diff --git "a/app.py" "b/app.py"
--- "a/app.py"
+++ "b/app.py"
@@ -1,6 +1,7 @@
"""
ULTIMATE Topcoder Challenge Intelligence Assistant
-๐ฅ FIXED VERSION - Indentation Error Resolved
+Combining ALL advanced features with REAL MCP Integration + OpenAI LLM
+FIXED VERSION - Hugging Face Compatible with Secrets Management
"""
import asyncio
import httpx
@@ -41,8 +42,6 @@ class UltimateTopcoderMCPEngine:
self.session_id = None
self.is_connected = False
self.mock_challenges = self._create_enhanced_fallback_challenges()
- self.cached_challenges = []
- self.last_cache_update = 0
print(f"โ
Loaded fallback system with {len(self.mock_challenges)} premium challenges")
def _create_enhanced_fallback_challenges(self) -> List[Challenge]:
@@ -107,46 +106,6 @@ class UltimateTopcoderMCPEngine:
prize="$6,000",
time_estimate="25 days",
registrants=24
- ),
- Challenge(
- id="30174846",
- title="DevOps Infrastructure Automation",
- description="Build automated CI/CD pipelines with infrastructure as code, monitoring, and deployment strategies for microservices.",
- technologies=["Kubernetes", "Terraform", "Jenkins", "AWS", "Docker"],
- difficulty="Advanced",
- prize="$5,500",
- time_estimate="20 days",
- registrants=31
- ),
- Challenge(
- id="30174847",
- title="Full-Stack Web Application",
- description="Develop a complete web application with user authentication, real-time features, and responsive design using modern frameworks.",
- technologies=["Node.js", "React", "MongoDB", "Socket.io", "Express"],
- difficulty="Intermediate",
- prize="$4,500",
- time_estimate="16 days",
- registrants=52
- ),
- Challenge(
- id="30174848",
- title="AI-Powered Customer Support Chatbot",
- description="Create an intelligent chatbot using natural language processing for customer support with sentiment analysis and multi-language support.",
- technologies=["Python", "NLP", "TensorFlow", "React", "Node.js"],
- difficulty="Advanced",
- prize="$8,000",
- time_estimate="30 days",
- registrants=15
- ),
- Challenge(
- id="30174849",
- title="Cloud Native Microservices Architecture",
- description="Design and implement a scalable microservices architecture with service mesh, observability, and security best practices.",
- technologies=["Go", "Kubernetes", "Istio", "Prometheus", "gRPC"],
- difficulty="Advanced",
- prize="$9,000",
- time_estimate="35 days",
- registrants=12
)
]
@@ -263,7 +222,7 @@ class UltimateTopcoderMCPEngine:
pass
return None
-
+
def convert_topcoder_challenge(self, tc_data: Dict) -> Challenge:
"""Convert real Topcoder challenge data with enhanced parsing"""
@@ -295,10 +254,9 @@ class UltimateTopcoderMCPEngine:
for prize_set in prize_sets:
if prize_set.get('type') == 'placement':
prizes = prize_set.get('prizes', [])
- if prizes:
- for prize in prizes:
- if prize.get('type') == 'USD':
- total_prize += prize.get('value', 0)
+ for prize in prizes:
+ if prize.get('type') == 'USD':
+ total_prize += prize.get('value', 0)
prize = f"${total_prize:,}" if total_prize > 0 else "Merit-based"
@@ -337,7 +295,7 @@ class UltimateTopcoderMCPEngine:
time_estimate=time_estimate,
registrants=registrants
)
-
+
async def fetch_real_challenges(self, limit: int = 30) -> List[Challenge]:
"""Fetch real challenges from Topcoder MCP with enhanced error handling"""
@@ -385,31 +343,6 @@ class UltimateTopcoderMCPEngine:
return challenges
- async def get_enhanced_real_challenges(self, limit: int = 20) -> List[Challenge]:
- """ENHANCED: Get real challenges with better filtering and caching"""
-
- # Check cache first
- current_time = time.time()
- if self.cached_challenges and (current_time - self.last_cache_update) < 300: # 5 min cache
- return self.cached_challenges[:limit]
-
- try:
- # Try real MCP connection first
- real_challenges = await self.fetch_real_challenges(limit)
-
- if real_challenges:
- # Update cache
- self.cached_challenges = real_challenges
- self.last_cache_update = current_time
- print(f"โ
Retrieved {len(real_challenges)} REAL challenges from MCP")
- return real_challenges
-
- except Exception as e:
- print(f"๐ MCP connection issue, using enhanced fallback: {str(e)}")
-
- # Enhanced fallback with realistic, consistent data
- return self.mock_challenges[:limit]
-
def extract_technologies_from_query(self, query: str) -> List[str]:
"""Enhanced technology extraction with expanded keywords"""
tech_keywords = {
@@ -626,12 +559,12 @@ class UltimateTopcoderMCPEngine:
"""ULTIMATE recommendation engine with real MCP data + advanced intelligence"""
start_time = datetime.now()
- print(f"๐ฏ Analyzing profile: {user_profile.skills} | Level: {user_profile.experience_level}")
+ print(f"๐ Analyzing profile: {user_profile.skills} | Level: {user_profile.experience_level}")
# Try to get real challenges first
- real_challenges = await self.get_enhanced_real_challenges(limit=50)
+ real_challenges = await self.fetch_real_challenges(limit=50)
- if len(real_challenges) > 10: # If we got substantial real data
+ if real_challenges:
challenges = real_challenges
data_source = "๐ฅ REAL Topcoder MCP Server (4,596+ challenges)"
print(f"๐ Using {len(challenges)} REAL Topcoder challenges!")
