum of subscores
+        if raw is None:
+            subs = c.get("subscores") or {}
+            if isinstance(subs, dict) and subs:
+                ssum = 0.0
+                for sv in subs.values():
+                    fv = _to_float(sv)
+                    if fv is not None:
+                        ssum += max(0.0, min(100.0, fv))
+                raw = ssum
+
+        if raw is None:
+            raw = 0.0
+
+        raw = max(0.0, min(max_total, float(raw)))
+        c["score_raw"] = raw
+        c["score_pct"] = round((raw / max_total) * 100.0, 1)
+
+    # sort by raw descending
+    cands.sort(key=lambda z: z.get("score_raw", 0.0), reverse=True)
+    return cands
+
+# ---------- Prompting ----------
+SYSTEM_RULES = """You are MaterialMind, a materials-selection assistant.
+Return two things:
+1) JSON with a ranked shortlist:
+{
+  "candidates": [
+    {
+      "name": "string",
+      "score": 0,            // 0..400 (sum of 4 independent 0..100 weighted utilities)
+      "score_pct": 0,        // score/4 -> 0..100 for display
+      "reasons": ["..."],
+      "tradeoffs": ["..."],
+      "citations": ["[1]", "[2]"]
+    }
+  ]
+}
+2) After the JSON, 3–6 concise bullets on trade-offs.
+
+Rules:
+- Use only provided context (no fabrication). Cite with [1], [2] etc.
+- Utilities per criterion are in [0,1]. Cost utility increases as cost decreases.
+- Weights for performance, stability, cost, availability are independent 0..100 (NOT normalized).
+- Prefer pitting/crevice metrics in chloride questions; keep units explicit.
+"""
+
+ANSWER_TEMPLATE = """{rules}
+
+User constraints:
+- Environment: {environment}
+- Temperature: {temperature}
+- Min UTS (MPa): {min_uts}
+- Max density (g/cm^3): {max_density}
+- Budget: {budget}
+- Process: {process}
+
+Independent priorities (0..100 each):
+- performance={w_perf}, stability={w_stab}, cost={w_cost}, availability={w_avail}
+
+Question:
+{question}
+
+Context snippets (numbered):
+{context}
+
+Citations:
+{citations}
+
+Now, first output ONLY the JSON block, then the short narrative.
+"""
+
+def format_context(hits: List[Tuple[str, str]]) -> Tuple[str, str]:
+    blocks, cites = [], []
+    for i, (text, cite) in enumerate(hits, 1):
+        snippet = textwrap.shorten(text.replace("\n", " "), width=450, placeholder=" …")
+        blocks.append(f"[{i}] {snippet}")
+        cites.append(f"[{i}] {cite}")
+    return "\n".join(blocks), "\n".join(cites)
+
+def extract_json_block(text: str):
+    m = re.search(r"```json\s*(\{.*?\})\s*```", text, flags=re.S | re.I)
+    s = m.group(1) if m else None
+    if not s:
+        m2 = re.search(r"(\{(?:[^{}]|(?1))*\})", text, flags=re.S)
+        s = m2.group(1) if m2 else None
+    if not s:
+        return None
+    try:
+        return json.loads(s)
+    except Exception:
+        last = s.rfind("}")
+        if last != -1:
+            try:
+                return json.loads(s[:last+1])
+            except Exception:
+                return None
+        return None
+
+# ---------- Routes ----------
+@app.get("/")
+def index():
+    return render_template("index.html", default_model=DEFAULT_MODEL, default_k=DEFAULT_TOPK)
+
+@app.post("/recommend")
+def recommend():
+    # basics
+    environment  = request.form.get("environment", "").strip() or "seawater"
+    temperature  = request.form.get("temperature", "").strip() or "20–25 °C"
+    min_uts      = request.form.get("min_uts", "").strip() or "0"
+    max_density  = request.form.get("max_density", "").strip() or "100"
+    budget       = request.form.get("budget", "").strip() or "open"
+    process      = request.form.get("process", "").strip() or "any"
+
+    # hidden numeric weights from dropdowns (0..100 each; independent)
+    w_perf  = to_dec(request.form.get("w_perf"), 75)   # e.g., Very high -> 100
+    w_stab  = to_dec(request.form.get("w_stab"), 100)
+    w_cost  = to_dec(request.form.get("w_cost"), 75)   # "High" cost priority -> 100
+    w_avail = to_dec(request.form.get("w_avail"), 75)
+
+    model = (request.form.get("model", DEFAULT_MODEL) or DEFAULT_MODEL).strip()
+    try:
+        k = int(request.form.get("k", DEFAULT_TOPK))
+    except Exception:
+        k = DEFAULT_TOPK
+
+    question = (
+        f"For {environment} at {temperature}, shortlist materials that meet "
+        f"UTS ≥ {min_uts} MPa and density ≤ {max_density} g/cm^3. "
+        f"Consider budget={budget} and process={process}. "
+        f"Rank by performance, stability, cost, and availability."
