Skeleton

requirements.txt

@@ -1,3 +1,5 @@
-altair
-pandas
-streamlit
+streamlit>=1.28
+requests
+huggingface_hub>=0.23      # if you pull the model from the Hub
+joblib                      # if you pickle weights
+scrython    

src/streamlit_app.py

@@ -1,40 +1,136 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+ """
+ MTG Draft Assistant Streamlit App
+ ---------------------------------
+ A simple demo streamlit app that wraps a Magic: The Gathering drafting helper model
+ and can be deployed on Hugging Face Spaces (Streamlit SDK).
+ 
+ Replace the stub functions with your real model logic and data sources. See
+ the accompanying README for build instructions.
+ """
+ 
+ import os
+ import random
+ from typing import List, Dict
+ 
+ import streamlit as st
+ 
+ # -----------------------------------------------------------------------------
+ # 1. Model loading
+ # -----------------------------------------------------------------------------
+ 
+ @st.cache_resource(show_spinner="Loading draft model…")
+ def load_model():
+     """Load and return your trained drafting model.
+ 
+     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:
+ 
+         from huggingface_hub import hf_hub_download
+         import joblib, os
+ 
+         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)
+     """
+     return None  # fallback: no-op model for demo purposes
+ 
+ model = load_model()
+ 
+ # -----------------------------------------------------------------------------
+ # 2. Helper functions
+ # -----------------------------------------------------------------------------
+ 
+ def suggest_pick(pack: List[Dict], picks: List[Dict]) -> Dict:
+     """Return the card dict the model recommends from the current pack."""
+     if model is None:
+         return random.choice(pack)
+     # 🔄 Replace with your model's inference API
+     return model.predict(pack=pack, picks=picks)
+ 
+ 
+ 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"},
+     )
+     response.raise_for_status()
+     data = response.json()
+     # multi‑faced cards may not have `image_uris`; fall back to the first face
+     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"
+     )
+     cards: List[Dict] = []
+     while url:
+         r = requests.get(url)
+         r.raise_for_status()
+         payload = r.json()
+         cards.extend(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.title("🃏 MTG Draft Assistant")
+ st.caption("A minimal booster‑draft helper that runs entirely client‑side.")
+ 
+ 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"):
+         st.session_state["pack"] = generate_booster(set_code)
+         st.session_state["picks"] = []
+ 
+ # Create session variables on first run
+ 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.stop()
+ 
+ pack = st.session_state["pack"]
+ picks = st.session_state["picks"]
+ 
+ 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
+ cols = st.columns(5)
+ for idx, card in enumerate(pack):
+     col = cols[idx % 5]
+     col.image(fetch_card_image(card["name"]), use_column_width=True)
+     if col.button(f"Pick {card['name']}", key=f"pick-{idx}"):
+         picks.append(card)
+         pack.remove(card)
+         if not pack:  # end of pack ⇒ open a fresh booster
+             st.session_state["pack"] = generate_booster(set_code)
+         st.experimental_rerun()
+ 
+ with st.expander("Current picks", expanded=False):
+     st.write("\n".join([c["name"] for c in picks]))
+