@@ -678,12 +611,12 @@ 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 len(real_challenges) > 10 else "Premium dataset"
+ "topcoder_total": "4,596+ live challenges" if real_challenges else "Premium dataset"
}
}
class EnhancedLLMChatbot:
- """FIXED: Enhanced LLM Chatbot with OpenAI Integration + HF Secrets + Anti-Hallucination"""
+ """FIXED: Enhanced LLM Chatbot with OpenAI Integration + HF Secrets"""
def __init__(self, mcp_engine):
self.mcp_engine = mcp_engine
@@ -700,29 +633,22 @@ class EnhancedLLMChatbot:
self.llm_available = True
print("โ
OpenAI API key loaded from HF secrets for intelligent responses")
- async def get_challenge_context(self, query: str, limit: int = 20) -> str:
- """ENHANCED: Get relevant challenge data for LLM context with smart filtering"""
+ async def get_challenge_context(self, query: str, limit: int = 10) -> str:
+ """Get relevant challenge data for LLM context"""
try:
- # Fetch more challenges to have better selection
- all_challenges = await self.mcp_engine.get_enhanced_real_challenges(limit=limit)
+ # Fetch real challenges from your working MCP
+ challenges = await self.mcp_engine.fetch_real_challenges(limit=limit)
- if not all_challenges:
- return "Using enhanced premium challenge dataset for analysis."
+ if not challenges:
+ return "Using premium challenge dataset for analysis."
- # ENHANCED: Filter and prioritize challenges based on user query
- relevant_challenges = self._filter_challenges_by_query(all_challenges, query)
-
- # Create rich context from filtered data
+ # Create rich context from real data
context_data = {
- "total_challenges_available": f"{len(all_challenges)}+ analyzed",
- "query_relevant_challenges": len(relevant_challenges),
+ "total_challenges_available": "4,596+",
"sample_challenges": []
}
- # Prioritize relevant challenges, then add general ones
- context_challenges = relevant_challenges[:7] + all_challenges[:3] if relevant_challenges else all_challenges[:10]
-
- for challenge in context_challenges:
+ for challenge in challenges[:5]: # Top 5 for context
challenge_info = {
"id": challenge.id,
"title": challenge.title,
@@ -740,69 +666,8 @@ class EnhancedLLMChatbot:
except Exception as e:
return f"Challenge data temporarily unavailable: {str(e)}"
- def _filter_challenges_by_query(self, challenges: List, query: str) -> List:
- """Filter challenges based on user query keywords"""
- query_lower = query.lower()
-
- # Extract technology keywords from query
- tech_keywords = {
- 'python': ['python', 'django', 'flask', 'fastapi', 'tensorflow', 'pytorch'],
- 'javascript': ['javascript', 'js', 'node', 'react', 'vue', 'angular'],
- 'java': ['java', 'spring', 'hibernate'],
- 'react': ['react', 'jsx', 'next.js', 'gatsby'],
- 'angular': ['angular', 'typescript'],
- 'vue': ['vue', 'vuejs', 'nuxt'],
- 'node': ['node', 'nodejs', 'express'],
- 'aws': ['aws', 'amazon', 'cloud', 'lambda', 's3'],
- 'docker': ['docker', 'container', 'kubernetes'],
- 'blockchain': ['blockchain', 'ethereum', 'solidity', 'web3'],
- 'ai': ['ai', 'ml', 'machine learning', 'artificial intelligence'],
- 'mobile': ['mobile', 'android', 'ios', 'react native', 'flutter'],
- 'ui': ['ui', 'ux', 'design', 'figma'],
- 'api': ['api', 'rest', 'graphql'],
- 'database': ['database', 'sql', 'mongodb', 'postgresql']
- }
-
- # Find matching keywords
- matching_keywords = []
- for main_tech, variations in tech_keywords.items():
- if any(keyword in query_lower for keyword in variations):
- matching_keywords.extend(variations)
-
- if not matching_keywords:
- return []
-
- # Filter challenges that match the query
- relevant_challenges = []
- for challenge in challenges:
- # Check if challenge technologies match query keywords
- challenge_techs_lower = [tech.lower() for tech in challenge.technologies]
- challenge_title_lower = challenge.title.lower()
- challenge_desc_lower = challenge.description.lower()
-
- # Score relevance
- relevance_score = 0
-
- # Direct technology match (highest priority)
- for keyword in matching_keywords:
- if any(keyword in tech for tech in challenge_techs_lower):
- relevance_score += 10
- if keyword in challenge_title_lower:
- relevance_score += 5
- if keyword in challenge_desc_lower:
- relevance_score += 2
-
- if relevance_score > 0:
- challenge.query_relevance = relevance_score
- relevant_challenges.append(challenge)
-
- # Sort by relevance score
- relevant_challenges.sort(key=lambda x: getattr(x, 'query_relevance', 0), reverse=True)
-
- return relevant_challenges
-
async def generate_llm_response(self, user_message: str, chat_history: List) -> str:
- """FIXED: Generate intelligent response using OpenAI API with real MCP data + anti-hallucination"""
+ """FIXED: Generate intelligent response using OpenAI API with real MCP data"""
# Get real challenge context
challenge_context = await self.get_challenge_context(user_message)
@@ -811,40 +676,35 @@ class EnhancedLLMChatbot:
recent_history = chat_history[-4:] if len(chat_history) > 4 else chat_history
history_text = "\n".join([f"User: {h[0]}\nAssistant: {h[1]}" for h in recent_history])
- # ENHANCED: Create comprehensive prompt for LLM with smart context filtering
+ # Create comprehensive prompt for LLM
system_prompt = f"""You are an expert Topcoder Challenge Intelligence Assistant with REAL-TIME access to live challenge data through MCP integration.
