Create app.py

app.py

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+# app.py — İki takım adıyla tek maç analizi (API-Sports)
+import os, requests, math, numpy as np, pandas as pd, streamlit as st
+
+st.set_page_config(page_title="⚽ İki Takım Analizi", layout="centered")
+API_BASE = "https://v3.football.api-sports.io"
+
+# ---- API KEY ----
+prefill = os.environ.get("API_FOOTBALL_KEY", "")
+try:
+    prefill = prefill or st.secrets.get("API_FOOTBALL_KEY", "")
+except Exception:
+    pass
+API_KEY = st.text_input("API-Sports anahtarın", value=prefill, type="password")
+HEADERS = {"x-apisports-key": API_KEY} if API_KEY else {}
+
+# ---------- Yardımcılar ----------
+def get(url, params=None):
+    return requests.get(f"{API_BASE}{url}", headers=HEADERS, params=params or {}, timeout=40)
+
+def search_team(name: str):
+    """En iyi eşleşen takımı döndür (id, ad, ülke)."""
+    r = get("/teams", {"search": name})
+    if r.status_code != 200: return None
+    resp = r.json().get("response", [])
+    if not resp: return None
+    t = resp[0]
+    return {"id": t["team"]["id"], "name": t["team"]["name"], "country": t["team"]["country"]}
+
+def lastN_profile(team_id: int, n: int = 20):
+    """FREE planda çalışır: lig/sezon filtresi olmadan son N maçtan ortalama üretir."""
+    r = get("/fixtures", {"team": team_id, "last": n})
+    if r.status_code != 200: return None
+    fx = r.json().get("response", [])
+    if not fx: return None
+
+    def agg(as_home=None):
+        gs=ga=cnt=0
+        for f in fx:
+            th, ta = f["teams"]["home"]["id"], f["teams"]["away"]["id"]
+            gh, ga_ = (f["goals"]["home"] or 0), (f["goals"]["away"] or 0)
+            if as_home is True and th != team_id:  continue
+            if as_home is False and ta != team_id: continue
+            if th == team_id: gs += gh; ga += ga_
+            else:             gs += ga_; ga += gh
+            cnt += 1
+        if cnt==0: return 0.0, 0.0
+        return gs/cnt, ga/cnt
+
+    hs,hc = agg(True)   # ev oynarken
+    as_,ac = agg(False) # deplasmanda
+    overall = (hs+as_)/2 if (hs and as_) else max(hs,as_,0.0)
+
+    return {"home_scored":hs, "home_conceded":hc, "away_scored":as_, "away_conceded":ac, "overall":overall}
+
+def next_h2h(home_id: int, away_id: int):
+    """İki takımın sıradaki resmî maçını bul (varsa)."""
+    r = get("/fixtures/headtohead", {"h2h": f"{home_id}-{away_id}", "next": 1})
+    if r.status_code != 200: return None
+    resp = r.json().get("response", [])
+    return resp[0] if resp else None
+
+def poisson_pmf(k, lam): 
+    return math.exp(-lam) * (lam**k) / math.factorial(k) if lam>0 else (1.0 if k==0 else 0.0)
+
+def score_matrix(lh, la, max_g=6):
+    h = np.array([poisson_pmf(i, lh) for i in range(max_g+1)])
+    a = np.array([poisson_pmf(j, la) for j in range(max_g+1)])
+    M = np.outer(h, a); M /= M.sum(); return M
+
+def probs_1x2(M):
+    p1 = np.tril(M,-1).sum(); px = np.trace(M); p2 = np.triu(M,1).sum()
+    return float(p1), float(px), float(p2)
+
+def ou_prob(M, line=2.5):
+    n=M.shape[0]; return float(sum(M[i,j] for i in range(n) for j in range(n) if i+j>line))
+
+def btts_prob(M):
+    n=M.shape[0]; return float(sum(M[i,j] for i in range(n) for j in range(n) if i>=1 and j>=1))
+
+def estimate_lambdas(home_prof, away_prof):
+    """Ev λ ve Dep λ: ev hücum + rakibin deplasman savunması harmanı (free-friendly)."""
