Update travel.py

travel.py

@@ -1,556 +1,629 @@
-"""
-# travel.py - AI Agent System for Travel Planning
-# ----------------------------------------------
-#
-# This module implements a sophisticated multi-agent system for travel planning
-# with a specific focus on Indian travel destinations and experiences.
-#
-# ARCHITECTURE:
-# The system uses a collection of specialized AI agents, each responsible for
-# a different aspect of travel planning:
-#
-# 1. Destination Research Agent - Researches destinations based on user preferences
-# 2. Accommodation Agent - Suggests suitable accommodations meeting budget and preference constraints
-# 3. Transportation Agent - Plans optimal transportation between locations
-# 4. Activities Agent - Curates personalized activities based on interests
-# 5. Dining Agent - Recommends dining experiences showcasing local cuisine
-# 6. Itinerary Agent - Compiles all recommendations into a cohesive day-by-day plan
-# 7. Chatbot Agent - Provides conversational responses to travel queries
-#
-# Each agent is powered by Google's Generative AI (Gemini) and is specialized through
-# careful system prompting that defines its role, goals, and expected outputs.
-#
-# WORKFLOW:
-# 1. User submits travel preferences
-# 2. Each agent processes the information in sequence
-# 3. Final itinerary is compiled from all agent outputs
-# 4. Results are presented to the user as a complete travel plan
-#
-# PRIMARY FUNCTIONS:
-# - run_task(): Core function to execute a specific agent task
-# - generate_travel_itinerary(): Orchestrates the full planning process
-# - save_itinerary_to_file(): Saves the generated itinerary for the user
-#
-# Created by TechMatrix Solvers for IIITDMJ HackByte3.0
-"""
-
-import os
-import json
-import logging
-from datetime import datetime, timedelta
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.schema import SystemMessage, HumanMessage
-
-# Setup logging configuration
-logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
-
-# -------------------------------------------------------------------------------
-# Agent and Task Classes with Type Hints and Docstrings
-# -------------------------------------------------------------------------------
-class Agent:
-    def __init__(self, role: str, goal: str, backstory: str, personality: str = "", llm=None) -> None:
-        """
-        Initialize an Agent with role, goal, backstory, personality, and assigned LLM.
-        """
-        self.role = role
-        self.goal = goal
-        self.backstory = backstory
-        self.personality = personality
-        self.tools = []  # Initialize with empty list for future tool integrations
-        self.llm = llm
-
-class Task:
-    def __init__(self, description: str, agent: Agent, expected_output: str, context=None) -> None:
-        """
-        Initialize a Task with its description, the responsible agent, expected output, and optional context.
-        """
-        self.description = description
-        self.agent = agent
-        self.expected_output = expected_output
-        self.context = context or []
-
-# -------------------------------------------------------------------------------
-# Initialize LLM
-# -------------------------------------------------------------------------------
-def initialize_llm(api_key=None):
-    """Initialize the LLM with the provided API key or from environment variables.
-    
-    Args:
-        api_key (str, optional): API key for Google Generative AI. 
-            If None, will try to get from environment variables.
-            
-    Returns:
-        ChatGoogleGenerativeAI or None: Initialized LLM instance or None if initialization failed.
-    """
-    # First try the provided API key
-    if api_key:
-        google_api_key = api_key
-    else:
-        # Fall back to environment variable
-        google_api_key = os.getenv("GEMINI_API_KEY")
-    
-    if not google_api_key:
-        logging.warning("GEMINI_API_KEY is not set. AI functionality will be limited.")
-        return None
-    
-    # Basic API key format validation
-    if not google_api_key.startswith("AI"):
-        logging.warning("API key format appears incorrect. Should start with 'AI'.")
-    
-    try:
-        # Attempt to initialize the LLM
-        llm_instance = ChatGoogleGenerativeAI(
-            model="gemini-2.0-flash", 
-            google_api_key=google_api_key,
-            temperature=0.7,
-            top_p=0.95,
-            top_k=40,
-            max_output_tokens=8192
-        )
-        logging.info("LLM initialized successfully.")
-        return llm_instance
-    except Exception as e:
-        logging.error(f"Error initializing LLM: {e}")
-        # More detailed error message for common issues
-        if "403" in str(e) or "401" in str(e):
-            logging.error("Authentication error: Your API key may be invalid or expired.")
-        elif "429" in str(e):
-            logging.error("Rate limit exceeded: Too many requests to the API.")
-        elif "timeout" in str(e).lower():
-            logging.error("Request timed out: Network issue or service unavailability.")
-        return None
-
-# Initialize with environment variable for now
-llm = initialize_llm()
-
-# -------------------------------------------------------------------------------
-# Define Travel Agents
-# -------------------------------------------------------------------------------
-destination_research_agent = Agent(
-    role="Destination Research Agent",
-    goal=(
-        "Research and provide comprehensive information about the destination including popular attractions, "
-        "local culture, weather patterns, best times to visit, and local transportation options with special focus on Indian context."
-    ),
-    backstory=(
-        "An experienced travel researcher with extensive knowledge of Indian destinations. "
-        "I specialize in uncovering both popular attractions and hidden gems that match travelers' interests."
