backend commit

app/main.py

@@ -4,7 +4,6 @@ import pandas as pd
 import uvicorn
 import plotly.graph_objects as go
 import logging
-import joblib
 import numpy as np
 import os
 import json
@@ -76,23 +75,8 @@ goals_df['y_coord'] = np.random.uniform(20, 80, len(goals_df)).round()
 teams = set(matches_df['home_team'].unique()).union(set(matches_df['away_team'].unique()))
 players = sorted([str(scorer) for scorer in goals_df['scorer'].dropna().unique() if pd.notna(scorer)])
 
-# Load models with detailed logging
-try:
-    logger.info("Loading logistic_regression_model.pkl")
-    logistic_model = joblib.load('model/logistic_regression_model.pkl')
-    logger.info("Loading linear_regression_team1_goals.pkl")
-    linear_model_team1 = joblib.load('model/linear_regression_team1_goals.pkl')
-    logger.info("Loading linear_regression_team2_goals.pkl")
-    linear_model_team2 = joblib.load('model/linear_regression_team2_goals.pkl')
-    logger.info("Loading label_encoder.pkl")
-    le = joblib.load('model/label_encoder.pkl')
-    logger.info("Models loaded successfully.")
-except FileNotFoundError as e:
-    logger.error(f"Model file not found: {e}")
-    raise HTTPException(status_code=500, detail="Trained model files not found.")
-except Exception as e:
-    logger.error(f"Error loading models: {str(e)}")
-    raise HTTPException(status_code=500, detail=f"Model loading failed: {str(e)}")
+# Skip model loading for now
+logger.warning("Model loading skipped due to compatibility issues. Prediction endpoint disabled.")
 
 def summarize_with_groq(text):
     try:
@@ -179,7 +163,7 @@ def get_head_to_head_stats(team1, team2, num_matches=5):
     
     if matches.empty:
         return {"total_matches": 0, f"{team1}_wins": 0, f"{team2}_wins": 0, "draws": 0,
-                f"{team1}_goals": 0, f"{team2}_goals": 0, "goal_difference": "Even",
+                f"{team1}_goals": 0, f"{team2}_wins": 0, "goal_difference": "Even",
                 "last_matches": [], "chart": None}
     
     total_matches = len(matches)
@@ -238,51 +222,13 @@ def get_player_stats(player_name):
     return {"player_name": player_name, "country": player_team, "total_goals": total_goals}
 
 def predict_match_outcome(team1, team2):
-    try:
-        teams_sorted = sorted([team1, team2])
-        team1_encoded = le.transform([teams_sorted[0]])[0] if teams_sorted[0] in le.classes_ else -1
-        team2_encoded = le.transform([teams_sorted[1]])[0] if teams_sorted[1] in le.classes_ else -1
-        
-        if team1_encoded == -1 or team2_encoded == -1:
-            raise ValueError("One or both teams not found in training data")
-        
-        X_pred = [[team1_encoded, team2_encoded]]
-        
-        probs = logistic_model.predict_proba(X_pred)[0]
-        
-        if team1 < team2:
-            outcome_probs = {
-                "team1_win": round(probs[0] * 100, 2),
-                "team2_win": round(probs[2] * 100, 2),
-                "draw": round(probs[1] * 100, 2)
-            }
-        else:
-            outcome_probs = {
-                "team1_win": round(probs[2] * 100, 2),
-                "team2_win": round(probs[0] * 100, 2),
-                "draw": round(probs[1] * 100, 2)
-            }
-        
-        if outcome_probs["team1_win"] > outcome_probs["team2_win"] and outcome_probs["team1_win"] >= outcome_probs["draw"]:
-            goals_pred = {"team1_goals": 2, "team2_goals": 1}
-        elif outcome_probs["team2_win"] > outcome_probs["team1_win"] and outcome_probs["team2_win"] >= outcome_probs["draw"]:
-            goals_pred = {"team1_goals": 1, "team2_goals": 2}
-        else:
-            goals_pred = {"team1_goals": 1, "team2_goals": 1}
-        
-        return {
-            "outcome_probabilities": outcome_probs,
-            "predicted_goals": goals_pred
-        }
-    except Exception as e:
-        logger.error(f"Prediction error for {team1} vs {team2}: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
+    raise HTTPException(status_code=503, detail="Prediction functionality is temporarily disabled due to model loading issues.")
 
 @app.get("/")
 async def home():
     return {
         "message": "Welcome to Football Prediction API",
-        "description": "This API provides football statistics, match predictions, and data visualizations",
+        "description": "This API provides football statistics, match predictions, and data visualizations. Note: Prediction endpoint is currently disabled.",
         "available_endpoints": {
             "/teams": "List all teams",
             "/players": "List all players",
@@ -290,7 +236,7 @@ async def home():
             "/team/{team_name}": "Get team statistics",
             "/head-to-head/{team1}/{team2}": "Get head-to-head statistics",
             "/player/{player_name}": "Get player statistics",
-            "/predict/{team1}/{team2}": "Predict match outcome",
+            "/predict/{team1}/{team2}": "Predict match outcome (currently disabled)",
             "/goal-spatial-heatmap/{team}": "Get goal distribution heatmap"
         }
     }
@@ -363,17 +309,7 @@ async def get_player_statistics(player_name: str, summarize: bool = False):
 
 @app.get("/predict/{team1}/{team2}")
 async def predict_match(team1: str, team2: str, summarize: bool = False):
-    if team1 not in teams or team2 not in teams:
-        raise HTTPException(status_code=404, detail="One or both teams not found")
-    predictions = predict_match_outcome(team1, team2)
-    response = {"team1": team1, "team2": team2, "predictions": predictions}
-    if summarize:
-        text = (f"Outcome Probabilities: {team1} Win: {predictions['outcome_probabilities']['team1_win']}%, "
-                f"{team2} Win: {predictions['outcome_probabilities']['team2_win']}%, Draw: {predictions['outcome_probabilities']['draw']}%\n"
-                f"Predicted Goals: {team1}: {predictions['predicted_goals']['team1_goals']}, {team2}: {predictions['predicted_goals']['team2_goals']}")
-        summary = summarize_with_groq(text)
-        response["summary"] = summary
-    return response
+    raise HTTPException(status_code=503, detail="Prediction functionality is temporarily disabled due to model loading issues.")
 
 @app.get("/goal-spatial-heatmap/{team}")
 async def get_goal_spatial_heatmap(team: str, start_year: int = 2000, end_year: int = 2023, summarize: bool = False):

requirements.txt

@@ -3,8 +3,8 @@ uvicorn==0.23.2
 pandas==2.0.3
 plotly==5.15.0
 joblib==1.3.2
-numpy==1.23.5
-groq==0.11.0  
+numpy==1.24.4  # Trying a version just before 1.25 restructuring
+groq==0.11.0
 python-dotenv==1.0.0
-httpx==0.27.0  
+httpx==0.27.0
 scikit-learn==1.3.2