fix

src/helper_files/card_encodings_fin_llama_3_8b_embedding_normalized.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd419abd0dd8ff7620ff8c71e3623722e772e6b4d245634d2e8e80907c172145
+size 279375708

src/helper_files/supported_sets.txt

@@ -0,0 +1,25 @@
+DFT
+PIO
+FDN
+DSK
+BLB
+MH3
+OTJ
+M19
+SIR
+VOW
+ONE
+LCI
+MOM
+MID
+MKM
+LTR
+DMU
+WOE
+KTK
+BRO
+AFR
+NEO
+SNC
+HBG
+STX

src/streamlit_app.py

@@ -1,115 +1,150 @@
+"""
+MTG Draft Assistant Streamlit App
+---------------------------------
+A booster-draft helper written with Streamlit and deployable on Hugging Face Spaces.
+
+Changes in this revision
+~~~~~~~~~~~~~~~~~~~~~~~~
+* **supported_sets.txt** – one set code per line.
+* Sidebar now shows a **single-choice list** (radio buttons) sourced from that
+  file instead of a free-text box, so users can only draft sets you actually
+  support.
+* Fallback to the old text input if the text-file is missing or empty.
+
+Replace the three stub functions (`load_model`, `suggest_pick`,
+`generate_booster`) with your real model-/API-calls when you are ready.
+"""
+
+from __future__ import annotations
+
 import os
 import random
-from typing import List, Dict
+from pathlib import Path
+from typing import Dict, List
 
+import requests
 import streamlit as st
 
 # -----------------------------------------------------------------------------
-# 1. Model loading
+# 0. Constants & helpers
 # -----------------------------------------------------------------------------
 
-@st.cache_resource(show_spinner="Loading draft model…")
-def load_model():
-    """Load and return your trained drafting model.
+SUPPORTED_SETS_PATH = Path("supported_sets.txt")
 
-    Adapt this stub to your own ML pipeline. For example, if you have pushed a
-    `draft_model.joblib` artifact to the Hub as `username/mtg-draft-model`, you
-    could:
+@st.cache_data(show_spinner="Reading supported sets …")
+def get_supported_sets(path: Path = SUPPORTED_SETS_PATH) -> List[str]:
+    """Return a list of legal set codes read from *supported_sets.txt*.
 
-        from huggingface_hub import hf_hub_download
-        import joblib, os
+    The file should contain **one set tag per line**, e.g.::
 
-        local = hf_hub_download(
-            repo_id="username/mtg-draft-model",
-            filename="draft_model.joblib",
-            token=os.getenv("HF_TOKEN"),  # optional for private repos
-        )
-        return joblib.load(local)
+        WOE
+        LCI
+        MKM
+
+    If the file is missing we fall back to an empty list so the UI
+    degrades gracefully.
+    """
+    if path.is_file():
+        return [ln.strip() for ln in path.read_text().splitlines() if ln.strip()]
+    return []
+
+# -----------------------------------------------------------------------------
+# 1. Model loading (stub)
+# -----------------------------------------------------------------------------
+
+@st.cache_resource(show_spinner="Loading draft model …")
+def load_model():
+    """Load and return the trained drafting model.
+
+    Adapt to your own pipeline; see previous revision for an example that pulls
+    an artefact from *huggingface_hub*.  Returning *None* leaves us in demo
+    mode and will pick random cards.
     """
-    return None  # fallback: no-op model for demo purposes
+    return None
 
 model = load_model()
 
 # -----------------------------------------------------------------------------
-# 2. Helper functions
+# 2. Draft-logic helpers (stubs)
 # -----------------------------------------------------------------------------
 
 def suggest_pick(pack: List[Dict], picks: List[Dict]) -> Dict:
-    """Return the card dict the model recommends from the current pack."""
+    """Return the card the model recommends from *pack*."""
     if model is None:
         return random.choice(pack)
-    # 🔄 Replace with your model's inference API
-    return model.predict(pack=pack, picks=picks)
+    return model.predict(pack=pack, picks=picks)  # type: ignore[attr-defined]
 