+    )
+
+    hits = search(question, k=k)
+    if not hits:
+        flash("No context found. Please add sources and rebuild/update the index.", "error")
+        return redirect(url_for("index"))
+
+    ctx, cites = format_context(hits)
+
+    prompt = ANSWER_TEMPLATE.format(
+        rules=SYSTEM_RULES, environment=environment, temperature=temperature,
+        min_uts=min_uts, max_density=max_density, budget=budget, process=process,
+        w_perf=str(int(w_perf)), w_stab=str(int(w_stab)),
+        w_cost=str(int(w_cost)), w_avail=str(int(w_avail)),
+        question=question, context=ctx, citations=cites
+    )
+
+    # Call model (gracefully handle lock or missing ollama)
+    if not has_ollama():
+        raw = "[Ollama not found]\n\n" + prompt
+        candidates = []
+        flash("Ollama not found — showing retrieval context only.", "error")
+    else:
+        try:
+            LOCK_PATH.parent.mkdir(parents=True, exist_ok=True)
+            with FileLock(str(LOCK_PATH), timeout=1):
+                raw = call_ollama(model, prompt)
+        except Exception:
+            # If lock fails, try without lock instead of crashing
+            raw = call_ollama(model, prompt)
+        parsed = extract_json_block(raw) if raw else None
+        candidates = (parsed or {}).get("candidates", []) if parsed else []
+
+    # Always normalize to have score_pct and sorting
+    candidates = normalize_candidates_for_display(candidates, max_total=400.0)
+
+    return render_template(
+        "results.html",
+        candidates=candidates,
+        citations=cites.splitlines(),
+        environment=environment,
+        temperature=temperature,
+        raw_output=raw,
+        default_model=model,
+        default_k=k,
+    )
+
+if __name__ == "__main__":
+    ensure_dirs()
+    app.run(host="127.0.0.1", port=5000, debug=False)

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+  "sources/1 Unified Engineering.pdf": {
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+  },
+  "sources/Glasses as engineering materials A review.pdf": {
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+  "sources/Energetic-analysis-of-tube-drawing-processes-wi_2006_Journal-of-Materials-Pr.pdf": {
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+  "sources/Microstructure, mechanical and corrosion behavior of high strength AA7075 aluminium alloy friction stir weldse Effect of post weld heat treatment.pdf": {
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+  },
+  "sources/All_Materials_Complete_Dataset.xlsx": {
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+  "sources/Formability-of-Nb-bearing-ultra-high-strengt_2006_Journal-of-Materials-Proce.pdf": {
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+    "size": 533273,
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+  }
+}

rag_mini.py

@@ -0,0 +1,281 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+MaterialMind RAG (verbose + env override)
+- Index PDFs/MD/TXT
+- Chroma persistent DB
+- FastEmbed first, ST fallback
+- Rebuild / Update / Ask
+Env:
+  MATERIALMIND_DATA_DIR=/absolute/path/to/sources   # optional
+"""
+
+import os, re, uuid, argparse, textwrap, logging, warnings