-    ),
-    personality="Curious, detail-oriented, and knowledgeable about Indian cultures and travel trends.",
-    llm=llm,
-)
-
-accommodation_agent = Agent(
-    role="Accommodation Agent",
-    goal="Find and recommend suitable accommodations based on the traveler's preferences, budget, and location requirements with focus on Indian hospitality options.",
-    backstory="A hospitality expert who understands different types of accommodations in India and can match travelers with their ideal places to stay.",
-    personality="Attentive, resourceful, and focused on comfort and value.",
-    llm=llm,
-)
-
-transportation_agent = Agent(
-    role="Transportation Agent",
-    goal="Plan efficient transportation between the origin, destination, and all points of interest in the itinerary using Indian transportation networks.",
-    backstory="A logistics specialist with knowledge of India's transportation systems, from flights to local transit options including trains, buses, and auto-rickshaws.",
-    personality="Efficient, practical, and detail-oriented.",
-    llm=llm,
-)
-
-activities_agent = Agent(
-    role="Activities & Attractions Agent",
-    goal="Curate personalized activities and attractions that align with the traveler's interests, preferences, and time constraints in Indian destinations.",
-    backstory="An enthusiastic explorer who has experienced diverse activities across India and knows how to match experiences to individual preferences.",
-    personality="Enthusiastic, creative, and personable.",
-    llm=llm,
-)
-
-dining_agent = Agent(
-    role="Dining & Culinary Agent",
-    goal="Recommend dining experiences that showcase local Indian cuisine while accommodating dietary preferences and budget considerations.",
-    backstory="A culinary expert with knowledge of India's diverse food scenes and an appreciation for authentic local dining experiences.",
-    personality="Passionate about food, culturally aware, and attentive to preferences.",
-    llm=llm,
-)
-
-itinerary_agent = Agent(
-    role="Itinerary Integration Agent",
-    goal="Compile all recommendations into a cohesive, day-by-day itinerary that optimizes time, minimizes travel fatigue, and maximizes enjoyment for travel in India.",
-    backstory="A master travel planner who understands how to balance activities, rest, and logistics to create the perfect Indian travel experience.",
-    personality="Organized, balanced, and practical.",
-    llm=llm,
-)
-
-# -------------------------------------------------------------------------------
-# Define Chatbot Agent and Task for Interactive Conversation
-# -------------------------------------------------------------------------------
-chatbot_agent = Agent(
-    role="Chatbot Agent",
-    goal="Engage in interactive conversation to answer travel-related queries about India.",
-    backstory="A conversational AI assistant who provides instant, accurate travel information and recommendations for Indian destinations.",
-    personality="Friendly, conversational, and knowledgeable about travel in India.",
-    llm=llm,
-)
-
-chatbot_task = Task(
-    description="Provide a conversational and detailed response to travel-related queries about Indian destinations.",
-    agent=chatbot_agent,
-    expected_output="A friendly, helpful response to the user's query about travel in India."
-)
-
-# -------------------------------------------------------------------------------
-# Define Other Travel Tasks
-# -------------------------------------------------------------------------------
-destination_research_task = Task(
-    description="""Research {destination} thoroughly, considering the traveler's interests in {preferences}.
-    
-Efficient research parameters:
-- Prioritize research in these critical categories:
-    * Top attractions that match specific {preferences} (not generic lists)
-    * Local transportation systems with cost-efficiency analysis
-    * Neighborhood breakdown with accommodation recommendations by budget tier
-    * Seasonal considerations for the specific travel dates
-    * Safety assessment with specific areas to embrace or avoid
-    * Cultural norms that impact visitor experience (dress codes, tipping, etiquette)
-    
-- Apply efficiency filters:
-    * Focus exclusively on verified information from official tourism boards, recent travel guides, and reliable local sources
-    * Analyze recent visitor reviews (< 6 months old) to identify changing conditions
-    * Evaluate price-to-experience value for attractions instead of just popularity
-    * Identify logistical clusters where multiple interests can be satisfied efficiently
-    * Research off-peak times for popular attractions to minimize waiting
-    * Evaluate digital tools (apps, passes, reservation systems) that streamline the visit
-    
-- Create practical knowledge matrices:
-    * Transportation method comparison (cost vs. time vs. convenience)
-    * Weather impact on specific activities
-    * Budget allocation recommendations based on preference priorities
-    * Time-saving opportunity identification""",
-    agent=destination_research_agent,
-    expected_output="""Targeted destination brief containing:
-1. Executive summary highlighting the 5 most relevant aspects based on {preferences}
-2. Neighborhood analysis with accommodation recommendations mapped to specific interests
-3. Transportation efficiency guide with cost/convenience matrix
-4. Cultural briefing focusing only on need-to-know information that impacts daily activities
-5. Seasonal advantages and challenges specific to travel dates
-6. Digital resource toolkit (essential apps, websites, reservation systems)
-7. Budget optimization strategies with price ranges for key experiences
-8. Safety and health quick-reference including emergency contacts
-9. Logistics efficiency map showing optimal activity clustering
-10. Local insider advantage recommendations that save time or money
-
-Format should prioritize scannable information with bullet points, comparison tables, and decision matrices rather than lengthy prose."""
-)
-
-accommodation_task = Task(
-    description="Find suitable accommodations in {destination} based on a {budget} budget and preferences for {preferences}. Focus on Indian accommodations including heritage hotels, homestays, and modern options.",
-    agent=accommodation_agent,
-    expected_output="List of recommended accommodations with details on location, amenities, price range, and availability."
-)
-
-transportation_task = Task(
-    description="Plan transportation from {origin} to {destination} and local transportation options during the stay. Include Indian railways, local buses, metro systems, and ride-hailing services where available.",
-    agent=transportation_agent,
-    expected_output="Transportation plan including flights/routes to the destination and recommendations for getting around locally."
-)
-
-activities_task = Task(
-    description="""Suggest activities and attractions in {destination} that align with interests in {preferences}.
-    
-Detailed requirements:
-- Categorize activities into: Cultural Experiences, Outdoor Adventures, Culinary Experiences, 
-  Entertainment & Nightlife, Family-Friendly Activities, and Local Hidden Gems
-- For each activity, include:
-    * Detailed description with historical/cultural context where relevant
-    * Precise location with neighborhood information
-    * Operating hours with seasonal variations noted
-    * Pricing information with different ticket options/packages
-    * Accessibility considerations for travelers with mobility limitations
-    * Recommended duration for the activity (minimum and ideal time)
-    * Best time of day/week/year to visit
-    * Crowd levels by season
-    * Photography opportunities and restrictions
-    * Required reservations or booking windows
-- Include a mix of iconic must-see attractions and off-the-beaten-path experiences
-- Consider weather patterns in {destination} during travel period
-- Analyze the {preferences} to match specific personality types and interest levels
-- Include at least 2-3 rainy day alternatives for outdoor activities
-- Provide local transportation options to reach each attraction
-- Note authentic local experiences that provide cultural immersion
-- Flag any activities requiring special equipment, permits, or physical fitness levels""",
-    agent=activities_agent,
-    expected_output="""Comprehensive curated list of activities and attractions with:
-1. Clear categorization by type (cultural, outdoor, culinary, entertainment, family-friendly, hidden gems)
-2. Essential details for planning (location, timing, cost, requirements)
-3. Special recommendations based on {preferences} with personalized suggestions
-4. Weather contingency options
-5. Insider tips for maximizing experience value
-6. Logistical guidance for efficient navigation between attractions"""
-)
-
-dining_task = Task(
-    description="""Recommend dining options in {destination} that showcase local cuisine while accommodating {preferences} with special attention to authentic Indian culinary experiences.