 
 def fetch_card_image(card_name: str) -> str:
-    """Return a medium‑sized card image URL from the Scryfall API."""
-    import requests
-
-    response = requests.get(
-        "https://api.scryfall.com/cards/named",
-        params={"exact": card_name, "format": "json"},
+    """Fetch card art URL from Scryfall (normal size)."""
+    r = requests.get(
+        "https://api.scryfall.com/cards/named", params={"exact": card_name, "format": "json"}
     )
-    response.raise_for_status()
-    data = response.json()
-    # multi‑faced cards may not have `image_uris`; fall back to the first face
+    r.raise_for_status()
+    data = r.json()
     if "image_uris" in data:
         return data["image_uris"]["normal"]
     return data["card_faces"][0]["image_uris"]["normal"]
 
 
 def generate_booster(set_code: str) -> List[Dict]:
-    """Very small demo booster generator using Scryfall search."""
-    import requests
-
-    url = (
-        f"https://api.scryfall.com/cards/search?q=set%3A{set_code}+is%3Abooster+unique%3Aprints"
-    )
+    """Return a pseudo-random 15-card booster using Scryfall search."""
+    url = f"https://api.scryfall.com/cards/search?q=set%3A{set_code}+is%3Abooster+unique%3Aprints"
     cards: List[Dict] = []
     while url:
-        r = requests.get(url)
-        r.raise_for_status()
-        payload = r.json()
-        cards.extend(payload["data"])
+        resp = requests.get(url)
+        resp.raise_for_status()
+        payload = resp.json()
+        cards += payload["data"]
         url = payload.get("next_page") if payload.get("has_more") else None
     return random.sample(cards, 15)
 
-
 # -----------------------------------------------------------------------------
 # 3. Streamlit UI
 # -----------------------------------------------------------------------------
 
-st.set_page_config(
-    page_title="MTG Draft Assistant",
-    page_icon="🃏",
-)
+st.set_page_config(page_title="MTG Draft Assistant", page_icon="🃏")
 
 st.title("🃏 MTG Draft Assistant")
-st.caption("A minimal booster‑draft helper that runs entirely client‑side.")
+
+# ---------------- Sidebar -----------------------------------------------------
 
 with st.sidebar:
     st.header("Draft setup")
-    set_code = st.text_input("Set code (e.g. WOE, LCI, MKM)", value="WOE")
-    if st.button("Start new draft"):
+
+    supported_sets = get_supported_sets()
+
+    if supported_sets:
+        set_code = st.radio("Choose a set to draft", supported_sets, index=0)
+    else:
+        st.warning(
+            "*supported_sets.txt* not found or empty. Using free-text input instead.",
+            icon="⚠️",
+        )
+        set_code = st.text_input("Set code", value="WOE")
+
+    if st.button("Start new draft", type="primary"):
         st.session_state["pack"] = generate_booster(set_code)
         st.session_state["picks"] = []
 
-# Create session variables on first run
+# ---------------- Session state guards ---------------------------------------
+
 st.session_state.setdefault("pack", [])
 st.session_state.setdefault("picks", [])
 
 if not st.session_state["pack"]:
-    st.info("Choose **Start new draft** in the sidebar to open pack 1.")
+    st.info("Choose **Start new draft** in the sidebar to open pack 1.")
     st.stop()
 
-pack = st.session_state["pack"]
-picks = st.session_state["picks"]
+pack: List[Dict] = st.session_state["pack"]
+picks: List[Dict] = st.session_state["picks"]
 
-st.subheader(f"Pack {len(picks)//15 + 1} — Pick {len(picks) % 15 + 1}")
+st.subheader(f"Pack {len(picks) // 15 + 1} — Pick {len(picks) % 15 + 1}")
 suggested = suggest_pick(pack, picks)
+
 st.success(f"**Model suggests:** {suggested['name']}")
 
-# Display pack as a grid of card images
+# Display current pack as a 5-column grid of card images
 cols = st.columns(5)
 for idx, card in enumerate(pack):
     col = cols[idx % 5]
@@ -123,4 +158,3 @@ for idx, card in enumerate(pack):
 
 with st.expander("Current picks", expanded=False):
     st.write("\n".join([c["name"] for c in picks]))
-