REAL CHALLENGE DATA CONTEXT:
{challenge_context}
-IMPORTANT CONTEXT NOTES:
-- The challenge data above has been filtered and prioritized based on the user's query
-- If query_relevant_challenges > 0, the challenges shown are specifically matched to the user's question
-- If you don't see challenges matching the user's query in the context, it may mean:
- 1. Those challenges exist but weren't in the filtered sample
- 2. The user should try the recommendation tool for personalized matching
- 3. They should check Topcoder platform directly for the most complete listing
-
Your capabilities:
-- Access to live Topcoder challenges through real MCP integration
-- Smart challenge filtering based on user queries
+- Access to 4,596+ live Topcoder challenges through real MCP integration
+- Advanced challenge matching algorithms with multi-factor scoring
- Real-time prize information, difficulty levels, and technology requirements
- Comprehensive skill analysis and career guidance
+- Market intelligence and technology trend insights
CONVERSATION HISTORY:
{history_text}
-RESPONSE GUIDELINES:
-- If challenges matching the user's query ARE in the context: Reference them specifically with details
-- If NO matching challenges in context: Acknowledge this and suggest they try the recommendation tool or check Topcoder directly
-- Always mention that this is from live MCP integration
-- If asked about highest prizes: Focus on the challenges with the largest prize amounts from the context
-- Keep responses helpful and informative (max 300 words)
-- Be honest about the limitations of the context sample while highlighting the real data access
+Guidelines:
+- Use the REAL challenge data provided above in your responses
+- Reference actual challenge titles, prizes, and technologies when relevant
+- Provide specific, actionable advice based on real data
+- Mention that your data comes from live MCP integration with Topcoder
+- Be enthusiastic about the real-time data capabilities
+- If asked about specific technologies, reference actual challenges that use them
+- For skill questions, suggest real challenges that match their level
+- Keep responses concise but informative (max 300 words)
User's current question: {user_message}
-Provide a helpful, intelligent response using the challenge data context and acknowledging any limitations."""
+Provide a helpful, intelligent response using the real challenge data context."""
# FIXED: Try OpenAI API if available
if self.llm_available:
@@ -859,10 +719,10 @@ Provide a helpful, intelligent response using the challenge data context and ack
json={
"model": "gpt-4o-mini", # Fast and cost-effective
"messages": [
- {"role": "system", "content": system_prompt},
- {"role": "user", "content": user_message}
+ {"role": "system", "content": "You are an expert Topcoder Challenge Intelligence Assistant with real MCP data access."},
+ {"role": "user", "content": system_prompt}
],
- "max_tokens": 500,
+ "max_tokens": 800,
"temperature": 0.7
}
)
@@ -877,218 +737,94 @@ Provide a helpful, intelligent response using the challenge data context and ack
return llm_response
else:
print(f"OpenAI API error: {response.status_code} - {response.text}")
- return await self.get_enhanced_fallback_response_with_context(user_message)
+ return await self.get_fallback_response_with_context(user_message, challenge_context)
except Exception as e:
print(f"OpenAI API error: {e}")
- return await self.get_enhanced_fallback_response_with_context(user_message)
+ return await self.get_fallback_response_with_context(user_message, challenge_context)
# Fallback to enhanced responses with real data
- return await self.get_enhanced_fallback_response_with_context(user_message)
+ return await self.get_fallback_response_with_context(user_message, challenge_context)
- async def get_enhanced_fallback_response_with_context(self, user_message: str) -> str:
- """ENHANCED: Smart fallback response with better challenge filtering"""
-
- # Get more challenges for better filtering
- challenges = await self.mcp_engine.get_enhanced_real_challenges(30)
-
- # Analyze user intent with enhanced keyword detection
+ async def get_fallback_response_with_context(self, user_message: str, challenge_context: str) -> str:
+ """Enhanced fallback using real challenge data"""
message_lower = user_message.lower()
- # Enhanced technology detection
- tech_mapping = {
- 'python': ['python', 'django', 'flask', 'fastapi', 'tensorflow', 'pytorch', 'pandas'],
- 'javascript': ['javascript', 'js', 'node', 'react', 'vue', 'angular'],
- 'java': ['java', 'spring', 'hibernate'],
- 'react': ['react', 'jsx', 'next.js', 'gatsby'],
- 'angular': ['angular', 'typescript'],
- 'vue': ['vue', 'vuejs', 'nuxt'],
- 'node': ['node', 'nodejs', 'express'],
- 'aws': ['aws', 'amazon', 'cloud', 'lambda', 's3'],
- 'docker': ['docker', 'container', 'kubernetes'],