+    if not home_prof or not away_prof:
+        return 1.5, 1.2, "fallback"
+
+    lam_h = 0.6*max(home_prof["home_scored"],0.01) + 0.4*max(away_prof["away_conceded"],0.01)
+    lam_a = 0.6*max(away_prof["away_scored"],0.01) + 0.4*max(home_prof["home_conceded"],0.01)
+    # genel ofans ile yumuşat
+    lam_h = 0.7*lam_h + 0.3*home_prof["overall"]
+    lam_a = 0.7*lam_a + 0.3*away_prof["overall"]
+    lam_h = float(np.clip(lam_h, 0.2, 3.8))
+    lam_a = float(np.clip(lam_a, 0.2, 3.8))
+    return lam_h, lam_a, "lastN"
+
+# ---------- UI ----------
+st.title("⚽ İki Takım — Maç Olasılık Analizi")
+
+c1, c2 = st.columns(2)
+with c1: team_home_name = st.text_input("Ev sahibi (ör. Fenerbahçe)", "")
+with c2: team_away_name = st.text_input("Deplasman (ör. Galatasaray)", "")
+
+if st.button("🔎 Analiz Et"):
+    if not API_KEY:
+        st.error("Önce API anahtarını yazmalısın."); st.stop()
+
+    # 1) Takımları bul
+    th = search_team(team_home_name.strip())
+    ta = search_team(team_away_name.strip())
+    if not th or not ta:
+        st.error("Takım(lar) bulunamadı. İsimleri biraz daha net yazmayı dene.")
+        st.stop()
+
+    st.write(f"**Eşleşen takımlar:** {th['name']} ({th['country']}) vs {ta['name']} ({ta['country']})")
+
+    # 2) Sıradaki resmi maç (varsa)
+    fx = next_h2h(th["id"], ta["id"])
+    if fx:
+        dt_str = fx["fixture"]["date"].replace("T"," ")[:16]
+        st.caption(f"Bulunan resmi maç: **{dt_str}** – Lig: {fx['league']['country']} / {fx['league']['name']}")
+
+    # 3) Profiller (son 20 – free uyumlu)
+    ph = lastN_profile(th["id"], 20)
+    pa = lastN_profile(ta["id"], 20)
+
+    lam_h, lam_a, src = estimate_lambdas(ph, pa)
+    M = score_matrix(lam_h, lam_a)
+    p1, px, p2 = probs_1x2(M)
+    p_ou25 = ou_prob(M, 2.5)
+    p_btts = btts_prob(M)
+
+    # 4) Özet çıktı
+    st.subheader("📈 Olasılıklar")
+    res = pd.DataFrame({
+        "Metrix": ["Ev kazanır", "Beraberlik", "Dep kazanır", "Üst 2.5", "KG Var"],
+        "Olasılık %": [round(p1*100,1), round(px*100,1), round(p2*100,1),
+                       round(p_ou25*100,1), round(p_btts*100,1)]
+    })
+    st.table(res)
+
+    st.caption(f"λ Ev = {lam_h:.2f}, λ Dep = {lam_a:.2f}  •  Kaynak: {src} (son 20 maç)")
+
+    # 5) Skor olasılıkları (en olası 5 skor)
+    scores = []
+    for i in range(0,6):
+        for j in range(0,6):
+            prob = M[i,j]
+            scores.append((f"{i}-{j}", prob))
+    top = sorted(scores, key=lambda x: x[1], reverse=True)[:5]
+    df_top = pd.DataFrame({"Skor": [t[0] for t in top],
+                           "Olasılık %": [round(t[1]*100,2) for t in top]})
+    st.subheader("🔝 En olası skorlar")
+    st.table(df_top)