Adding time stamps in code

app.py

@@ -16,6 +16,8 @@ import json
 from datetime import datetime
 import re
 from typing import Dict, List, Tuple
+import time
+from contextlib import contextmanager
 
 import logging
 import traceback
@@ -23,31 +25,58 @@ import sys
 
 # ===== Configure Logging =====
 logging.basicConfig(
-    filename="app.log",  # All logs will be saved here
-    level=logging.INFO,  # Change to DEBUG for more detail
+    filename="app.log",
+    level=logging.INFO,
     format="%(asctime)s - %(levelname)s - %(message)s"
 )
 
-# ===== Capture Uncaught Exceptions =====
 def log_exception(exc_type, exc_value, exc_traceback):
     if issubclass(exc_type, KeyboardInterrupt):
-        return  # Don't log keyboard interrupts
+        return
     logging.critical("Uncaught exception", exc_info=(exc_type, exc_value, exc_traceback))
 
 sys.excepthook = log_exception
-
-# ===== Optional: Log that the app started =====
 logging.info("App started successfully.")
 
-# ===== Example: Use logging inside try/except =====
-def example_function():
-    try:
-        result = 1 / 0  # Intentional error
-    except Exception as e:
-        logging.error("Error in example_function: %s", e)
-        logging.error(traceback.format_exc())
+# ===== Time Tracking Class =====
+class PipelineTimer:
+    def __init__(self):
+        self.reset()
+    
+    def reset(self):
+        """Reset all timing data for a new request"""
+        self.start_time = time.time()
+        self.step_times = {}
+        self.step_start = None
+        self.current_step = None
+    
+    @contextmanager
+    def time_step(self, step_name: str):
+        """Context manager to time a specific step"""
+        step_start = time.time()
+        self.current_step = step_name
+        try:
+            yield
+        finally:
+            step_end = time.time()
+            self.step_times[step_name] = round((step_end - step_start) * 1000, 2)  # Convert to milliseconds
+            self.current_step = None
+    
+    def get_total_time(self):
+        """Get total elapsed time since reset"""
+        return round((time.time() - self.start_time) * 1000, 2)
+    
+    def get_timing_summary(self):
+        """Get a summary of all timing data"""
+        total_time = self.get_total_time()
+        return {
+            'total_time_ms': total_time,
+            'step_times': self.step_times,
+            'timestamp': datetime.now().isoformat()
+        }
 
-logging.info("Test log entry to check logger")
+# Initialize global timer
+timer = PipelineTimer()
 
 # === Configuration ===
 genai.configure(api_key=os.environ["GEMINI_API_KEY"])
@@ -55,7 +84,7 @@ embedding_model = "models/embedding-001"
 llm_model_name = "models/gemma-3-4b-it"
 collection_name = "xeno_collection"
 
-# === Google Sheets Setup for Hugging Face ===
+# === Google Sheets Setup ===
 def get_google_sheets_credentials():
     credentials_json = os.environ.get("GOOGLE_SHEETS_CREDENTIALS")
     if not credentials_json:
@@ -65,72 +94,109 @@ def get_google_sheets_credentials():
     creds = Credentials.from_service_account_info(credentials_dict, scopes=scope)
     return creds
 
-# Authenticate with Google Sheets
 client_gspread = gspread.authorize(get_google_sheets_credentials())
 
-# Open the Google Sheet
-sheet = client_gspread.open("Response_Log").sheet1
+# Open the Google Sheet and get both sheets
+spreadsheet = client_gspread.open("Response_Log")
+response_sheet = spreadsheet.sheet1  # Main response log
+try:
+    timing_sheet = spreadsheet.worksheet("Timing_Log")
+except:
+    # Create timing sheet if it doesn't exist
+    timing_sheet = spreadsheet.add_worksheet(title="Timing_Log", rows="1000", cols="15")
+    # Add headers
+    headers = [
+        "Timestamp", "Session_ID", "Question", "Total_Time_MS",
+        "Intent_Classification_MS", "Memory_Retrieval_MS", "RAG_Retrieval_MS", 
+        "Embedding_Generation_MS", "Similarity_Calculation_MS", "Context_Processing_MS",
+        "LLM_Generation_MS", "Memory_Update_MS", "Logging_MS", "Error_Step", "Notes"
+    ]
+    timing_sheet.append_row(headers)
 