- 'blockchain': ['blockchain', 'ethereum', 'solidity', 'web3', 'smart contract'],
- 'ai': ['ai', 'ml', 'machine learning', 'artificial intelligence', 'neural'],
- 'mobile': ['mobile', 'android', 'ios', 'react native', 'flutter'],
- 'ui': ['ui', 'ux', 'design', 'figma'],
- 'api': ['api', 'rest', 'graphql'],
- 'database': ['database', 'sql', 'mongodb', 'postgresql']
- }
+ # Parse challenge context for intelligent responses
+ try:
+ context_data = json.loads(challenge_context)
+ challenges = context_data.get("sample_challenges", [])
+ except:
+ challenges = []
- # Find what technologies the user is asking about
- detected_techs = []
- for main_tech, keywords in tech_mapping.items():
- if any(keyword in message_lower for keyword in keywords):
- detected_techs.append(main_tech)
+ # Technology-specific responses using real data
+ tech_keywords = ['python', 'react', 'javascript', 'blockchain', 'ai', 'ml', 'java', 'nodejs', 'angular', 'vue']
+ matching_tech = [tech for tech in tech_keywords if tech in message_lower]
- if detected_techs:
- # Filter challenges for the detected technologies
+ if matching_tech:
relevant_challenges = []
for challenge in challenges:
- challenge_techs_lower = [tech.lower() for tech in challenge.technologies]
- challenge_title_lower = challenge.title.lower()
- challenge_desc_lower = challenge.description.lower()
-
- # Check for matches
- relevance_score = 0
- matched_techs = []
-
- for tech in detected_techs:
- tech_keywords = tech_mapping[tech]
- for keyword in tech_keywords:
- # Check in technologies array
- if any(keyword in ct for ct in challenge_techs_lower):
- relevance_score += 10
- if tech not in matched_techs:
- matched_techs.append(tech)
- # Check in title
- elif keyword in challenge_title_lower:
- relevance_score += 5
- if tech not in matched_techs:
- matched_techs.append(tech)
- # Check in description
- elif keyword in challenge_desc_lower:
- relevance_score += 2
- if tech not in matched_techs:
- matched_techs.append(tech)
-
- if relevance_score > 0:
- challenge.relevance_score = relevance_score
- challenge.matched_techs = matched_techs
+ challenge_techs = [tech.lower() for tech in challenge.get('technologies', [])]
+ if any(tech in challenge_techs for tech in matching_tech):
relevant_challenges.append(challenge)
- # Sort by relevance
- relevant_challenges.sort(key=lambda x: x.relevance_score, reverse=True)
-
if relevant_challenges:
- tech_names = ", ".join(detected_techs)
- response = f"Great! I found **{len(relevant_challenges)} challenges** involving **{tech_names}** from my live MCP data:\n\n"
-
- # Show top 3-5 most relevant challenges
- for i, challenge in enumerate(relevant_challenges[:5], 1):
- response += f"**{i}. {challenge.title}**\n"
- response += f" ๐ฐ **Prize:** {challenge.prize}\n"
- response += f" ๐ ๏ธ **Technologies:** {', '.join(challenge.technologies)}\n"
- response += f" ๐ **Difficulty:** {challenge.difficulty}\n"
- response += f" ๐ฅ **Registrants:** {challenge.registrants}\n"
-
- # Add link if valid ID
- if challenge.id and challenge.id.startswith("301"):
- response += f" ๐ **[View Details](https://www.topcoder.com/challenges/{challenge.id})**\n\n"
- else:
- response += f" ๐ **Available on Topcoder platform**\n\n"
-
- # Add some insights
- if any('prize' in message_lower or 'money' in message_lower or 'pay' in message_lower for _ in [None]):
- prizes = []
- for c in relevant_challenges:
- if c.prize.startswith('$'):
- try:
- prize_str = c.prize.replace('$', '').replace(',', '')
- if prize_str.isdigit():
- prizes.append(int(prize_str))
- except:
- continue
-
- # FIXED INDENTATION: This is where the error was
- if prizes:
- avg_prize = sum(prizes) / len(prizes)
- max_prize = max(prizes)
- response += f"\n๐ก **Prize Insights:** Average prize: ${avg_prize:,.0f} | Highest: ${max_prize:,}\n"
-
- response += f"\n*๐ Found from {len(challenges)} live challenges via real MCP integration*"
+ response = f"Great question about {', '.join(matching_tech)}! ๐ Based on my real MCP data access, here are actual challenges:\n\n"
+ for i, challenge in enumerate(relevant_challenges[:3], 1):
+ response += f"๐ฏ **{challenge['title']}**\n"
+ response += f" ๐ฐ Prize: {challenge['prize']}\n"
+ response += f" ๐ ๏ธ Technologies: {', '.join(challenge['technologies'])}\n"
+ response += f" ๐ Difficulty: {challenge['difficulty']}\n"
+ response += f" ๐ฅ Registrants: {challenge['registrants']}\n\n"
+
+ response += f"*These are REAL challenges from my live MCP connection to Topcoder's database of 4,596+ challenges!*"
return response
- else:
- # No matches found, but provide helpful response
- tech_names = ", ".join(detected_techs)
- return f"""I searched through **{len(challenges)} live challenges** from the real MCP server, but didn't find any that specifically match **{tech_names}** in my current dataset.
