Upload app.py

app.py

@@ -578,10 +578,16 @@ class UltimateTopcoderMCPEngine:
         sort_by: str = None, sort_order: str = None,
         limit: int = 50
     ) -> Dict[str, Any]:
+        """FIXED: Debug the actual recommendation flow"""
         start_time = datetime.now()
-        print(f"🎯 Analyzing profile: {user_profile.skills} | Level: {user_profile.experience_level}")
-
-        # FIXED: More aggressive real data fetching
+        print(f"\n🎯 === STARTING RECOMMENDATION REQUEST ===")
+        print(f"🎯 User skills: {user_profile.skills}")
+        print(f"🎯 Query: '{query}'")
+        print(f"🎯 Status filter: {status}")
+        print(f"🎯 Limit: {limit}")
+
+        # FIXED: Try to get real challenges with detailed debugging
+        print(f"\n🔄 Step 1: Attempting to fetch REAL challenges...")
         real_challenges = await self.fetch_real_challenges(
             user_profile=user_profile,
             query=query,
@@ -595,29 +601,45 @@ class UltimateTopcoderMCPEngine:
             sort_order=sort_order,
         )
 
-        if real_challenges:
+        print(f"📊 Step 2: fetch_real_challenges returned {len(real_challenges)} challenges")
+
+        # Determine data source and challenges to use
+        if real_challenges and len(real_challenges) > 0:
             challenges = real_challenges
-            data_source = "🔥 REAL Topcoder MCP Server (4,596+ challenges)"
-            print(f"🎉 Using {len(challenges)} REAL Topcoder challenges!")
+            data_source = f"🔥 REAL Topcoder MCP Server ({len(challenges)} live challenges)"
+            print(f"✅ Step 3: Using {len(challenges)} REAL MCP challenges!")
+            
+            # Show sample challenge details
+            if challenges:
+                sample = challenges[0]
+                print(f"📋 Sample real challenge: {sample.title} | {sample.prize} | {sample.technologies}")
         else:
             challenges = self.mock_challenges
-            data_source = "✨ Enhanced Intelligence Engine (Premium Dataset)"
-            print(f"⚡ Using {len(challenges)} premium challenges with advanced algorithms")
+            data_source = "⚡ Enhanced Intelligence Engine (Premium Dataset)"
+            print(f"⚠️ Step 3: No real challenges returned, using {len(challenges)} fallback challenges")
+            print(f"🔍 Debug: Why no real challenges? Check fetch_real_challenges output above")
 
+        # Score and rank challenges
+        print(f"\n🧠 Step 4: Scoring {len(challenges)} challenges...")
         scored_challenges = []
         for challenge in challenges:
             score, factors = self.calculate_advanced_compatibility_score(challenge, user_profile, query)
             challenge.compatibility_score = score
             challenge.rationale = f"Match: {score:.0f}%. " + ". ".join(factors[:2]) + "."
             scored_challenges.append(challenge)
+        
         scored_challenges.sort(key=lambda x: x.compatibility_score, reverse=True)
         recommendations = scored_challenges[:5]
+        
         processing_time = (datetime.now() - start_time).total_seconds()
         query_techs = self.extract_technologies_from_query(query)
         avg_score = sum(c.compatibility_score for c in challenges) / len(challenges) if challenges else 0
-        print(f"✅ Generated {len(recommendations)} recommendations in {processing_time:.3f}s:")
-        for i, rec in enumerate(recommendations, 1):
-            print(f"   {i}. {rec.title} - {rec.compatibility_score:.0f}% compatibility")
+        
+        print(f"✅ Step 5: Generated {len(recommendations)} recommendations in {processing_time:.3f}s")
+        print(f"🏆 Top recommendation: {recommendations[0].title} ({recommendations[0].compatibility_score:.0f}%)")
+        print(f"📊 Data source being reported: {data_source}")
+        print(f"🎯 === RECOMMENDATION REQUEST COMPLETE ===\n")
+
         return {
             "recommendations": [asdict(rec) for rec in recommendations],
             "insights": {
@@ -630,7 +652,7 @@ class UltimateTopcoderMCPEngine:
                 "session_active": bool(self.session_id),
                 "mcp_connected": self.is_connected,
                 "algorithm_version": "Advanced Multi-Factor v2.0",
-                "topcoder_total": "4,596+ live challenges" if real_challenges else "Premium dataset"
+                "topcoder_total": f"{len(challenges)} challenges analyzed"
             }
         }