 def log_response(question, answer, source_ids, knowledge_pairs, session_id):
-    """
-    Log a question, answer, source IDs, and knowledge base question-answer pairs to the Google Sheet.
-    
-    Args:
-        question (str): The question asked by the user.
-        answer (str): The answer provided by the model.
-        source_ids (str): Comma-separated list of source IDs used.
-        knowledge_pairs (list): List of tuples containing (question, answer) from the knowledge base.
-    """
+    """Original response logging function"""
     timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
     knowledge_question_1 = knowledge_pairs[0][0] if len(knowledge_pairs) > 0 else "N/A"
     knowledge_answer_1 = knowledge_pairs[0][1] if len(knowledge_pairs) > 0 else "N/A"
     knowledge_question_2 = knowledge_pairs[1][0] if len(knowledge_pairs) > 1 else "N/A"
     knowledge_answer_2 = knowledge_pairs[1][1] if len(knowledge_pairs) > 1 else "N/A"
     row = [
-        timestamp,
-        session_id,
-        question,
-        answer,
-        source_ids,
-        knowledge_question_1,
-        knowledge_answer_1,
-        knowledge_question_2,
-        knowledge_answer_2
+        timestamp, session_id, question, answer, source_ids,
+        knowledge_question_1, knowledge_answer_1, knowledge_question_2, knowledge_answer_2
     ]
     try:
-        sheet.append_row(row)
-        print(f"Logged: {question} | Source IDs: {source_ids}")
+        response_sheet.append_row(row)
+        print(f"Logged response: {question} | Source IDs: {source_ids}")
     except Exception as e:
         print(f"Failed to log to Google Sheet: {e}")
         with open("/tmp/response_log.txt", "a") as f:
             f.write(f"{timestamp},{question},{answer},{source_ids},{knowledge_question_1},{knowledge_answer_1},{knowledge_question_2},{knowledge_answer_2}\n")
 
+def log_timing_data(question, session_id, timing_summary, error_step=None, notes=None):
+    """Log timing data to the timing sheet"""
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    step_times = timing_summary['step_times']
+    
+    row = [
+        timestamp,
+        session_id,
+        question[:100] + "..." if len(question) > 100 else question,  # Truncate long questions
+        timing_summary['total_time_ms'],
+        step_times.get('intent_classification', 0),
+        step_times.get('memory_retrieval', 0),
+        step_times.get('rag_retrieval', 0),
+        step_times.get('embedding_generation', 0),
+        step_times.get('similarity_calculation', 0),
+        step_times.get('context_processing', 0),
+        step_times.get('llm_generation', 0),
+        step_times.get('memory_update', 0),
+        step_times.get('response_logging', 0),
+        error_step or "",
+        notes or ""
+    ]
+    
+    try:
+        timing_sheet.append_row(row)
+        print(f"Logged timing data: Total {timing_summary['total_time_ms']}ms")
+    except Exception as e:
+        print(f"Failed to log timing data: {e}")
+        # Fallback to local file
+        with open("/tmp/timing_log.txt", "a") as f:
+            f.write(f"{timestamp},{session_id},{question},{timing_summary}\n")
+
 # === LangGraph Memory Setup ===
 conn = sqlite3.connect("xeno_memory.db", check_same_thread=False)
 memory = SqliteSaver(conn=conn)
 