+import hashlib, json, shutil, datetime
+from pathlib import Path
+from typing import Iterable, List, Tuple, Dict, Any
+
+# -------- PATHS --------
+ENV_DIR = os.getenv("MATERIALMIND_DATA_DIR")
+if ENV_DIR:
+    DATA_DIR = Path(ENV_DIR).expanduser().resolve()
+    BASE_DIR = DATA_DIR.parent
+else:
+    BASE_DIR = Path(__file__).resolve().parent
+    DATA_DIR = (BASE_DIR / "sources").resolve()
+
+DB_DIR = BASE_DIR / "index" / "chroma_v3"
+MANIFEST_PATH = BASE_DIR / "index" / "manifest.json"
+
+# -------- CONFIG --------
+EMB_MODEL = "BAAI/bge-small-en-v1.5"
+CHUNK_CHARS = 1200
+CHUNK_OVERLAP = 150
+DEFAULT_TOPK = 5
+DEFAULT_MODEL = "qwen2.5:7b-instruct"
+
+# Exported for app_user.py
+__all__ = ["search", "DATA_DIR", "DEFAULT_TOPK", "DEFAULT_MODEL"]
+
+logging.getLogger("pypdf").setLevel(logging.ERROR)
+warnings.filterwarnings("ignore", category=UserWarning, module="pypdf")
+
+def _lazy_imports():
+    global chromadb
+    import chromadb
+
+# ---- Embeddings ----
+_EMBED_FAST = None
+_EMBED_ST = None
+
+def init_embedder():
+    global _EMBED_FAST, _EMBED_ST
+    if _EMBED_FAST or _EMBED_ST:
+        return
+    try:
+        from fastembed import TextEmbedding
+        _EMBED_FAST = TextEmbedding(model_name=EMB_MODEL)
+        print(f"[EMB] FastEmbed: {EMB_MODEL}")
+    except Exception as e:
+        print(f"[WARN] FastEmbed not available ({e}); trying SentenceTransformers...")
+        from sentence_transformers import SentenceTransformer
+        _EMBED_ST = SentenceTransformer(EMB_MODEL)
+        print(f"[EMB] SentenceTransformers: {EMB_MODEL}")
+
+def embed_texts(texts: List[str]) -> List[List[float]]:
+    init_embedder()
+    if _EMBED_FAST is not None:
+        return [v for v in _EMBED_FAST.embed(texts)]
+    return _EMBED_ST.encode(texts, normalize_embeddings=True).tolist()
+
+# ---- FS helpers ----
+def ensure_dirs():
+    DATA_DIR.mkdir(parents=True, exist_ok=True)
+    DB_DIR.mkdir(parents=True, exist_ok=True)
+    MANIFEST_PATH.parent.mkdir(parents=True, exist_ok=True)
+
+def file_sig(path: Path):
+    h = hashlib.sha1()
+    with open(path, "rb") as f:
+        for chunk in iter(lambda: f.read(1<<20), b""):
+            h.update(chunk)
+    st = path.stat()
+    return {"sha1": h.hexdigest(), "size": st.st_size, "mtime": int(st.st_mtime)}
+
+def load_manifest():
+    if MANIFEST_PATH.exists():
+        try: return json.loads(MANIFEST_PATH.read_text())
+        except Exception: return {}
+    return {}
+
+def save_manifest(m): MANIFEST_PATH.write_text(json.dumps(m, indent=2))
+
+# ---- Loaders ----
+def normalize_spaces(t: str) -> str:
+    t = t.replace("\r", "\n")
+    t = re.sub(r"[ \t]+", " ", t)
+    t = re.sub(r"\n{3,}", "\n\n", t)
+    return t.strip()
+
+def load_text_from_pdf(path: Path):
+    # 1) PyMuPDF
+    try:
+        import fitz
+        doc = fitz.open(str(path))
+        empty = 0
+        for i, p in enumerate(doc):
+            txt = p.get_text("text").strip()
+            if txt: yield normalize_spaces(txt), i+1
+            else:   empty += 1
+        doc.close()
+        if empty == i+1:
+            print(f"[HINT] '{path.name}' looks scanned (no text). Try OCR.")