-    
-For each recommended dining establishment, provide:
-- Name, location, and cuisine type
-- Signature dishes and culinary specialties
-- Price range and value assessment
-- Ambiance description and dress code if applicable
-- Operating hours and reservation policies
-- Local popularity vs. tourist popularity
-- Special dietary accommodations (vegetarian, vegan, gluten-free, etc.)
-- Authentic cultural dining experiences that provide insight into local life""",
-    agent=dining_agent,
-    expected_output="""Curated dining guide for {destination} with:
-1. Top restaurant recommendations categorized by meal type, cuisine, and price point
-2. Must-try local dishes specific to the region
-3. Unique dining experiences that reflect local culture
-4. Budget-friendly options with exceptional value
-5. Strategic meal timing to complement activity schedule
-6. Advance reservation requirements where applicable
-7. Special dietary consideration options
-8. Hidden gems frequented by locals"""
-)
-
-itinerary_task = Task(
-    description="""Create a day-by-day itinerary for a {duration}-day trip to {destination} based on all previous research and recommendations. Focus on authentic experiences that reflect true Indian culture and heritage.
-    
-The itinerary should:
-- Balance activities with rest and travel time
-- Group attractions by geographic proximity to minimize transit time
-- Consider opening hours, best times to visit, and seasonal factors
-- Include meal recommendations that complement the day's activities
-- Provide contingency options for weather or unexpected closures
-- Balance must-see attractions with authentic local experiences
-- Account for {preferences} in prioritizing experiences
-- Include precise timing, transportation methods, and logistical details
-- Optimize for the traveler's interests while maintaining a realistic pace
-- Incorporate free time for exploration and spontaneity""",
-    agent=itinerary_agent,
-    expected_output="""Complete day-by-day itinerary containing:
-1. Daily schedule with timing, locations, and activities
-2. Transportation details between all points
-3. Meal recommendations with timing and cuisine type
-4. Activity duration estimates with buffer time included
-5. Alternative options for flexibility
-6. Booking/reservation requirements with deadlines
-7. Cost estimates for budgeting purposes
-8. Local cultural insights for each experience
-9. Strategic planning notes (best photo spots, crowd avoidance, etc.)
-10. Evening entertainment options
-
-Format should be scannable with clear headings, timing, and logistical details for easy reference during travel."""
-)
-
-# -------------------------------------------------------------------------------
-# Helper Function to Run a Task with Full Agent & Task Information
-# -------------------------------------------------------------------------------
-def run_task(task: Task, input_text: str, api_key=None) -> str:
-    """
-    Run an agent task with the given input text and API key.
-    
-    Args:
-        task: The Task to run
-        input_text: User input text
-        api_key: Optional Gemini API key to use
-        
-    Returns:
-        str: The generated response or error message
-    """
-    # Check if we need to initialize or reinitialize the LLM
-    current_llm = initialize_llm(api_key)
-    if current_llm:
-        # Update the agent's LLM
-        task.agent.llm = current_llm
-    elif not task.agent.llm:
-        logging.error("No valid API key provided")
-        return "⚠️ API Key Error: Please enter a valid Gemini API key in the settings to access AI features."
-    
-    # Prepare the system prompt
-    system_prompt = f"""
-    # Role: {task.agent.role}
-    # Goal: {task.agent.goal}
-    # Backstory: {task.agent.backstory}
-    
-    Instructions for output:
-    {task.expected_output}
-    """
-    
-    messages = [
-        SystemMessage(content=system_prompt),
-        HumanMessage(content=input_text)
-    ]
-    
-    try:
-        # Start tracking time for possible timeout issues
-        start_time = datetime.now()
-        
-        # Make the API call with timeout handling
-        response = task.agent.llm.invoke(messages).content
-        
-        # Calculate response time for logging
-        response_time = (datetime.now() - start_time).total_seconds()
-        logging.info(f"Task '{task.description[:30]}...' completed in {response_time:.2f} seconds")
-        
-        return response
-    except Exception as e:
-        error_msg = str(e)
-        logging.error(f"Error running task: {error_msg}")
-        
-        # Create user-friendly error messages based on the exception
-        if "timeout" in error_msg.lower():
-            return "⚠️ Request timed out. The service might be experiencing high traffic. Please try again later."
-        elif "429" in error_msg:
-            return "⚠️ Rate limit exceeded. Please try again in a few minutes."
-        elif "403" in error_msg or "401" in error_msg or "authentication" in error_msg.lower():
-            return "⚠️ API Key Error: Your API key appears to be invalid or has expired. Please update it in settings."
-        elif "quota" in error_msg.lower():
-            return "⚠️ API quota exceeded. Your Gemini API key has reached its usage limit."
-        else:
-            # Generic error message for other types of errors
-            return f"⚠️ Error processing your request. Please try again or check your API key settings."
-
-# -------------------------------------------------------------------------------
-# User Input Functions
-# -------------------------------------------------------------------------------
-def get_user_input() -> dict:
-    """
-    Collects user input for travel itinerary generation.