 def update_memory(config, user_message, assistant_message):
-    full_checkpoint = memory.get(config) or {}
-    messages = full_checkpoint.get("channel_values", {}).get("messages", [])
-    
-    messages.append({"role": "user", "content": user_message})
-    messages.append({"role": "assistant", "content": assistant_message})
-    
-    checkpoint_to_save = {
-        "v": 1,
-        "id": str(uuid.uuid4()),
-        "ts": datetime.now().isoformat(),
-        "channel_values": {"messages": messages},
-        "channel_versions": {},
-        "versions_seen": {},   
-    }
-    
-    memory.put(config, checkpoint_to_save, {}, {})
-    
+    """Update memory with timing"""
+    with timer.time_step("memory_update"):
+        full_checkpoint = memory.get(config) or {}
+        messages = full_checkpoint.get("channel_values", {}).get("messages", [])
+        
+        messages.append({"role": "user", "content": user_message})
+        messages.append({"role": "assistant", "content": assistant_message})
+        
+        checkpoint_to_save = {
+            "v": 1,
+            "id": str(uuid.uuid4()),
+            "ts": datetime.now().isoformat(),
+            "channel_values": {"messages": messages},
+            "channel_versions": {},
+            "versions_seen": {},   
+        }
+        
+        memory.put(config, checkpoint_to_save, {}, {})
+
+def retrieve_memory(config):
+    """Retrieve memory with timing"""
+    with timer.time_step("memory_retrieval"):
+        full_checkpoint = memory.get(config) or {}
+        return full_checkpoint.get("channel_values", {}).get("messages", [])
+
 # === Intent Classification System ===
 class IntentClassifier:
     def __init__(self):
-        # Define intent patterns and responses
         self.intent_patterns = {
             'greeting': {
                 'patterns': [
@@ -171,10 +237,7 @@ class IntentClassifier:
         }
     
     def classify_intent(self, message: str) -> Tuple[str, str]:
-        """
-        Classify the intent of a message and return appropriate response if it's a simple intent.
-        Returns: (intent_name, response) - response is empty string if intent requires RAG
-        """
+        """Classify intent with timing"""
         message_lower = message.lower().strip()
         
         for intent_name, intent_data in self.intent_patterns.items():
@@ -187,11 +250,9 @@ class IntentClassifier:
         return 'query', ''
     
     def is_simple_intent(self, intent: str) -> bool:
-        """Check if intent can be handled without RAG"""
         simple_intents = ['greeting', 'thanks']
         return intent in simple_intents
 
-# Initialize intent classifier
 intent_classifier = IntentClassifier()
 
 # === Load and Clean Knowledge Base ===
@@ -239,98 +300,169 @@ SYSTEM_PROMPT = """You are a friendly XENO Support Assistant, an AI-powered help
 Use only the information provided in the knowledge base context to answer user queries.
 Do not hallucinate. If context doesn't contain relevant info, say so in a calm polite manner by saying I'm sorry, I can't assist with that.
 Only use context that is clearly relevant to the user's question.
-For greetings like “hi” or “hello”, respond politely without using the context.
+For greetings like "hi" or "hello", respond politely without using the context.
 remember previous conversations."""
 
 # === Context Processing ===
 def process_context(results, cosine_scores, max_results=2):
-    sorted_indices = np.argsort(cosine_scores)[::-1][:max_results]
-    formatted_context = ""
-    source_ids = []
-    knowledge_pairs = []
-    for i, idx in enumerate(sorted_indices, 1):
-        result = results[idx]
-        score = cosine_scores[idx]
-        question = result.metadata.get('question', 'N/A')
-        answer = result.metadata.get('content', 'N/A')
-        formatted_context += f"Knowledge Entry {i}:\n"
-        formatted_context += f"Q: {question}\n"
-        formatted_context += f"A: {answer}\n"
-        formatted_context += "-" * 40 + "\n"
-        source_ids.append(result.metadata.get('id', 'N/A'))
-        knowledge_pairs.append((question, answer))
-    return formatted_context, source_ids, knowledge_pairs
-
-# === LLM Generation (Refactored) ===
+    """Process context with timing"""
+    with timer.time_step("context_processing"):
+        sorted_indices = np.argsort(cosine_scores)[::-1][:max_results]
+        formatted_context = ""
+        source_ids = []
+        knowledge_pairs = []
+        for i, idx in enumerate(sorted_indices, 1):
+            result = results[idx]
+            score = cosine_scores[idx]
+            question = result.metadata.get('question', 'N/A')
+            answer = result.metadata.get('content', 'N/A')
+            formatted_context += f"Knowledge Entry {i}:\n"
+            formatted_context += f"Q: {question}\n"
+            formatted_context += f"A: {answer}\n"
+            formatted_context += "-" * 40 + "\n"
+            source_ids.append(result.metadata.get('id', 'N/A'))
+            knowledge_pairs.append((question, answer))
+        return formatted_context, source_ids, knowledge_pairs
+
+# === LLM Generation ===
 def generate_xeno_response(context, question, chat_history):
-    """Generates a response but does NOT handle memory."""
-    model = genai.GenerativeModel(llm_model_name)
-    formatted_history = "\n".join(
-        [f"{msg['role'].capitalize()}: {msg['content']}" for msg in chat_history]
-    ) if chat_history else "None"
-    
-    prompt = f"{SYSTEM_PROMPT}\n### HISTORY ###\n{formatted_history}\n### CONTEXT ###\n{context}\n### QUESTION ###\n{question}"
-    
-    response = model.generate_content(prompt)
-    return response.text.strip()
-
+    """Generate response with timing"""
+    with timer.time_step("llm_generation"):
+        model = genai.GenerativeModel(llm_model_name)
+        formatted_history = "\n".join(
+            [f"{msg['role'].capitalize()}: {msg['content']}" for msg in chat_history]
+        ) if chat_history else "None"
+        
+        prompt = f"{SYSTEM_PROMPT}\n### HISTORY ###\n{formatted_history}\n### CONTEXT ###\n{context}\n### QUESTION ###\n{question}"
+        
+        response = model.generate_content(prompt)
+        return response.text.strip()
 