-
-**๐ This could mean:**
-โข These challenges might be in a different category or status
-โข The technology keywords might be listed differently
-โข New challenges with these technologies haven't been added yet
-
-**๐ก Suggestions:**
-โข Try the **๐ฏ ULTIMATE Recommendations** tab above with your skills
-โข Check the Topcoder platform directly for the latest challenges
-โข Ask me about related technologies (e.g., if you asked about Python, try "web development" or "backend")
-
-*๐ Searched {len(challenges)} real challenges via live MCP integration*"""
- # Handle prize/earning questions
- elif any(word in message_lower for word in ['prize', 'money', 'earn', 'pay', 'salary', 'income', 'highest']):
+ # Prize/earning questions with real data
+ if any(word in message_lower for word in ['prize', 'money', 'earn', 'pay', 'salary', 'income']):
if challenges:
- # Sort by prize amount
- prize_challenges = []
- for challenge in challenges:
- if challenge.prize.startswith('$'):
- try:
- prize_str = challenge.prize.replace('$', '').replace(',', '')
- if prize_str.isdigit():
- prize_amount = int(prize_str)
- challenge.prize_amount = prize_amount
- prize_challenges.append(challenge)
- except:
- continue
-
- prize_challenges.sort(key=lambda x: x.prize_amount, reverse=True)
-
- if prize_challenges:
- response = f"๐ฐ **Highest Prize Challenges** from {len(challenges)} live challenges:\n\n"
- for i, challenge in enumerate(prize_challenges[:5], 1):
- response += f"**{i}. {challenge.title}**\n"
- response += f" ๐ฐ **Prize:** {challenge.prize}\n"
- response += f" ๐ ๏ธ **Technologies:** {', '.join(challenge.technologies)}\n"
- response += f" ๐ **Difficulty:** {challenge.difficulty} | ๐ฅ {challenge.registrants} registered\n"
- if challenge.id and challenge.id.startswith("301"):
- response += f" ๐ **[View Details](https://www.topcoder.com/challenges/{challenge.id})**\n\n"
- else:
- response += f" ๐ **Available on Topcoder platform**\n\n"
-
- total_prizes = sum(c.prize_amount for c in prize_challenges)
- avg_prize = total_prizes / len(prize_challenges)
- response += f"๐ **Prize Stats:** Total: ${total_prizes:,} | Average: ${avg_prize:,.0f}\n"
- response += f"\n*๐ Live prize data from real MCP integration*"
- return response
+ response = f"๐ฐ Based on real MCP data, current Topcoder challenges offer:\n\n"
+ for i, challenge in enumerate(challenges[:3], 1):
+ response += f"{i}. **{challenge['title']}** - {challenge['prize']}\n"
+ response += f" ๐ Difficulty: {challenge['difficulty']} | ๐ฅ Competition: {challenge['registrants']} registered\n\n"
+ response += f"*This is live prize data from {context_data.get('total_challenges_available', '4,596+')} real challenges!*"
+ return response
# Career/skill questions
- elif any(word in message_lower for word in ['career', 'skill', 'learn', 'beginner', 'advanced', 'help', 'start']):
+ if any(word in message_lower for word in ['career', 'skill', 'learn', 'beginner', 'advanced', 'help']):
if challenges:
sample_challenge = challenges[0]
- return f"""I'm your intelligent Topcoder assistant with **REAL MCP integration**! ๐
+ return f"""I'm your intelligent Topcoder assistant with REAL MCP integration! ๐
-I currently have live access to **{len(challenges)} real challenges**. For example:
+I currently have live access to {context_data.get('total_challenges_available', '4,596+')} real challenges. For example, right now there's:
-๐ฏ **"{sample_challenge.title}"**
-๐ฐ Prize: **{sample_challenge.prize}**
-๐ ๏ธ Technologies: {', '.join(sample_challenge.technologies[:3])}
-๐ Difficulty: {sample_challenge.difficulty}
+๐ฏ **"{sample_challenge['title']}"**
+๐ฐ Prize: **{sample_challenge['prize']}**
+๐ ๏ธ Technologies: {', '.join(sample_challenge['technologies'][:3])}
+๐ Difficulty: {sample_challenge['difficulty']}
-**I can help you with:**
+I can help you with:
๐ฏ Find challenges matching your specific skills
๐ฐ Compare real prize amounts and competition levels
๐ Analyze difficulty levels and technology requirements
๐ Career guidance based on market demand
-**๐ก Try asking me:**
-โข "What React challenges are available?"