-    """
-    print("\n=== Travel Itinerary Generator ===\n")
-    origin = input("Enter your origin: ")
-    destination = input("Enter your destination: ")
-    duration = input("Enter duration in days: ")
-    
-    current_date = datetime.now()
-    start_date = current_date + timedelta(days=7)
-    end_date = start_date + timedelta(days=int(duration))
-    
-    preferences = input("Enter your preferences (comma separated): ")
-    budget = input("Enter your budget: ")
-    
-    return {
-        "origin": origin,
-        "destination": destination,
-        "duration": duration,
-        "start_date": start_date.strftime("%Y-%m-%d"),
-        "end_date": end_date.strftime("%Y-%m-%d"),
-        "preferences": preferences,
-        "budget": budget
-    }
-
-# -------------------------------------------------------------------------------
-# Main Function to Generate Travel Itinerary
-# -------------------------------------------------------------------------------
-def generate_travel_itinerary(user_input: dict) -> str:
-    """
-    Generates a personalized travel itinerary by sequentially running defined tasks.
-    """
-    print("\nGenerating your personalized travel itinerary...\n")
-    
-    # Create input context using f-string formatting
-    input_context = (
-        f"Travel Request Details:\n"
-        f"Origin: {user_input['origin']}\n"
-        f"Destination: {user_input['destination']}\n"
-        f"Duration: {user_input['duration']} days\n"
-        f"Budget Level: {user_input['budget']}\n"
-        f"Preferences/Interests: {user_input['preferences']}\n"
-        f"Special Requirements: {user_input['special_requirements']}\n"
-    )
-    
-    # Step 1: Destination Research
-    print("Researching your destination...")
-    destination_info = run_task(destination_research_task, input_context)
-    print("✓ Destination research completed")
-    
-    # Step 2: Accommodation Recommendations
-    print("Finding ideal accommodations...")
-    accommodation_info = run_task(accommodation_task, input_context)
-    print("✓ Accommodation recommendations completed")
-    
-    # Step 3: Transportation Planning
-    print("Planning transportation...")
-    transportation_info = run_task(transportation_task, input_context)
-    print("✓ Transportation planning completed")
-    
-    # Step 4: Activities & Attractions
-    print("Curating activities and attractions...")
-    activities_info = run_task(activities_task, input_context)
-    print("✓ Activities and attractions curated")
-    
-    # Step 5: Dining Recommendations
-    print("Finding dining experiences...")
-    dining_info = run_task(dining_task, input_context)
-    print("✓ Dining recommendations completed")
-    
-    # Step 6: Create Day-by-Day Itinerary
-    print("Creating your day-by-day itinerary...")
-    combined_info = (
-        input_context + "\n"
-        "Destination Information:\n" + destination_info + "\n"
-        "Accommodation Options:\n" + accommodation_info + "\n"
-        "Transportation Plan:\n" + transportation_info + "\n"
-        "Recommended Activities:\n" + activities_info + "\n"
-        "Dining Recommendations:\n" + dining_info + "\n"
-    )
-    itinerary = run_task(itinerary_task, combined_info)
-    print("✓ Itinerary creation completed")
-    print("✓ Itinerary generation completed")
-    
-    return itinerary
-
-# -------------------------------------------------------------------------------
-# Save Itinerary to File
-# -------------------------------------------------------------------------------
-def save_itinerary_to_file(itinerary: str, user_input: dict, output_dir: str = None) -> str:
-    """
-    Saves the generated itinerary to a text file and returns the filepath.
-    """
-    date_str = datetime.now().strftime("%Y-%m-%d")
-    filename = f"India_Travel_Itinerary_{user_input['destination']}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt"
-    
-    if output_dir:
-        if not os.path.exists(output_dir):
-            try:
-                os.makedirs(output_dir)
-                logging.info(f"Created output directory: {output_dir}")
-            except Exception as e:
-                logging.error(f"Error creating directory {output_dir}: {e}")
-                return ""
-        filepath = os.path.join(output_dir, filename)
-    else:
-        filepath = filename
-    
-    try:
-        with open(filepath, "w", encoding="utf-8") as f:
-            f.write(itinerary)
-        logging.info(f"Your itinerary has been saved as: {filepath}")
-        return filepath
-    except Exception as e:
-        logging.error(f"Error saving itinerary: {e}")
-        return ""
-
-# -------------------------------------------------------------------------------
-# Main Function
-# -------------------------------------------------------------------------------
-def main() -> None:
-    """
-    Main entry point for the travel itinerary generator application.
-    """
-    print("Welcome to the India Travel Planner! Let's create your perfect itinerary.")
-    user_input = get_user_input()
-    
-    print("\nGenerating your personalized travel itinerary...\n")
-    itinerary = generate_travel_itinerary(user_input)
-    
-    print("\n" + "=" * 50)
-    print("Your travel itinerary is ready!")
-    print("=" * 50 + "\n")
-    
-    print(itinerary)
-    
-    output_file = save_itinerary_to_file(itinerary, user_input)
-    print(f"\nYour itinerary has been saved to: {output_file}")
-
-if __name__ == "__main__":
-    main()
+"""
+# travel.py - AI Agent System for Travel Planning
+# ----------------------------------------------
+#
+# This module implements a sophisticated multi-agent system for travel planning
+# with a specific focus on Indian travel destinations and experiences.
+#
+# ARCHITECTURE:
+# The system uses a collection of specialized AI agents, each responsible for
+# a different aspect of travel planning:
+#
+# 1. Destination Research Agent - Researches destinations based on user preferences
+# 2. Accommodation Agent - Suggests suitable accommodations meeting budget and preference constraints
+# 3. Transportation Agent - Plans optimal transportation between locations
+# 4. Activities Agent - Curates personalized activities based on interests
+# 5. Dining Agent - Recommends dining experiences showcasing local cuisine
+# 6. Itinerary Agent - Compiles all recommendations into a cohesive day-by-day plan
+# 7. Chatbot Agent - Provides conversational responses to travel queries
+#
+# Each agent is powered by Google's Generative AI (Gemini) and is specialized through
+# careful system prompting that defines its role, goals, and expected outputs.