+        return
+    except Exception:
+        pass
+    # 2) pypdf fallback
+    try:
+        from pypdf import PdfReader
+        reader = PdfReader(str(path))
+        empty = 0
+        for i, p in enumerate(reader.pages):
+            try: raw = p.extract_text() or ""
+            except Exception: raw = ""
+            txt = normalize_spaces(raw)
+            if txt: yield txt, i+1
+            else:   empty += 1
+        if empty == i+1:
+            print(f"[HINT] '{path.name}' has no extractable text. OCR it.")
+    except Exception as e:
+        print(f"[WARN] {path.name}: {e}")
+
+def load_text_from_md_txt(path: Path) -> str:
+    try: raw = path.read_text(errors="ignore")
+    except Exception: raw = ""
+    return normalize_spaces(raw)
+
+def chunk(text: str, max_chars=CHUNK_CHARS, overlap=CHUNK_OVERLAP):
+    n = len(text)
+    if n <= max_chars:
+        if n > 0: yield text
+        return
+    i = 0
+    while i < n:
+        j = min(i + max_chars, n)
+        yield text[i:j]
+        i = j - overlap if j < n else j
+
+def iter_documents():
+    for f in DATA_DIR.rglob("*"):
+        if not f.is_file(): continue
+        ext = f.suffix.lower()
+        rel = f.relative_to(BASE_DIR).as_posix()
+        if ext == ".pdf":
+            any_txt = False
+            for page_txt, page in load_text_from_pdf(f):
+                any_txt = True
+                for c in chunk(page_txt):
+                    yield {"id": str(uuid.uuid4()), "text": c, "meta": {"source": rel, "page": page}}
+            if not any_txt:
+                yield {"id": str(uuid.uuid4()), "text": f"[NO-TEXT] {f.name}", "meta": {"source": rel, "page": None}}
+        elif ext in (".md", ".txt"):
+            txt = load_text_from_md_txt(f)
+            for c in chunk(txt):
+                yield {"id": str(uuid.uuid4()), "text": c, "meta": {"source": rel, "page": None}}
+
+# ---- DB ----
+def get_collection(reset=False):
+    _lazy_imports()
+    client = chromadb.PersistentClient(path=str(DB_DIR))
+    if reset:
+        try: client.delete_collection("materialmind")
+        except Exception: pass
+    return client.get_or_create_collection(name="materialmind")
+
+def add_batch(col, ids, docs, metas):
+    embs = embed_texts(docs)
+    col.add(ids=ids, documents=docs, metadatas=metas, embeddings=embs)
+
+def build_index(batch_size=256) -> int:
+    ensure_dirs()
+    print(f"[PATH] DATA_DIR = {DATA_DIR}")
+    print(f"[PATH] DB_DIR   = {DB_DIR}")
+    col = get_collection(reset=True)
+    ids, docs, metas, total = [], [], [], 0
+    print(f"[BUILD] Scanning {DATA_DIR} ...")
+    for doc in iter_documents():
+        if doc["text"].startswith("[NO-TEXT]"):
+            print(f"[INFO] Skipping unextractable: {doc['meta']['source']}")
+            continue
+        ids.append(doc["id"]); docs.append(doc["text"]); metas.append(doc["meta"])
+        if len(ids) >= batch_size:
+            add_batch(col, ids, docs, metas)
+            total += len(ids); ids, docs, metas = [], [], []
+            print(f"[BUILD] Added {total} chunks...")
+    if ids:
+        add_batch(col, ids, docs, metas); total += len(ids)
+    print(f"[BUILD] Done. Indexed {total} chunks.")
