Update app.py

app.py

@@ -13,28 +13,6 @@ load_dotenv()
 
 app = FastAPI(title="Sports Predictor", description="AI-powered sports predictions for soccer and NBA")
 
-sentiment_model = pipeline("zero-shot-classification", model="valhalla/distilbart-mnli-12-1")
-reasoning_model = pipeline("text2text-generation", model="google/flan-t5-base")
-similarity_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
-
-FOOTBALL_API_KEY = os.getenv("FOOTBALL_API_KEY", "YOUR_FOOTBALL_DATA_API_KEY")
-FOOTBALL_ENDPOINT = "https://api.football-data.org/v4/matches"
-NBA_API_KEY = os.getenv("NBA_API_KEY", "your-api-key")
-nba_api = BalldontlieAPI(api_key=NBA_API_KEY)
-
-SOCCER_LEAGUES = {
-    "EPL": 2021,
-    "LaLiga": 2014,
-    "Bundesliga": 2002
-}
-
-def get_team_news(team: str, sport: str = "football"):
-    url = f"https://news.google.com/search?q={team}+{sport}&hl=en"
-    r = requests.get(url, headers={"User-Agent": "Mozilla/5.0"})
-    soup = BeautifulSoup(r.text, "html.parser")
-    headlines = [h.text for h in soup.select("h3")[:5]]
-    return " ".join(headlines)
-
 @app.get("/")
 def root():
     return {
@@ -68,18 +46,67 @@ def root():
         }
     }
 
+sentiment_model = pipeline("zero-shot-classification", model="valhalla/distilbart-mnli-12-1")
+reasoning_model = pipeline("text2text-generation", model="google/flan-t5-base")
+similarity_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+FOOTBALL_API_KEY = os.getenv("FOOTBALL_API_KEY", "FOOTBALL_API_KEY")
+FOOTBALL_ENDPOINT = "https://api.football-data.org/v4/matches"
+NBA_API_KEY = os.getenv("NBA_API_KEY", "NBA_API_KEY")
+nba_api = BalldontlieAPI(api_key=NBA_API_KEY)
+
+SOCCER_LEAGUES = {
+    "EPL": 2021,
+    "LaLiga": 2014,
+    "Bundesliga": 2002
+}
+
+def get_team_news(team: str, sport: str = "football"):
+    url = f"https://news.google.com/search?q={team}+{sport}&hl=en"
+    r = requests.get(url, headers={"User-Agent": "Mozilla/5.0"})
+    soup = BeautifulSoup(r.text, "html.parser")
+    headlines = [h.text for h in soup.select("h3")[:5]]
+    return " ".join(headlines)
+
 @app.get("/soccer-predictions")
 def soccer_predictions():
-    today = date.today().isoformat()
+    from datetime import timedelta
+    
+    
+    today = date.today()
+    date_from = (today - timedelta(days=5)).isoformat()
+    date_to = (today + timedelta(days=5)).isoformat()
+    
     predictions = []
+    debug_info = []
 
     for league, code in SOCCER_LEAGUES.items():
         headers = {"X-Auth-Token": FOOTBALL_API_KEY}
-        resp = requests.get(
-            f"{FOOTBALL_ENDPOINT}?competitions={code}&dateFrom={today}&dateTo={today}",
-            headers=headers
-        )
-        matches = resp.json().get("matches", [])
+        try:
+            resp = requests.get(
+                f"{FOOTBALL_ENDPOINT}?competitions={code}&dateFrom={date_from}&dateTo={date_to}",
+                headers=headers
+            )
+            resp.raise_for_status()
+            data = resp.json()
+            matches = data.get("matches", [])
+            
+            debug_info.append({
+                "league": league,
+                "code": code,
+                "matches_found": len(matches),
+                "api_response_status": resp.status_code,
+                "date_range": f"{date_from} to {date_to}"
+            })
+            
+        except requests.exceptions.RequestException as e:
+            debug_info.append({
+                "league": league,
+                "code": code,
+                "error": str(e),
+                "api_response_status": getattr(resp, 'status_code', 'No response')
+            })
+            continue
 
         for match in matches:
             home, away = match["homeTeam"]["name"], match["awayTeam"]["name"]
@@ -116,13 +143,100 @@ def soccer_predictions():
                 }
             })
 