+#
+# WORKFLOW:
+# 1. User submits travel preferences
+# 2. Each agent processes the information in sequence
+# 3. Final itinerary is compiled from all agent outputs
+# 4. Results are presented to the user as a complete travel plan
+#
+# PRIMARY FUNCTIONS:
+# - run_task(): Core function to execute a specific agent task
+# - generate_travel_itinerary(): Orchestrates the full planning process
+# - save_itinerary_to_file(): Saves the generated itinerary for the user
+#
+# Created by TechMatrix Solvers for IIITDMJ HackByte3.0
+"""
+
+import os
+import json
+import logging
+from datetime import datetime, timedelta
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.schema import SystemMessage, HumanMessage
+
+# Setup logging configuration
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
+# -------------------------------------------------------------------------------
+# Agent and Task Classes with Type Hints and Docstrings
+# -------------------------------------------------------------------------------
+class Agent:
+    def __init__(self, role: str, goal: str, backstory: str, personality: str = "", llm=None) -> None:
+        """
+        Initialize an Agent with role, goal, backstory, personality, and assigned LLM.
+        """
+        self.role = role
+        self.goal = goal
+        self.backstory = backstory
+        self.personality = personality
+        self.tools = []  # Initialize with empty list for future tool integrations
+        self.llm = llm
+
+class Task:
+    def __init__(self, description: str, agent: Agent, expected_output: str, context=None) -> None:
+        """
+        Initialize a Task with its description, the responsible agent, expected output, and optional context.
+        """
+        self.description = description
+        self.agent = agent
+        self.expected_output = expected_output
+        self.context = context or []
+
+# -------------------------------------------------------------------------------
+# API Key Management
+# -------------------------------------------------------------------------------
+def get_api_keys():
+    """Get all available Gemini API keys from environment variables.
+
+    Returns:
+        list: List of API keys found in environment variables.
+    """
+    api_keys = []
+
+    # Check for GEMINI_API_KEY_1 and GEMINI_API_KEY_2
+    key1 = os.getenv("GEMINI_API_KEY_1")
+    key2 = os.getenv("GEMINI_API_KEY_2")
+
+    if key1:
+        api_keys.append(key1)
+        logging.info("Found GEMINI_API_KEY_1")
+
+    if key2:
+        api_keys.append(key2)
+        logging.info("Found GEMINI_API_KEY_2")
+
+    # Fall back to GEMINI_API_KEY if no numbered keys found
+    if not api_keys:
+        fallback_key = os.getenv("GEMINI_API_KEY")
+        if fallback_key:
+            api_keys.append(fallback_key)
+            logging.info("Using fallback GEMINI_API_KEY")
+
+    return api_keys
+
+# -------------------------------------------------------------------------------
+# Initialize LLM
+# -------------------------------------------------------------------------------
+def initialize_llm(api_key=None):
+    """Initialize the LLM with the provided API key or from environment variables.
+    
+    Args:
+        api_key (str, optional): API key for Google Generative AI. 
+            If None, will try to get from environment variables.
+            
+    Returns:
+        ChatGoogleGenerativeAI or None: Initialized LLM instance or None if initialization failed.
+    """
+    # First try the provided API key
+    if api_key:
+        google_api_key = api_key
+    else:
+        # Fall back to environment variable
+        google_api_key = os.getenv("GEMINI_API_KEY")
+    
+    if not google_api_key:
+        logging.warning("GEMINI_API_KEY is not set. AI functionality will be limited.")
+        return None
+    
+    # Basic API key format validation
+    if not google_api_key.startswith("AI"):
+        logging.warning("API key format appears incorrect. Should start with 'AI'.")
+    
+    try:
+        # Attempt to initialize the LLM
+        llm_instance = ChatGoogleGenerativeAI(
+            model="gemini-2.5-flash",
+            google_api_key=google_api_key,
+            temperature=0.7,
+            top_p=0.95,
+            top_k=40,
+            max_output_tokens=8192
+        )
+        logging.info("LLM initialized successfully.")
+        return llm_instance
+    except Exception as e:
+        logging.error(f"Error initializing LLM: {e}")
+        # More detailed error message for common issues
+        if "403" in str(e) or "401" in str(e):
+            logging.error("Authentication error: Your API key may be invalid or expired.")
+        elif "429" in str(e):
+            logging.error("Rate limit exceeded: Too many requests to the API.")
+        elif "timeout" in str(e).lower():
+            logging.error("Request timed out: Network issue or service unavailability.")
+        return None
+
+# Initialize with environment variable for now
+llm = initialize_llm()
+
+# -------------------------------------------------------------------------------
+# Define Travel Agents
+# -------------------------------------------------------------------------------
+destination_research_agent = Agent(
+    role="Destination Research Agent",
+    goal=(
+        "Research and provide comprehensive information about the destination including popular attractions, "
+        "local culture, weather patterns, best times to visit, and local transportation options with special focus on Indian context."
+    ),
+    backstory=(
+        "An experienced travel researcher with extensive knowledge of Indian destinations. "
+        "I specialize in uncovering both popular attractions and hidden gems that match travelers' interests."