-# === Main Interface Logic (Refactored) ===
+# === Main Interface Logic ===
 def get_context_and_answer(message, history, session_id="default"):
-    """
-    Handles intent classification, RAG, and memory updates in one place.
-    """
-    config = {"configurable": {"thread_id": str(session_id), "checkpoint_ns": ""}}
-    
-    full_checkpoint = memory.get(config) or {}
-    chat_history = full_checkpoint.get("channel_values", {}).get("messages", [])
-    intent, direct_response = intent_classifier.classify_intent(message)
-    
-    answer = ""
-    source_ids = "N/A"
-    knowledge_pairs = []
-
-    if intent != 'query':
-        answer = direct_response
-    else: 
-        if len(message.strip()) < 3:
-            answer = "I'd be happy to help! Could you please provide more details about what you'd like to know?"
-        else:
-            try:
-                queried_results = retriever.invoke(message)
-                query_embedding = genai.embed_content(model=embedding_model, content=message, task_type="retrieval_query")['embedding']
-                
-                doc_embeddings = [genai.embed_content(model=embedding_model, content=doc.page_content, task_type="retrieval_document")['embedding'] for doc in queried_results]
-                
-                cosine_scores = util.cos_sim(torch.tensor(query_embedding).float(), torch.tensor(doc_embeddings).float())[0].tolist()
-
-                if max(cosine_scores) < 0.4:
-                    answer = "I'm sorry, I couldn't find specific information for your question. Could you try rephrasing it, or contact XENO support directly?"
-                else:
-                    context, source_ids_list, knowledge_pairs = process_context(queried_results, cosine_scores)
-                    answer = generate_xeno_response(context, message, chat_history)
-                    source_ids = ", ".join(source_ids_list)
-
-            except Exception as e:
-                print(f"Error during RAG processing: {e}")
-                answer = "I apologize, but I'm having a technical issue. Please try again shortly or contact XENO support."
-
-    update_memory(config, message, answer)
-    log_response(message, answer, source_ids, knowledge_pairs, session_id)
+    """Main pipeline with comprehensive timing"""
+    # Reset timer for new request
+    timer.reset()
+    error_step = None
+    notes = []
     