-โข "Show me high-prize Python opportunities"
-โข "Which challenges are best for beginners?"
-โข "What technologies are most in-demand?"
+Try asking me about specific technologies like "Python challenges" or "React opportunities"!
*Powered by live MCP connection to Topcoder's challenge database*"""
- # General response with real data
+ # Default intelligent response with real data
if challenges:
return f"""Hi! I'm your intelligent Topcoder assistant! ๐ค
-I have **REAL MCP integration** with live access to **{len(challenges)} challenges** from Topcoder's database.
+I have REAL MCP integration with live access to **{context_data.get('total_challenges_available', '4,596+')} challenges** from Topcoder's database.
-**๐ฅ Currently active challenges include:**
-โข **{challenges[0].title}** ({challenges[0].prize})
-โข **{challenges[1].title}** ({challenges[1].prize})
-โข **{challenges[2].title}** ({challenges[2].prize})
+**Currently active challenges include:**
+โข **{challenges[0]['title']}** ({challenges[0]['prize']})
+โข **{challenges[1]['title']}** ({challenges[1]['prize']})
+โข **{challenges[2]['title']}** ({challenges[2]['prize']})
-**Ask me about:**
+Ask me about:
๐ฏ Specific technologies (Python, React, blockchain, etc.)
๐ฐ Prize ranges and earning potential
๐ Difficulty levels and skill requirements
@@ -1096,13 +832,9 @@ I have **REAL MCP integration** with live access to **{len(challenges)} challeng
*All responses powered by real-time Topcoder MCP data!*"""
- return "I'm your intelligent Topcoder assistant with real MCP data access! Ask me about challenges, skills, or career advice and I'll help you using live data from real challenges! ๐"
-
-# Initialize the enhanced intelligence engine
-print("๐ Starting ULTIMATE Topcoder Intelligence Assistant...")
-intelligence_engine = UltimateTopcoderMCPEngine()
+ return "I'm your intelligent Topcoder assistant with real MCP data access! Ask me about challenges, skills, or career advice and I'll help you using live data from 4,596+ real challenges! ๐"
-# FIXED: Function signature - now accepts 3 parameters as expected
+# FIXED: Properly placed standalone functions with correct signatures
async def chat_with_enhanced_llm_agent(message: str, history: List[Tuple[str, str]], mcp_engine) -> Tuple[List[Tuple[str, str]], str]:
"""FIXED: Enhanced chat with real LLM and MCP data integration - 3 parameters"""
print(f"๐ง Enhanced LLM Chat: {message}")
@@ -1132,8 +864,14 @@ def chat_with_enhanced_llm_agent_sync(message: str, history: List[Tuple[str, str
"""FIXED: Synchronous wrapper for Gradio - calls async function with correct parameters"""
return asyncio.run(chat_with_enhanced_llm_agent(message, history, intelligence_engine))
+# Initialize the ULTIMATE intelligence engine
+print("๐ Starting ULTIMATE Topcoder Intelligence Assistant...")
+intelligence_engine = UltimateTopcoderMCPEngine()
+
+# Rest of your formatting functions remain the same...
+
def format_challenge_card(challenge: Dict) -> str:
- """FIXED: Format challenge as professional HTML card without broken links"""
+ """Format challenge as professional HTML card with enhanced styling"""
# Create technology badges
tech_badges = " ".join([
@@ -1168,23 +906,6 @@ def format_challenge_card(challenge: Dict) -> str:
prize_color = "#6c757d"
prize_display = "Merit-based"
- # FIXED: Better link handling
- challenge_link = ""
- if challenge['id'] and challenge['id'].startswith("301"): # Valid Topcoder ID format
- challenge_link = f"""
- """
- else:
- challenge_link = """
-
- ๐ก Available on Topcoder platform - search by title
-
"""
-
return f"""
@@ -1229,3297 +950,6 @@ def format_challenge_card(challenge: Dict) -> str:
Registered
-
-
-
-
-
-
๐ฏ Your Intelligence Profile
-
-
-
-
๐ค Developer Profile
-
{insights['profile_type']}
-
-
-
๐ช Core Strengths
-
{insights['strengths']}
-
-
-
๐ Growth Focus
-
{insights['growth_areas']}
-
-
-
๐ Progression Path
-
{insights['skill_progression']}
-
-
-
๐ Market Intelligence
-
{insights['market_trends']}
-
-
-
๐ฏ Success Forecast
-
{insights['success_probability']}
-
-
-
-
-
โ ๏ธ
-
Please enter your skills
-
Example: Python, JavaScript, React, AWS, Docker
-
-
{data_source_emoji}
-
Found {len(recommendations)} Perfect Matches!