-    return {"date": today, "predictions": predictions}
+    return {
+        "date": today, 
+        "predictions": predictions,
+        "debug_info": debug_info,
+        "total_predictions": len(predictions)
+    }
+
+@app.get("/test-api-keys")
+def test_api_keys():
+    """Test endpoint to check if API keys are working"""
+    results = {}
+    
+    
+    if FOOTBALL_API_KEY != "FOOTBALL_API_KEY":
+        try:
+            headers = {"X-Auth-Token": FOOTBALL_API_KEY}
+            resp = requests.get(f"{FOOTBALL_ENDPOINT}?competitions=2021&dateFrom=2025-01-01&dateTo=2025-01-31", headers=headers)
+            results["football_api"] = {
+                "status": "working" if resp.status_code == 200 else "error",
+                "status_code": resp.status_code,
+                "response_keys": list(resp.json().keys()) if resp.status_code == 200 else None
+            }
+        except Exception as e:
+            results["football_api"] = {"status": "error", "error": str(e)}
+    else:
+        results["football_api"] = {"status": "not_configured", "message": "API key not set"}
+    
+    # Test NBA API
+    try:
+        games = nba_api.nba.games.list(dates=["2025-01-15"])
+        results["nba_api"] = {
+            "status": "working",
+            "games_found": len(games) if games else 0
+        }
+    except Exception as e:
+        results["nba_api"] = {"status": "error", "error": str(e)}
+    
+    return results
 
 @app.get("/nba-predictions")
 def nba_predictions():
-    today = date.today().isoformat()
-    games = nba_api.nba.games.list(dates=[today])
+    from datetime import timedelta
+    
+    # Use a 2-day forward range for NBA
+    today = date.today()
+    date_from = today.isoformat()
+    date_to = (today + timedelta(days=2)).isoformat()
+    
+    # Generate list of dates for the next 2 days (including today)
+    dates = [(today + timedelta(days=i)).isoformat() for i in range(3)]
+    
     predictions = []
+    debug_info = []
+    
+    try:
+        # Test NBA API connectivity first
+        games_response = nba_api.nba.games.list(dates=dates)
+        
+        # Handle the PaginatedListResponse object properly
+        if hasattr(games_response, 'data'):
+            games = games_response.data
+            debug_info.append({
+                "api_status": "working",
+                "games_found": len(games),
+                "date_range": f"{date_from} to {date_to}",
+                "dates_queried": dates
+            })
+        else:
+            debug_info.append({
+                "api_status": "error",
+                "error": "Unexpected response format",
+                "response_type": str(type(games_response))
+            })
+            return {
+                "date_range": f"{date_from} to {date_to}",
+                "predictions": [],
+                "debug_info": debug_info,
+                "total_predictions": 0,
+                "error": "NBA API returned unexpected response format"
+            }
+            
+    except Exception as e:
+        debug_info.append({
+            "api_status": "error",
+            "error": str(e),
+            "date_range": f"{date_from} to {date_to}"
+        })
+        return {
+            "date_range": f"{date_from} to {date_to}",
+            "predictions": [],
+            "debug_info": debug_info,
+            "total_predictions": 0,
+            "error": f"NBA API error: {str(e)}"
+        }
 
     for g in games:
         home, away = g["home_team"]["full_name"], g["visitor_team"]["full_name"]
@@ -153,7 +267,12 @@ def nba_predictions():
             }
         })
 
-    return {"date": today, "predictions": predictions}
+    return {
+        "date_range": f"{date_from} to {date_to}",
+        "predictions": predictions,
+        "debug_info": debug_info,
+        "total_predictions": len(predictions)
+    }
 
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=7860)