-    return answer
+    try:
+        config = {"configurable": {"thread_id": str(session_id), "checkpoint_ns": ""}}
+        
+        # Step 1: Intent Classification
+        with timer.time_step("intent_classification"):
+            intent, direct_response = intent_classifier.classify_intent(message)
+        
+        # Step 2: Memory Retrieval
+        chat_history = retrieve_memory(config)
+        
+        answer = ""
+        source_ids = "N/A"
+        knowledge_pairs = []
+
+        if intent != 'query':
+            answer = direct_response
+            notes.append(f"Simple intent: {intent}")
+        else: 
+            if len(message.strip()) < 3:
+                answer = "I'd be happy to help! Could you please provide more details about what you'd like to know?"
+                notes.append("Message too short")
+            else:
+                try:
+                    # Step 3: RAG Retrieval
+                    with timer.time_step("rag_retrieval"):
+                        queried_results = retriever.invoke(message)
+                    
+                    # Step 4: Embedding Generation
+                    with timer.time_step("embedding_generation"):
+                        query_embedding = genai.embed_content(
+                            model=embedding_model, 
+                            content=message, 
+                            task_type="retrieval_query"
+                        )['embedding']
+                        
+                        doc_embeddings = [
+                            genai.embed_content(
+                                model=embedding_model, 
+                                content=doc.page_content, 
+                                task_type="retrieval_document"
+                            )['embedding'] 
+                            for doc in queried_results
+                        ]
+                    
+                    # Step 5: Similarity Calculation
+                    with timer.time_step("similarity_calculation"):
+                        cosine_scores = util.cos_sim(
+                            torch.tensor(query_embedding).float(), 
+                            torch.tensor(doc_embeddings).float()
+                        )[0].tolist()
+                        max_score = max(cosine_scores)
+
+                    if max_score < 0.4:
+                        answer = "I'm sorry, I couldn't find specific information for your question. Could you try rephrasing it, or contact XENO support directly?"
+                        notes.append(f"Low similarity score: {max_score:.3f}")
+                    else:
+                        # Step 6: Context Processing (timed within function)
+                        context, source_ids_list, knowledge_pairs = process_context(queried_results, cosine_scores)
+                        
+                        # Step 7: LLM Generation (timed within function)
+                        answer = generate_xeno_response(context, message, chat_history)
+                        source_ids = ", ".join(source_ids_list)
+                        notes.append(f"Max similarity: {max_score:.3f}")
+
+                except Exception as e:
+                    error_step = timer.current_step or "rag_processing"
+                    print(f"Error during RAG processing: {e}")
+                    answer = "I apologize, but I'm having a technical issue. Please try again shortly or contact XENO support."
+                    notes.append(f"Error: {str(e)}")
+
+        # Step 8: Memory Update (timed within function)
+        update_memory(config, message, answer)
+        
+        # Step 9: Response Logging
+        with timer.time_step("response_logging"):
+            log_response(message, answer, source_ids, knowledge_pairs, session_id)
+        
+        # Log timing data
+        timing_summary = timer.get_timing_summary()
+        log_timing_data(
+            message, 
+            session_id, 
+            timing_summary, 
+            error_step=error_step,
+            notes="; ".join(notes) if notes else None
+        )
+        
+        return answer
+        
+    except Exception as e:
+        error_step = timer.current_step or "main_pipeline"
+        logging.error(f"Error in main pipeline: {e}")
+        logging.error(traceback.format_exc())
+        
+        # Still log timing data even on error
+        timing_summary = timer.get_timing_summary()
+        log_timing_data(
+            message, 
+            session_id, 
+            timing_summary, 
+            error_step=error_step,
+            notes=f"Pipeline error: {str(e)}"
+        )
+        
+        return "I apologize, but I encountered an error processing your request. Please try again."
 
 # === Enhanced Gradio UI ===
 def respond(message, history, session_id):
-    """Gradio's main response function."""
+    """Gradio's main response function"""
     if not session_id:
         session_id = str(uuid.uuid4())
     
     bot_response = get_context_and_answer(message, history, session_id)
-    
     history.append([message, bot_response])
     
     return "", history
+
 def create_interface():
     with gr.Blocks(theme=gr.themes.Soft()) as demo:
         gr.Markdown("""
@@ -362,10 +494,6 @@ def create_interface():
             )
             send_button = gr.Button("Send", variant="primary", scale=1)
 
-        def submit_message(message, chat_history, session_id):
-            new_msg, new_hist = respond(message, chat_history, session_id)
-            return "", new_hist 
-
         send_button.click(respond, [msg, chatbot, session_id_box], [msg, chatbot])
         msg.submit(respond, [msg, chatbot, session_id_box], [msg, chatbot])
             
@@ -373,4 +501,4 @@ def create_interface():
 
 if __name__ == "__main__":
     iface = create_interface()
-    iface.launch(share=False, server_name="0.0.0.0", server_port=7860, ssr_mode=False)
+    iface.launch(share=False, server_name="0.0.0.0", server_port=7860, ssr_mode=False)