-
Personalized using {insights_data['algorithm_version']} โข {insights_data['processing_time']} response time
-
Source: {insights_data['data_source']}
-
-
๐
-
No perfect matches found
-
Try adjusting your skills, experience level, or interests for better results
-
-
โณ
-
Processing Error
-
{str(e)}
-
Please try again or contact support
-
-
๐ฅ ULTIMATE Powered by OpenAI GPT-4 + Real MCP Integration
-
- Revolutionizing developer success through authentic challenge discovery,
- advanced AI intelligence, and secure enterprise-grade API management.
-
-
- ๐ฏ Live Connection to 4,596+ Real Challenges โข ๐ค OpenAI GPT-4 Integration โข ๐ Secure HF Secrets Management
-
-
-
๐ ULTIMATE Topcoder Challenge Intelligence Assistant
-
๐ฅ Real MCP Integration โข ๐ค OpenAI GPT-4 โข โก Lightning Performance
-
๐ฏ Built with Gradio โข ๐ Deployed on Hugging Face Spaces โข ๐ Competition-Winning Quality
-
๐ OpenAI Status: {"โ
Active" if os.getenv("OPENAI_API_KEY") else "โ ๏ธ Configure OPENAI_API_KEY in HF Secrets"}
-
- ๐ก Available on Topcoder platform - search by title
-
"""
-
- return f"""
-
-
-
-
-
-
-
{challenge['title']}
-
-
{score:.0f}%
-
{score_label}
-
-
-
-
{challenge['description']}
-
-
-
๐ ๏ธ Technologies & Skills:
-
{tech_badges}
-
-
-
-
๐ญ Why This Matches You:
-
{challenge['rationale']}
-
-
-
-
-
{prize_display}
-
Prize Pool
-
-
-
{challenge['difficulty']}
-
Difficulty
-
-
-
{challenge['time_estimate']}
-
Timeline
-
-
-
{challenge.get('registrants', 'N/A')}
-
Registered
-
-
-
- {challenge_link}
-
-
-
-
-
-
-
๐ฏ Your Intelligence Profile
-
-
-
-
๐ค Developer Profile
-
{insights['profile_type']}
-
-
-
๐ช Core Strengths
-
{insights['strengths']}
-
-
-
๐ Growth Focus
-
{insights['growth_areas']}
-
-
-
๐ Progression Path
-
{insights['skill_progression']}
-
-
-
๐ Market Intelligence
-
{insights['market_trends']}
-
-
-
๐ฏ Success Forecast
-
{insights['success_probability']}
-
-
-
-
-
โ ๏ธ
-
Please enter your skills
-
Example: Python, JavaScript, React, AWS, Docker
-
-
{data_source_emoji}
-
Found {len(recommendations)} Perfect Matches!
-
Personalized using {insights_data['algorithm_version']} โข {insights_data['processing_time']} response time
-
Source: {insights_data['data_source']}
-
-
๐
-
No perfect matches found
-
Try adjusting your skills, experience level, or interests for better results
-
-
โ
-
Processing Error
-
{str(e)}
-
Please try again or contact support
-
-
๐ฅ ULTIMATE Powered by OpenAI GPT-4 + Real MCP Integration
-
- Revolutionizing developer success through authentic challenge discovery,
- advanced AI intelligence, and secure enterprise-grade API management.
-
-
- ๐ฏ Live Connection to 4,596+ Real Challenges โข ๐ค OpenAI GPT-4 Integration โข ๐ Secure HF Secrets Management
-
-
-
๐ ULTIMATE Topcoder Challenge Intelligence Assistant
-
๐ฅ Real MCP Integration โข ๐ค OpenAI GPT-4 โข โก Lightning Performance
-
๐ฏ Built with Gradio โข ๐ Deployed on Hugging Face Spaces โข ๐ Competition-Winning Quality
-
๐ OpenAI Status: {"โ
Active" if os.getenv("OPENAI_API_KEY") else "โ ๏ธ Configure OPENAI_API_KEY in HF Secrets"}
-
- ๐ก Available on Topcoder platform - search by title
-
"""
-
- return f"""
-
-
-
-
-
-
-
{challenge['title']}
-
-
{score:.0f}%
-
{score_label}
-
-
-
-
{challenge['description']}
-
-
-
๐ ๏ธ Technologies & Skills:
-
{tech_badges}
-
-
-
-
๐ญ Why This Matches You:
-
{challenge['rationale']}
-
-
-
-
-
{prize_display}
-
Prize Pool
-
-
-
{challenge['difficulty']}
-
Difficulty
-
-
-
{challenge['time_estimate']}
-
Timeline
-
-
-
{challenge.get('registrants', 'N/A')}
-
Registered
-
-
-
- {challenge_link}
-
-
-
-
-
-
-
๐ฏ Your Intelligence Profile
-
-
-
-
๐ค Developer Profile
-
{insights['profile_type']}
-
-
-
๐ช Core Strengths
-
{insights['strengths']}
-
-
-
๐ Growth Focus
-
{insights['growth_areas']}
-
-
-
๐ Progression Path
-
{insights['skill_progression']}
-
-
-
๐ Market Intelligence
-
{insights['market_trends']}
-
-
-
๐ฏ Success Forecast
-
{insights['success_probability']}
-
-
-
-
-
โ ๏ธ
-
Please enter your skills
-
Example: Python, JavaScript, React, AWS, Docker
-
-
{data_source_emoji}
-
Found {len(recommendations)} Perfect Matches!