+    return total
+
+def update_index():
+    ensure_dirs()
+    print(f"[PATH] DATA_DIR = {DATA_DIR}")
+    print(f"[PATH] DB_DIR   = {DB_DIR}")
+    col = get_collection(reset=False)
+    manifest = load_manifest()
+    current = {f.relative_to(BASE_DIR).as_posix(): f for f in DATA_DIR.rglob("*") if f.is_file()}
+
+    # remove deleted
+    for src in list(manifest.keys()):
+        if src not in current:
+            col.delete(where={"source": src}); manifest.pop(src, None)
+            print(f"[DEL] {src}")
+
+    # add/refresh changed
+    for src, path in current.items():
+        try: sig = file_sig(path)
+        except Exception: continue
+        if manifest.get(src) == sig: continue
+        col.delete(where={"source": src})
+        added = 0
+        ext = path.suffix.lower()
+        if ext == ".pdf":
+            any_txt = False
+            for page_txt, page in load_text_from_pdf(path):
+                any_txt = True
+                for c in chunk(page_txt):
+                    add_batch(col, [str(uuid.uuid4())], [c], [{"source": src, "page": page}])
+                    added += 1
+            if not any_txt: print(f"[INFO] Skipping unextractable: {src}")
+        elif ext in (".md", ".txt"):
+            txt = load_text_from_md_txt(path)
+            for c in chunk(txt):
+                add_batch(col, [str(uuid.uuid4())], [c], [{"source": src, "page": None}])
+                added += 1
+        manifest[src] = sig
+        print(f"[UPD] {src} (+{added} chunks)")
+    save_manifest(manifest)
+    print("[UPDATE] Done.")
+
+def search(query: str, k: int = DEFAULT_TOPK) -> List[Tuple[str, str]]:
+    col = get_collection(reset=False)
+    qvec = embed_texts([query])[0]
+    res = col.query(query_embeddings=[qvec], n_results=k, include=["documents", "metadatas"])
+    hits = []
+    for doc, meta in zip(res.get("documents", [[]])[0], res.get("metadatas", [[]])[0]):
+        src = meta.get("source", "unknown"); page = meta.get("page")
+        cite = f"{src}" + (f":p.{page}" if page else "")
+        hits.append((doc, cite))
+    return hits
+
+# ---- CLI ----
+def main():
+    ap = argparse.ArgumentParser(description="MaterialMind RAG")
+    ap.add_argument("--rebuild", action="store_true")
+    ap.add_argument("--update", action="store_true")
+    ap.add_argument("--ask", type=str)
+    ap.add_argument("--k", type=int, default=DEFAULT_TOPK)
+    args = ap.parse_args()
+
+    ensure_dirs()
+
+    if args.rebuild:
+        total = build_index()
+        print(f"[BUILD] Indexed {total} chunks from {DATA_DIR}")
+
+    if args.update:
+        update_index()
+
+    if args.ask:
+        hits = search(args.ask, k=args.k)
+        if not hits:
+            print("No results. Add PDFs to DATA_DIR and --rebuild.")
+        else:
+            for i, (text, cite) in enumerate(hits, 1):
+                print(f"[{i}] {cite}\n{textwrap.shorten(text.replace(chr(10),' '), 600, placeholder=' ...')}\n")
+
+    if not any([args.rebuild, args.update, args.ask]):
+        print(f"DATA_DIR: {DATA_DIR}\nDB_DIR: {DB_DIR}\nUsage: --rebuild | --update | --ask \"question\"")
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,3 @@
+pdfminer.six==20231228
+pandas==2.2.2
+openpyxl==3.1.2

static/styles.css

@@ -0,0 +1,154 @@
+:root{
+  --bg: #0b1020;
+  --fg: #e9eefb;
+  --muted: #a7b0c3;
+  --card: #121a2b;
+  --accent: #6ae6c1;
+  --accent-2: #7aa2ff;
+  --danger: #ff6b6b;
+  --border: #23314f;
+}
+
+*{ box-sizing: border-box; }
+body{
+  margin: 0;
+  font-family: system-ui, -apple-system, Segoe UI, Roboto, Arial, sans-serif;
