Adding options for specifying specific interests and cuisine preferences.

app.py

@@ -1,52 +1,81 @@
 #!/usr/bin/env python
-import sys
+"""
+Travel planner based on Agentic AI workflow.
+
+This module deploys a portal which can customize a day to day travel itinerary
+for a person using multiple specialized AI crews.
+
+Implemented using Gradio and Crew AI
+A deployment is available at https://huggingface.co/spaces/sambanovasystems/trip-planner
+"""
+
 import json
+import logging
+
 import gradio as gr
 import plotly.graph_objects as go
-import logging
-from crew import TravelCrew, AddressSummaryCrew
-
-def filter_map(text_list, lat, lon):
-    fig = go.Figure(go.Scattermapbox(
-            lat=lat,
-            lon=lon,
-            mode='markers',
-            marker=go.scattermapbox.Marker(
-                size=11
-            ),
-            hovertext=text_list
-        ))
+from crew import AddressSummaryCrew, TravelCrew
+from typing import List
+
+
+def filter_map(text_list: List[str], lat: List[str], lon: List[str]) -> go.Figure:
+    """
+    Create a Map showing the points specified in the inputs.
+
+    Args:
+        text_list: List of the description of all locations that will be shown on the map
+        lat:       List of latitude coordinates of the locations
+        lon:       List of longitude coordinates of the locations
+
+    Returns:
+        Figure: Map with the points specified in the inputs
+    """
+
+    fig = go.Figure(
+        go.Scattermapbox(lat=lat, lon=lon, mode='markers', marker=go.scattermapbox.Marker(size=11), hovertext=text_list)
+    )
 
     fig.update_layout(
-        mapbox_style="open-street-map",
+        mapbox_style='open-street-map',
         hovermode='closest',
-        mapbox=dict(
-            bearing=0,
-            center=go.layout.mapbox.Center(
-                lat=lat[1],
-                lon=lon[1]
-            ),
-            pitch=0,
-            zoom=10
-        ),
+        mapbox=dict(bearing=0, center=go.layout.mapbox.Center(lat=lat[1], lon=lon[1]), pitch=0, zoom=10),
     )
     return fig
 
-def run(origin, destination, age, trip_duration, children, budget):
-    # Replace with your inputs, it will automatically interpolate any tasks and agents information
-    logger.info(f"Origin: {origin}, Destination: {destination}, Age: {age}, Duration: {trip_duration}, Children: {children}, Daily Budget: {budget}")
+
+def run(origin: str, destination: str, age: int, trip_duration: int,
+        interests: List, cuisine_preferences: List,  children: bool, budget: int) -> (str, go.Figure):
+    """
+    Run the specfied query using Crew AI agents
+
+    Args:
+        origin: Origin city of the traveller
+        destination: Destination that traveller is going to
+        age: Age profile of traveller
+        interests: Specific interests of the traveller
+        cuisine_preferences: Specific cuisine preferences of the traveller
+        children: Whether traveller has children travelling with them
+        budget: Total budget of traveller in US Dollars
+
+    Returns:
+        Returns a tuple containing the itinerary and map
+    """
+    logger.info(
+        f'Origin: {origin}, Destination: {destination}, Age: {age}, Duration: {trip_duration},'
+        f' Interests: {interests}, Cuisines: {cuisine_preferences}, Children: {children}, Daily Budget: {budget}'
+    )
     inputs = {
         'origin': origin,
         'destination': destination,
         'age': age,
         'trip_duration': trip_duration,
+        'interests': interests,
+        'cuisine_preferences': cuisine_preferences,
         'children': children,
-        'budget': budget
+        'budget': budget,
     }
     result = TravelCrew().crew().kickoff(inputs=inputs)
-    inputs_for_address = {
-        'text': str(result)
-    }
+    inputs_for_address = {'text': str(result)}
 
     addresses = AddressSummaryCrew().crew().kickoff(inputs=inputs_for_address)
     json_addresses = None
@@ -60,27 +89,39 @@ def run(origin, destination, age, trip_duration, children, budget):
             try:
                 json_addresses = json.loads(addresses.raw[8:-4])
             except json.JSONDecodeError as e:
-                logger.error("Error loading Crew Output for addresses")
-                logger.info(addresses.raw)
-                return (result, None)
-    fig = filter_map(json_addresses["name"], json_addresses["lat"], json_addresses["lon"])
+                # Try with different format of result data generated with ``` and ending with ```
+                try:
+                    json_addresses = json.loads(addresses.raw[4:-4])
+                except json.JSONDecodeError as e:
+                    logger.error('Error loading Crew Output for addresses')
+                    logger.info(addresses.raw)
+                    return (result, None)
+    fig = filter_map(json_addresses['name'], json_addresses['lat'], json_addresses['lon'])
     return (result, fig)
 