+    ),
+    personality="Curious, detail-oriented, and knowledgeable about Indian cultures and travel trends.",
+    llm=llm,
+)
+
+accommodation_agent = Agent(
+    role="Accommodation Agent",
+    goal="Find and recommend suitable accommodations based on the traveler's preferences, budget, and location requirements with focus on Indian hospitality options.",
+    backstory="A hospitality expert who understands different types of accommodations in India and can match travelers with their ideal places to stay.",
+    personality="Attentive, resourceful, and focused on comfort and value.",
+    llm=llm,
+)
+
+transportation_agent = Agent(
+    role="Transportation Agent",
+    goal="Plan efficient transportation between the origin, destination, and all points of interest in the itinerary using Indian transportation networks.",
+    backstory="A logistics specialist with knowledge of India's transportation systems, from flights to local transit options including trains, buses, and auto-rickshaws.",
+    personality="Efficient, practical, and detail-oriented.",
+    llm=llm,
+)
+
+activities_agent = Agent(
+    role="Activities & Attractions Agent",
+    goal="Curate personalized activities and attractions that align with the traveler's interests, preferences, and time constraints in Indian destinations.",
+    backstory="An enthusiastic explorer who has experienced diverse activities across India and knows how to match experiences to individual preferences.",
+    personality="Enthusiastic, creative, and personable.",
+    llm=llm,
+)
+
+dining_agent = Agent(
+    role="Dining & Culinary Agent",
+    goal="Recommend dining experiences that showcase local Indian cuisine while accommodating dietary preferences and budget considerations.",
+    backstory="A culinary expert with knowledge of India's diverse food scenes and an appreciation for authentic local dining experiences.",
+    personality="Passionate about food, culturally aware, and attentive to preferences.",
+    llm=llm,
+)
+
+itinerary_agent = Agent(
+    role="Itinerary Integration Agent",
+    goal="Compile all recommendations into a cohesive, day-by-day itinerary that optimizes time, minimizes travel fatigue, and maximizes enjoyment for travel in India.",
+    backstory="A master travel planner who understands how to balance activities, rest, and logistics to create the perfect Indian travel experience.",
+    personality="Organized, balanced, and practical.",
+    llm=llm,
+)
+
+# -------------------------------------------------------------------------------
+# Define Chatbot Agent and Task for Interactive Conversation
+# -------------------------------------------------------------------------------
+chatbot_agent = Agent(
+    role="Chatbot Agent",
+    goal="Engage in interactive conversation to answer travel-related queries about India.",
+    backstory="A conversational AI assistant who provides instant, accurate travel information and recommendations for Indian destinations.",
+    personality="Friendly, conversational, and knowledgeable about travel in India.",
+    llm=llm,
+)
+
+chatbot_task = Task(
+    description="Provide a conversational and detailed response to travel-related queries about Indian destinations.",
+    agent=chatbot_agent,
+    expected_output="A friendly, helpful response to the user's query about travel in India."
+)
+
+# -------------------------------------------------------------------------------
+# Define Other Travel Tasks
+# -------------------------------------------------------------------------------
+destination_research_task = Task(
+    description="""Research {destination} thoroughly, considering the traveler's interests in {preferences}.
+    
+Efficient research parameters:
+- Prioritize research in these critical categories:
+    * Top attractions that match specific {preferences} (not generic lists)
+    * Local transportation systems with cost-efficiency analysis
+    * Neighborhood breakdown with accommodation recommendations by budget tier
+    * Seasonal considerations for the specific travel dates
+    * Safety assessment with specific areas to embrace or avoid
+    * Cultural norms that impact visitor experience (dress codes, tipping, etiquette)
+    
+- Apply efficiency filters:
+    * Focus exclusively on verified information from official tourism boards, recent travel guides, and reliable local sources
+    * Analyze recent visitor reviews (< 6 months old) to identify changing conditions
+    * Evaluate price-to-experience value for attractions instead of just popularity
+    * Identify logistical clusters where multiple interests can be satisfied efficiently
+    * Research off-peak times for popular attractions to minimize waiting
+    * Evaluate digital tools (apps, passes, reservation systems) that streamline the visit
+    
+- Create practical knowledge matrices:
+    * Transportation method comparison (cost vs. time vs. convenience)
+    * Weather impact on specific activities
+    * Budget allocation recommendations based on preference priorities
+    * Time-saving opportunity identification""",
+    agent=destination_research_agent,
+    expected_output="""Targeted destination brief containing:
+1. Executive summary highlighting the 5 most relevant aspects based on {preferences}
+2. Neighborhood analysis with accommodation recommendations mapped to specific interests
+3. Transportation efficiency guide with cost/convenience matrix
+4. Cultural briefing focusing only on need-to-know information that impacts daily activities
+5. Seasonal advantages and challenges specific to travel dates
+6. Digital resource toolkit (essential apps, websites, reservation systems)
+7. Budget optimization strategies with price ranges for key experiences
+8. Safety and health quick-reference including emergency contacts
+9. Logistics efficiency map showing optimal activity clustering
+10. Local insider advantage recommendations that save time or money
+
+Format should prioritize scannable information with bullet points, comparison tables, and decision matrices rather than lengthy prose."""
+)
+
+accommodation_task = Task(
+    description="Find suitable accommodations in {destination} based on a {budget} budget and preferences for {preferences}. Focus on Indian accommodations including heritage hotels, homestays, and modern options.",
+    agent=accommodation_agent,
+    expected_output="List of recommended accommodations with details on location, amenities, price range, and availability."
+)
+
+transportation_task = Task(
+    description="Plan transportation from {origin} to {destination} and local transportation options during the stay. Include Indian railways, local buses, metro systems, and ride-hailing services where available.",
+    agent=transportation_agent,
+    expected_output="Transportation plan including flights/routes to the destination and recommendations for getting around locally."
+)
+
+activities_task = Task(
+    description="""Suggest activities and attractions in {destination} that align with interests in {preferences}.
+    
+Detailed requirements:
+- Categorize activities into: Cultural Experiences, Outdoor Adventures, Culinary Experiences, 
+  Entertainment & Nightlife, Family-Friendly Activities, and Local Hidden Gems
+- For each activity, include:
+    * Detailed description with historical/cultural context where relevant
+    * Precise location with neighborhood information
+    * Operating hours with seasonal variations noted
+    * Pricing information with different ticket options/packages
+    * Accessibility considerations for travelers with mobility limitations
+    * Recommended duration for the activity (minimum and ideal time)
+    * Best time of day/week/year to visit
+    * Crowd levels by season
+    * Photography opportunities and restrictions
+    * Required reservations or booking windows
+- Include a mix of iconic must-see attractions and off-the-beaten-path experiences
+- Consider weather patterns in {destination} during travel period
+- Analyze the {preferences} to match specific personality types and interest levels
+- Include at least 2-3 rainy day alternatives for outdoor activities
+- Provide local transportation options to reach each attraction
+- Note authentic local experiences that provide cultural immersion
+- Flag any activities requiring special equipment, permits, or physical fitness levels""",
+    agent=activities_agent,
+    expected_output="""Comprehensive curated list of activities and attractions with:
+1. Clear categorization by type (cultural, outdoor, culinary, entertainment, family-friendly, hidden gems)
+2. Essential details for planning (location, timing, cost, requirements)
+3. Special recommendations based on {preferences} with personalized suggestions
+4. Weather contingency options
+5. Insider tips for maximizing experience value
+6. Logistical guidance for efficient navigation between attractions"""
+)
+
+dining_task = Task(
+    description="""Recommend dining options in {destination} that showcase local cuisine while accommodating {preferences} with special attention to authentic Indian culinary experiences.