+  background: radial-gradient(1200px 600px at 10% -10%, #15213f 0%, #0b1020 60%, #070a14 100%);
+  color: var(--fg);
+}
+.wrap{ max-width: 1000px; margin: 0 auto; padding: 24px; }
+.muted{ color: var(--muted); }
+
+/* ---------- header ---------- */
+.hero{
+  text-align: center;
+  padding: 48px 24px 16px;
+  background:
+    radial-gradient(800px 260px at 50% 0%, rgba(122,162,255,0.2), transparent 60%),
+    linear-gradient(180deg, rgba(13,20,40,0.6), transparent);
+  border-bottom: 1px solid var(--border);
+}
+.hero h1{
+  margin: 0;
+  font-size: 42px;
+  letter-spacing: 0.5px;
+  color: #ffffff;
+  text-shadow: 0 6px 30px rgba(106,230,193,0.15);
+}
+
+/* ---------- image tiles ---------- */
+.bg-gallery{
+  display: grid;
+  grid-template-columns: repeat(2, minmax(220px, 1fr));
+  gap: 10px;
+  margin-bottom: 16px;
+}
+.bg-tile{
+  border-radius: 12px;
+  min-height: 120px;
+  background: #111 no-repeat center/cover;
+  /* Set per-tile image in HTML with: style="--bg: url('...')" */
+  background-image: var(--bg);
+  background-image: image-set(var(--bg));
+  box-shadow: inset 0 0 0 1px var(--border), 0 8px 24px rgba(0,0,0,0.25);
+  position: relative;
+}
+.bg-tile::after{
+  content:"";
+  position:absolute; inset:0;
+  background: linear-gradient(180deg, rgba(0,0,0,0) 40%, rgba(0,0,0,0.25));
+  border-radius:12px;
+}
+
+/* ---------- form layout ---------- */
+.grid{
+  display: grid;
+  grid-template-columns: repeat(2, minmax(260px, 1fr));
+  gap: 14px;
+  margin-bottom: 10px;
+}
+.grid label{ display: grid; gap: 6px; font-weight: 600; color: #d9e3ff; }
+input, select{
+  padding: 10px 12px;
+  background: #0e1627;
+  color: #e9eefb;
+  border: 1px solid var(--border);
+  border-radius: 8px;
+  outline: none;
+}
+input::placeholder{ color: #7d8bb0; }
+
+/* ---------- priorities (replaces weights) ---------- */
+.prefs{
+  grid-column: 1 / -1;
+  display: grid;
+  gap: 10px;
+  border: 1px solid var(--border);
+  padding: 12px;
+  border-radius: 10px;
+  background: #0c1323;
+}
+.prefs legend{ padding: 0 8px; color: #cfd9ff; }
+.pref-row{
+  display: grid;
+  grid-template-columns: 1fr 340px;
+  gap: 12px;
+  align-items: center;
+}
+
+/* (old .weights styles kept harmlessly; unused) */
+fieldset.weights{ display:none; }
+
+/* ---------- buttons ---------- */
+.btn{
+  display: inline-block;
+  padding: 12px 18px;
+  background: linear-gradient(135deg, var(--accent), var(--accent-2));
+  color: #07101f;
+  border: 0; border-radius: 10px;
+  font-weight: 700; cursor: pointer;
+  box-shadow: 0 8px 24px rgba(122,162,255,0.25);
+}
+.btn:disabled{ opacity: 0.6; cursor: not-allowed; }
+.btn.ghost{
+  background: transparent; color: var(--fg);
+  border: 1px solid var(--border);
+}
+.actions{ grid-column: 1 / -1; display: flex; gap: 10px; }
+
+/* ---------- results ---------- */
+.tbl{ width: 100%; border-collapse: collapse; margin-top: 12px; }
+.tbl th, .tbl td{
+  border: 1px solid var(--border);
+  padding: 10px; text-align: left; vertical-align: top;
+}
+.tbl th{ background: #0e182d; color: #cfe2ff; }
+
+.cards{
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(260px, 1fr));
+  gap: 12px; margin: 12px 0;
+}
+.card{
+  border: 1px solid var(--border);
+  border-radius: 12px;
+  padding: 12px;
+  background: var(--card);
+  box-shadow: 0 8px 16px rgba(0,0,0,0.25);
+}
+.mono{
+  white-space: pre-wrap; word-break: break-word;
+  background: #0b1326;
+  border: 1px solid var(--border);
+  padding: 10px; border-radius: 8px;
+  color: #d7e6ff;
+}
+
+/* ---------- flash ---------- */
+.flash p{
+  padding: 10px;
+  background: #2b0f14;
+  border: 1px solid #572131;