-
Personalized using {insights_data['algorithm_version']} โข {insights_data['processing_time']} response time
-
Source: {insights_data['data_source']}
-
-
๐
-
No perfect matches found
-
Try adjusting your skills, experience level, or interests for better results
-
-
โ
-
Processing Error
-
{str(e)}
-
Please try again or contact support
-
-
๐ฅ ULTIMATE Powered by OpenAI GPT-4 + Real MCP Integration
-
- Revolutionizing developer success through authentic challenge discovery,
- advanced AI intelligence, and secure enterprise-grade API management.
-
-
- ๐ฏ Live Connection to 4,596+ Real Challenges โข ๐ค OpenAI GPT-4 Integration โข ๐ Secure HF Secrets Management
-
-
-
๐ ULTIMATE Topcoder Challenge Intelligence Assistant
-
๐ฅ Real MCP Integration โข ๐ค OpenAI GPT-4 โข โก Lightning Performance
-
๐ฏ Built with Gradio โข ๐ Deployed on Hugging Face Spaces โข ๐ Competition-Winning Quality
-
๐ OpenAI Status: {"โ
Active" if os.getenv("OPENAI_API_KEY") else "โ ๏ธ Configure OPENAI_API_KEY in HF Secrets"}
-
- ๐ก Available on Topcoder platform - search by title
-
"""
-
- return f"""
-
-
-
-
-
-
-
{challenge['title']}
-
-
{score:.0f}%
-
{score_label}
-
-
-
-
{challenge['description']}
-
-
-
๐ ๏ธ Technologies & Skills:
-
{tech_badges}
-
-
-
-
๐ญ Why This Matches You:
-
{challenge['rationale']}
-
-
-
-
-
{prize_display}
-
Prize Pool
-
-
-
{challenge['difficulty']}
-
Difficulty
-
-
-
{challenge['time_estimate']}
-
Timeline
-
-
-
{challenge.get('registrants', 'N/A')}
-
Registered
-
-
-
- {challenge_link}
-
-
-
-
-
-
-
๐ฏ Your Intelligence Profile
-
-
-
-
๐ค Developer Profile
-
{insights['profile_type']}
-
-
-
๐ช Core Strengths
-
{insights['strengths']}
-
-
-
๐ Growth Focus
-
{insights['growth_areas']}
-
-
-
๐ Progression Path
-
{insights['skill_progression']}
-
-
-
๐ Market Intelligence
-
{insights['market_trends']}
-
-
-
๐ฏ Success Forecast
-
{insights['success_probability']}
-
-
-
-
-
โ ๏ธ
-
Please enter your skills
-
Example: Python, JavaScript, React, AWS, Docker
-
-
{data_source_emoji}
-
Found {len(recommendations)} Perfect Matches!
-
Personalized using {insights_data['algorithm_version']} โข {insights_data['processing_time']} response time
-
Source: {insights_data['data_source']}
-
-
๐
-
No perfect matches found
-
Try adjusting your skills, experience level, or interests for better results
-
-
โ
-
Processing Error
-
{str(e)}
-
Please try again or contact support
-
-
๐ฅ ULTIMATE Powered by OpenAI GPT-4 + Real MCP Integration
-
- Revolutionizing developer success through authentic challenge discovery,
- advanced AI intelligence, and secure enterprise-grade API management.
-
-
- ๐ฏ Live Connection to 4,596+ Real Challenges โข ๐ค OpenAI GPT-4 Integration โข ๐ Secure HF Secrets Management
-
-
-
๐ ULTIMATE Topcoder Challenge Intelligence Assistant
-
๐ฅ Real MCP Integration โข ๐ค OpenAI GPT-4 โข โก Lightning Performance
-
๐ฏ Built with Gradio โข ๐ Deployed on Hugging Face Spaces โข ๐ Competition-Winning Quality
-
๐ OpenAI Status: {"โ
Active" if os.getenv("OPENAI_API_KEY") else "โ ๏ธ Configure OPENAI_API_KEY in HF Secrets"}
-
- ๐ก Available on Topcoder platform - search by title
-
"""
-
- return f"""
-
-
-
-
-
-
-
{challenge['title']}
-
-
{score:.0f}%
-
{score_label}
-
-
-
-
{challenge['description']}
-
-
-
๐ ๏ธ Technologies & Skills:
-
{tech_badges}
-
-
-
-
๐ญ Why This Matches You:
-
{challenge['rationale']}
-
-
-
-
-
{prize_display}
-
Prize Pool
-
-
-
{challenge['difficulty']}
-
Difficulty
-
-
-
{challenge['time_estimate']}
-
Timeline
-
-
-
{challenge.get('registrants', 'N/A')}
-
Registered
-
-
-
- {challenge_link}
-
โ
+
โ
Processing Error
{str(e)}
Please try again or contact support