+    
+For each recommended dining establishment, provide:
+- Name, location, and cuisine type
+- Signature dishes and culinary specialties
+- Price range and value assessment
+- Ambiance description and dress code if applicable
+- Operating hours and reservation policies
+- Local popularity vs. tourist popularity
+- Special dietary accommodations (vegetarian, vegan, gluten-free, etc.)
+- Authentic cultural dining experiences that provide insight into local life""",
+    agent=dining_agent,
+    expected_output="""Curated dining guide for {destination} with:
+1. Top restaurant recommendations categorized by meal type, cuisine, and price point
+2. Must-try local dishes specific to the region
+3. Unique dining experiences that reflect local culture
+4. Budget-friendly options with exceptional value
+5. Strategic meal timing to complement activity schedule
+6. Advance reservation requirements where applicable
+7. Special dietary consideration options
+8. Hidden gems frequented by locals"""
+)
+
+itinerary_task = Task(
+    description="""Create a day-by-day itinerary for a {duration}-day trip to {destination} based on all previous research and recommendations. Focus on authentic experiences that reflect true Indian culture and heritage.
+    
+The itinerary should:
+- Balance activities with rest and travel time
+- Group attractions by geographic proximity to minimize transit time
+- Consider opening hours, best times to visit, and seasonal factors
+- Include meal recommendations that complement the day's activities
+- Provide contingency options for weather or unexpected closures
+- Balance must-see attractions with authentic local experiences
+- Account for {preferences} in prioritizing experiences
+- Include precise timing, transportation methods, and logistical details
+- Optimize for the traveler's interests while maintaining a realistic pace
+- Incorporate free time for exploration and spontaneity""",
+    agent=itinerary_agent,
+    expected_output="""Complete day-by-day itinerary containing:
+1. Daily schedule with timing, locations, and activities
+2. Transportation details between all points
+3. Meal recommendations with timing and cuisine type
+4. Activity duration estimates with buffer time included
+5. Alternative options for flexibility
+6. Booking/reservation requirements with deadlines
+7. Cost estimates for budgeting purposes
+8. Local cultural insights for each experience
+9. Strategic planning notes (best photo spots, crowd avoidance, etc.)
+10. Evening entertainment options
+
+Format should be scannable with clear headings, timing, and logistical details for easy reference during travel."""
+)
+
+# -------------------------------------------------------------------------------
+# Helper Function to Run a Task with Full Agent & Task Information
+# -------------------------------------------------------------------------------
+def run_task(task: Task, input_text: str, api_key=None) -> str:
+    """
+    Run an agent task with the given input text and API key.
+    Automatically switches between multiple API keys if rate limits are hit.
+
+    Args:
+        task: The Task to run
+        input_text: User input text
+        api_key: Optional Gemini API key to use
+
+    Returns:
+        str: The generated response or error message
+    """
+    # Get all available API keys
+    available_api_keys = get_api_keys()
+
+    # If a specific API key is provided, use it first
+    if api_key:
+        api_keys_to_try = [api_key] + [k for k in available_api_keys if k != api_key]
+    else:
+        api_keys_to_try = available_api_keys
+
+    if not api_keys_to_try:
+        logging.error("No valid API key provided")
+        return "⚠️ API Key Error: Please set GEMINI_API_KEY_1 or GEMINI_API_KEY_2 in environment variables to access AI features."
+
+    # Prepare the system prompt
+    system_prompt = f"""
+    # Role: {task.agent.role}
+    # Goal: {task.agent.goal}
+    # Backstory: {task.agent.backstory}
+
+    Instructions for output:
+    {task.expected_output}
+    """
+
+    messages = [
+        SystemMessage(content=system_prompt),
+        HumanMessage(content=input_text)
+    ]
+
+    # Try each API key until one succeeds
+    last_error = None
+    for idx, current_api_key in enumerate(api_keys_to_try):
+        try:
+            # Initialize LLM with current API key
+            current_llm = initialize_llm(current_api_key)
+            if not current_llm:
+                continue
+
+            # Update the agent's LLM
+            task.agent.llm = current_llm
+
+            # Start tracking time for possible timeout issues
+            start_time = datetime.now()
+
+            # Make the API call with timeout handling
+            response = task.agent.llm.invoke(messages).content
+
+            # Calculate response time for logging
+            response_time = (datetime.now() - start_time).total_seconds()
+            logging.info(f"Task '{task.description[:30]}...' completed in {response_time:.2f} seconds using API key #{idx+1}")
+
+            return response
+
+        except Exception as e:
+            error_msg = str(e)
+            last_error = error_msg
+
+            # Check if it's a rate limit error
+            if "429" in error_msg or "rate limit" in error_msg.lower() or "quota" in error_msg.lower():
+                logging.warning(f"Rate limit hit on API key #{idx+1}. Trying next API key...")
+
+                # If this is not the last API key, try the next one
+                if idx < len(api_keys_to_try) - 1:
+                    logging.info(f"Switching to API key #{idx+2}")
+                    continue
+                else:
+                    logging.error("All API keys have hit rate limits")
+                    return "⚠️ Rate limit exceeded on all API keys. Please try again in a few minutes."
+
+            # For non-rate-limit errors, log and try next key or return error
+            logging.error(f"Error running task with API key #{idx+1}: {error_msg}")
+
+            # For authentication errors, try next key
+            if "403" in error_msg or "401" in error_msg or "authentication" in error_msg.lower():
+                if idx < len(api_keys_to_try) - 1:
+                    logging.info(f"Authentication failed, trying API key #{idx+2}")
+                    continue
+                else:
+                    return "⚠️ API Key Error: All API keys appear to be invalid or expired. Please update them in environment variables."