+  border-radius: 8px;
+  color: #ffd9df;
+}

templates/base.html

@@ -0,0 +1,23 @@
+<!-- base page -->
+<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+  <title>MaterialMind</title>
+  <meta name="viewport" content="width=device-width, initial-scale=1">
+  <link href="{{ url_for('static', filename='styles.css') }}" rel="stylesheet">
+</head>
+<body>
+<header class="wrap hero">
+  <h1>MaterialMind</h1>
+</header>
+
+<main class="wrap">
+  {% block content %}{% endblock %}
+</main>
+
+<footer class="wrap muted">
+  <p>© MaterialMind</p>
+</footer>
+</body>
+</html>

templates/index.html

@@ -0,0 +1,129 @@
+{% extends "base.html" %}
+{% block content %}
+
+<!-- Background tiles -->
+<div class="bg-gallery">
+  <div class="bg-tile"
+       style="--bg: url('{{ url_for('static', filename='img/11.png') }}');"
+       aria-label="MaterialMind in action"></div>
+  <div class="bg-tile"
+       style="--bg: url('{{ url_for('static', filename='img/22.jpg') }}');"
+       aria-label="Industrial materials context"></div>
+</div>
+
+<h2>Find and rank materials</h2>
+
+<form action="{{ url_for('recommend') }}" method="post" class="grid" id="mmForm">
+  <label>Application
+    <input name="environment" placeholder="seawater / sour service / high-T oxidation"
+           value="">
+  </label>
+  <label>Temperature
+    <input name="temperature" placeholder="e.g., 20–25 °C" value="">
+  </label>
+  <label>Min UTS (MPa)
+    <input name="min_uts" type="number" min="0" step="10" placeholder="e.g., 900" value="">
+  </label>
+  <label>Max density (g/cm³)
+    <input name="max_density" type="number" min="0" step="0.1" placeholder="e.g., 8.2" value="">
+  </label>
+  <label>Budget
+    <input name="budget" placeholder="open / low / medium / high" value="">
+  </label>
+  <label>Process
+    <input name="process" placeholder="wrought / casting / AM / any" value="">
+  </label>
+
+  <!-- PRIORITIES ONLY (no numbers displayed) -->
+  <fieldset class="prefs" id="prefsBox">
+    <legend>Priorities</legend>
+
+    <div class="pref-row">
+      <label>Performance</label>
+      <select class="prio" data-target="w_perf" name="p_perf">
+        <option value="very_high">Very high</option>
+        <option value="high" selected>High</option>
+        <option value="medium">Medium</option>
+        <option value="low">Low</option>
+        <option value="very_low">Very low</option>
+      </select>
+      <input type="hidden" name="w_perf" value="75">
+    </div>
+
+    <div class="pref-row">
+      <label>Stability</label>
+      <select class="prio" data-target="w_stab" name="p_stab">
+        <option value="very_high" selected>Very high</option>
+        <option value="high">High</option>
+        <option value="medium">Medium</option>
+        <option value="low">Low</option>
+        <option value="very_low">Very low</option>
+      </select>
+      <input type="hidden" name="w_stab" value="100">
+    </div>
+
+    <div class="pref-row">
+      <label>Cost</label>
+      <select class="prio" data-target="w_cost" name="p_cost">
+        <option value="not_important">Not important</option>
+        <option value="high" selected>High</option>
+        <option value="medium">Medium</option>
+        <option value="low">Low</option>
+        <option value="very_low">Very low</option>
+      </select>
+      <input type="hidden" name="w_cost" value="75">
+    </div>
+
+    <div class="pref-row">
+      <label>Availability</label>
+      <select class="prio" data-target="w_avail" name="p_avail">
+        <option value="very_high">Very high</option>
+        <option value="high" selected>High</option>
