Updated tools

app.py

@@ -68,6 +68,8 @@ def preprocess_query(query: str) -> str:
         query = re.sub(rf'\b{k}\b', v, query, flags=re.IGNORECASE)
     query = re.sub(r'\bproblem\s*(\d+)', r'Problem_\1', query, flags=re.IGNORECASE)
     query = re.sub(r'\bquestion\s*(\d+)', r'Problem_\1', query, flags=re.IGNORECASE)
+    query = re.sub(r'\b(find|search)\s+interview\s+questions\s+for\s+', '', query, flags=re.IGNORECASE)
+    query = re.sub(r'\binterview\s+questions\b', '', query, flags=re.IGNORECASE).strip()
     return query
 
 def get_daily_coding_question(query: str = "") -> dict:
@@ -130,21 +132,21 @@ def fetch_interview_questions(query: str) -> dict:
     if not TAVILY_API_KEY:
         return {"status": "error", "response": "Tavily API key not configured"}
     
-    if not query.strip() or query.lower() in ["a interview question", "interview question", "interview questions"]:
+    if not query.strip() or query.lower() in ["a", "interview", "question", "questions"]:
         return {"status": "error", "response": "Please provide a specific topic for interview questions (e.g., 'Python', 'data structures', 'system design')."}
     
     try:
-        tavily = TavilySearch(api_key=TAVILY_API_KEY, max_results=3)
-        search_query = f"{query} interview questions site:*.edu | site:*.org | site:*.gov -inurl:(signup | login)"
+        tavily = TavilySearch(api_key=TAVILY_API_KEY, max_results=5)
+        search_query = f"{query} interview questions -inurl:(signup | login)"
         print(f"Executing Tavily search for: {search_query}")
         
-        # Use invoke method for TavilySearch
         results = tavily.invoke(search_query)
+        print(f"Raw Tavily results: {results}")
         
         if not results or not isinstance(results, list) or len(results) == 0:
             return {"status": "success", "response": "No relevant interview questions found. Try a more specific topic or different keywords."}
         
-        resp = "**Interview Questions Search Results:**\n\n"
+        resp = "**Interview Questions Search Results for '{}':**\n\n".format(query)
         for i, r in enumerate(results, 1):
             if isinstance(r, dict):
                 title = r.get('title', 'No title')
@@ -216,14 +218,14 @@ You are an interview preparation assistant. Analyze the user's query and determi
 
 Available tools:
 1. get_daily_coding_question - For coding practice, LeetCode problems, daily questions
-2. fetch_interview_questions - For searching interview questions on specific topics  
+2. fetch_interview_questions - For searching interview questions on specific topics
 3. simulate_mock_interview - For mock interview practice (HR/behavioral or technical)
 
 Instructions:
 - If user asks for coding questions, daily questions, LeetCode problems, practice problems -> use get_daily_coding_question
-- If user asks for interview questions on specific topics, wants to search for questions -> use fetch_interview_questions  
-- If user asks for mock interview, interview simulation, practice interview -> use simulate_mock_interview
-- For HR/behavioral questions specifically, use simulate_mock_interview
+- If user asks for interview questions on specific topics (e.g., Python, data structures) without "mock" or "simulate" -> use fetch_interview_questions
+- If user asks for mock interview, interview simulation, practice interview, or HR/behavioral questions -> use simulate_mock_interview
+- If user explicitly mentions "HR" or "behavioral" -> use simulate_mock_interview with HR focus
 
 Respond ONLY with valid JSON in this exact format:
 {"tool": "tool_name", "args": {"param1": "value1", "param2": "value2"}}
@@ -234,6 +236,15 @@ User Query: {query}
     def _classify_intent(self, query: str) -> tuple[str, dict]:
         query_lower = query.lower()
         
+        # Prioritize HR/behavioral for explicit mentions
+        if any(keyword in query_lower for keyword in ["hr", "behavioral", "give hr questions", "give behavioral questions"]):
+            return "simulate_mock_interview", {"query": query, "user_id": "default"}
+        
+        # Handle mock interview or simulation requests
+        if any(keyword in query_lower for keyword in ["mock interview", "practice interview", "interview simulation", "simulate_mock_interview"]):
+            return "simulate_mock_interview", {"query": query, "user_id": "default"}
+        
+        # Handle coding-related queries
         if any(keyword in query_lower for keyword in ["daily", "coding question", "leetcode", "practice problem", "coding practice"]):
             problem_match = re.search(r'problem\s*(\d+)', query_lower)
             if problem_match:
@@ -248,17 +259,12 @@ User Query: {query}
             
             return "get_daily_coding_question", {"query": ""}
         
-        if any(keyword in query_lower for keyword in ["mock interview", "practice interview", "interview simulation"]) or \
-           ("give" in query_lower and "interview question" in query_lower):
-            return "simulate_mock_interview", {"query": query, "user_id": "default"}
-        
-        if "interview question" in query_lower and any(word in query_lower for word in ["technical", "hr", "behavioral"]):
-            return "simulate_mock_interview", {"query": query, "user_id": "default"}
-        
+        # Handle topic-specific interview questions
         if any(keyword in query_lower for keyword in ["search interview questions", "find interview questions", "interview prep resources"]) or \
-           (query_lower.startswith("fetch_interview_questions") and "give" not in query_lower):
+           "interview" in query_lower:
             return "fetch_interview_questions", {"query": query}
         
+        # Fallback to LLM classification
         try:
             prompt = self.instruction_text.format(query=query)
             response = self.model.generate_content(prompt)
@@ -324,4 +330,4 @@ def root():
 
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    uvicorn.run(app, host="0.0.0.0", port=8000)