+
+            # For other errors, try next key or return error
+            if idx < len(api_keys_to_try) - 1:
+                continue
+
+    # If we've exhausted all API keys, return a user-friendly error
+    if last_error:
+        if "timeout" in last_error.lower():
+            return "⚠️ Request timed out. The service might be experiencing high traffic. Please try again later."
+        else:
+            return f"⚠️ Error processing your request with all available API keys. Please try again or check your API key settings."
+
+    return "⚠️ Unable to complete the request. Please check your API keys."
+
+# -------------------------------------------------------------------------------
+# User Input Functions
+# -------------------------------------------------------------------------------
+def get_user_input() -> dict:
+    """
+    Collects user input for travel itinerary generation.
+    """
+    print("\n=== Travel Itinerary Generator ===\n")
+    origin = input("Enter your origin: ")
+    destination = input("Enter your destination: ")
+    duration = input("Enter duration in days: ")
+    
+    current_date = datetime.now()
+    start_date = current_date + timedelta(days=7)
+    end_date = start_date + timedelta(days=int(duration))
+    
+    preferences = input("Enter your preferences (comma separated): ")
+    budget = input("Enter your budget: ")
+    
+    return {
+        "origin": origin,
+        "destination": destination,
+        "duration": duration,
+        "start_date": start_date.strftime("%Y-%m-%d"),
+        "end_date": end_date.strftime("%Y-%m-%d"),
+        "preferences": preferences,
+        "budget": budget
+    }
+
+# -------------------------------------------------------------------------------
+# Main Function to Generate Travel Itinerary
+# -------------------------------------------------------------------------------
+def generate_travel_itinerary(user_input: dict) -> str:
+    """
+    Generates a personalized travel itinerary by sequentially running defined tasks.
+    """
+    print("\nGenerating your personalized travel itinerary...\n")
+    
+    # Create input context using f-string formatting
+    input_context = (
+        f"Travel Request Details:\n"
+        f"Origin: {user_input['origin']}\n"
+        f"Destination: {user_input['destination']}\n"
+        f"Duration: {user_input['duration']} days\n"
+        f"Budget Level: {user_input['budget']}\n"
+        f"Preferences/Interests: {user_input['preferences']}\n"
+        f"Special Requirements: {user_input['special_requirements']}\n"
+    )
+    
+    # Step 1: Destination Research
+    print("Researching your destination...")
+    destination_info = run_task(destination_research_task, input_context)
+    print("✓ Destination research completed")
+    
+    # Step 2: Accommodation Recommendations
+    print("Finding ideal accommodations...")
+    accommodation_info = run_task(accommodation_task, input_context)
+    print("✓ Accommodation recommendations completed")
+    
+    # Step 3: Transportation Planning
+    print("Planning transportation...")
+    transportation_info = run_task(transportation_task, input_context)
+    print("✓ Transportation planning completed")
+    
+    # Step 4: Activities & Attractions
+    print("Curating activities and attractions...")
+    activities_info = run_task(activities_task, input_context)
+    print("✓ Activities and attractions curated")
+    
+    # Step 5: Dining Recommendations
+    print("Finding dining experiences...")
+    dining_info = run_task(dining_task, input_context)
+    print("✓ Dining recommendations completed")
+    
+    # Step 6: Create Day-by-Day Itinerary
+    print("Creating your day-by-day itinerary...")
+    combined_info = (
+        input_context + "\n"
+        "Destination Information:\n" + destination_info + "\n"
+        "Accommodation Options:\n" + accommodation_info + "\n"
+        "Transportation Plan:\n" + transportation_info + "\n"
+        "Recommended Activities:\n" + activities_info + "\n"
+        "Dining Recommendations:\n" + dining_info + "\n"
+    )
+    itinerary = run_task(itinerary_task, combined_info)
+    print("✓ Itinerary creation completed")
+    print("✓ Itinerary generation completed")
+    
+    return itinerary
+
+# -------------------------------------------------------------------------------
+# Save Itinerary to File
+# -------------------------------------------------------------------------------
+def save_itinerary_to_file(itinerary: str, user_input: dict, output_dir: str = None) -> str:
+    """
+    Saves the generated itinerary to a text file and returns the filepath.
+    """
+    date_str = datetime.now().strftime("%Y-%m-%d")
+    filename = f"India_Travel_Itinerary_{user_input['destination']}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt"
+    
+    if output_dir:
+        if not os.path.exists(output_dir):
+            try:
+                os.makedirs(output_dir)
+                logging.info(f"Created output directory: {output_dir}")
+            except Exception as e:
+                logging.error(f"Error creating directory {output_dir}: {e}")
+                return ""
+        filepath = os.path.join(output_dir, filename)
+    else:
+        filepath = filename
+    
+    try:
+        with open(filepath, "w", encoding="utf-8") as f:
+            f.write(itinerary)
+        logging.info(f"Your itinerary has been saved as: {filepath}")
+        return filepath
+    except Exception as e:
+        logging.error(f"Error saving itinerary: {e}")
+        return ""
+
+# -------------------------------------------------------------------------------
+# Main Function
+# -------------------------------------------------------------------------------
+def main() -> None:
+    """
+    Main entry point for the travel itinerary generator application.
+    """
+    print("Welcome to the India Travel Planner! Let's create your perfect itinerary.")
+    user_input = get_user_input()
+    
+    print("\nGenerating your personalized travel itinerary...\n")
+    itinerary = generate_travel_itinerary(user_input)
+    
+    print("\n" + "=" * 50)
+    print("Your travel itinerary is ready!")
+    print("=" * 50 + "\n")
+    
+    print(itinerary)
+    
+    output_file = save_itinerary_to_file(itinerary, user_input)
+    print(f"\nYour itinerary has been saved to: {output_file}")
+
+if __name__ == "__main__":
+    main()
+