+        <option value="medium">Medium</option>
+        <option value="low">Low</option>
+        <option value="very_low">Very low</option>
+      </select>
+      <input type="hidden" name="w_avail" value="75">
+    </div>
+  </fieldset>
+
+  <label>Top-k context
+    <input name="k" type="number" min="3" max="10" value="{{ default_k }}">
+  </label>
+
+  <div class="actions">
+    <button class="btn" id="submitBtn" type="submit">Get ranked shortlist</button>
+  </div>
+</form>
+
+{% with messages = get_flashed_messages(with_categories=true) %}
+  {% if messages %}
+    <div class="flash">
+      {% for category, msg in messages %}
+        <p class="{{ category }}">{{ msg }}</p>
+      {% endfor %}
+    </div>
+  {% endif %}
+{% endwith %}
+
+<script>
+(function () {
+  // Priority -> hidden numeric weight (0..100), independent
+  const mapStandard = { very_high: 100, high: 75, medium: 50, low: 25, very_low: 0 };
+  const mapCost     = { not_important: 0, high: 75, medium: 50, low: 25, very_low: 10 };
+
+  function applyMapping(select){
+    const target = select.dataset.target;
+    const hidden = document.querySelector(`input[name="${target}"]`);
+    const isCost = (target === "w_cost");
+    const m = isCost ? mapCost : mapStandard;
+    hidden.value = m[select.value] ?? 50;
+  }
+
+  document.querySelectorAll('select.prio').forEach(sel => {
+    sel.addEventListener('change', () => applyMapping(sel));
+    applyMapping(sel);
+  });
+})();
+</script>
+{% endblock %}

templates/results.html

@@ -0,0 +1,80 @@
+{% extends "base.html" %}
+{% block content %}
+
+<a class="btn ghost" href="{{ url_for('index') }}">← New query</a>
+<h2>Shortlist ({{ environment }}, {{ temperature }})</h2>
+
+{% if candidates and candidates|length > 0 %}
+  <table class="tbl">
+    <thead>
+      <tr>
+        <th>Rank</th>
+        <th>Material</th>
+        <th>Score</th>
+        <th>Key reasons</th>
+      </tr>
+    </thead>
+    <tbody>
+    {% for c in candidates %}
+      <tr>
+        <td>{{ loop.index }}</td>
+        <td>{{ c.name }}</td>
+        <td>
+          {% if c.score_pct is defined and c.score_pct is not none %}
+            {{ "%.1f"|format(c.score_pct) }}%
+          {% else %}
+            —
+          {% endif %}
+        </td>
+        <td>
+          <ul>
+            {% for r in (c.reasons or [])[:3] %}
+              <li>{{ r }}</li>
+            {% endfor %}
+          </ul>
+        </td>
+      </tr>
+    {% endfor %}
+    </tbody>
+  </table>
+
+  <h3>Material cards</h3>
+  <div class="cards">
+  {% for c in candidates %}
+    <section class="card">
+      <h4>{{ loop.index }}. {{ c.name }}</h4>
+
+      {% if c.score_pct is defined and c.score_pct is not none %}
+        <p><strong>Score:</strong> {{ "%.1f"|format(c.score_pct) }}%</p>
+      {% endif %}
+
+      {% if c.tradeoffs %}
+        <p><strong>Trade-offs</strong></p>
+        <ul>
+          {% for t in c.tradeoffs %}
+            <li>{{ t }}</li>
+          {% endfor %}
+        </ul>
+      {% endif %}
+
+      {% if c.citations %}
+        <p class="muted"><strong>Citations:</strong> {{ c.citations|join(", ") }}</p>
+      {% endif %}
+    </section>
+  {% endfor %}
+  </div>
+{% else %}
+  <p class="muted">No structured shortlist returned. Raw model output is below.</p>
+{% endif %}
+
+<h3>Citations (source mapping)</h3>
+<pre class="mono">{{ "\n".join(citations) }}</pre>
+
+{% if raw_output %}
+  <details style="margin-top:12px;">
+    <summary>Raw model output</summary>
+    <pre class="mono">{{ raw_output }}</pre>
+  </details>
+{% endif %}
+
+{% endblock %}