 logger = logging.getLogger()
 logger.setLevel(logging.INFO)
 
 demo = gr.Interface(
-    title="Plan your itinerary with the help of AI",
-    description="Use this app to create a detailed itinerary on how to explore a new place. Itinerary is customized to your taste. Powered by Sambanova Cloud.",
+    title='Plan your itinerary with the help of AI',
+    description='Use this app to create a detailed itinerary on how to explore a new place.'
+                ' Itinerary is customized to your taste. Powered by Sambanova Cloud.',
     fn=run,
-    inputs=[gr.Textbox(label="Origin"), gr.Textbox(label="Destination"),
-            gr.Slider(label="Your age?", value=30, minimum=15, maximum=90, step=5),
-            gr.Slider(label="How many days are you travelling?", value=5, minimum=1, maximum=14, step=1),
-            gr.Checkbox(label="Check if children are travelling with you"),
-            gr.Slider(label="Total budget of trip in USD", show_label=True, value=1000, minimum=500, maximum=10000, step=500)],
+    inputs=[
+        gr.Textbox(label='Where are you travelling from?'),
+        gr.Textbox(label='Where are you going?'),
+        gr.Slider(label='Your age?', value=30, minimum=15, maximum=90, step=5),
+        gr.Slider(label='How many days are you travelling?', value=5, minimum=1, maximum=14, step=1),
+        gr.CheckboxGroup(["Museums", "Shopping", "Entertainment", "Nightlife", "Outdoor Adventures"], label="Checkbox your specific interests."),
+        gr.CheckboxGroup(["Ethnic", "American", "Italian", "Mexican", "Chinese", "Japanese", "Indian", "Thai", "French", "Vietnamese", "Vegan"], label="Checkbox your cuisine preferences."),
+        gr.Checkbox(label='Check if children are travelling with you'),
+        gr.Slider(
+            label='Total budget of trip in USD', show_label=True, value=1000, minimum=500, maximum=10000, step=500
+        ),
+    ],
     outputs=[
-        gr.Textbox(label="Complete Itinerary", show_copy_button=True, autoscroll=False),
-        gr.Plot(label="Venues on a Map. Please verify with a Navigation System before traveling.")
-    ]
+        gr.Textbox(label='Complete Personalized Itinerary of your Trip', show_copy_button=True, autoscroll=False),
+        gr.Plot(label='Venues on a Map. Please verify with a Navigation System before traveling.'),
+    ],
 )
 demo.launch()

config/agents.yaml

@@ -14,22 +14,14 @@ restaurant_scout:
   backstory: >
     As a food lover, you know the best spots in town for a delightful culinary experience. You also have a knack for finding picturesque and entertaining locations.
 
-museum_scout:
+interest_scout:
   role: >
-    Museum Scout
+    Interest Scout
   goal: >
-    Find well-known as well as off-the-beat museums matching the age profile. Also pay particular attention to if there are children. If children are accompanying, pick up museums which might be more interesting to children.
+    Find well-known as well as off-the-beat activities tailored to the list of interests of the traveller and matching the age profile. Also pay particular attention to if there are children. If children are accompanying, pick up activities which might be more interesting to children.
   backstory: >
     You know the best museums in town for different age groups. Older people may like quieter museums such as art museums. Young children like museums where they can get to touch, feel, and play.
 
-shopping_scout:
-  role: >
-    Shopping Scout
-  goal: >
-    Find good areas for shopping. This can include local markets where visitors can buy souvenir, glamorous shopping mall, and flea markets where visiors can buy used goods. If children are accompanying, pick shopping options with suitable amenities and entertainment options for children.
-  backstory: >
-    You are an expert of the city and good at finding shopping options where high quality goods reflecting the local values and culture can be bought at a discount
-
 itinerary_compiler:
   role: >
     Itinerary Compiler

config/tasks.yaml

@@ -12,6 +12,10 @@ personalized_activity_planning_task:
 
     - Approximate age of the adult travelers: {age}
 
+    - List of interests that the traveller may have: {interests}
+
+    - List of cuisine preferences that the traveller may have: {cuisine_preferences}
+
     - Are children accompanying the trip: {children}
 
     - how long is the trip: {trip_duration}
@@ -25,8 +29,9 @@ personalized_activity_planning_task:
 
 restaurant_scenic_location_scout_task:
   description: >
-    Find highly-rated restaurants and dining experiences at {destination}.
-    Recommend scenic locations and fun activities that align with the traveler's preferences.
+    Find highly-rated restaurants and dining experiences for the specific cuisine interests {cuisine_preferences} at {destination}.
+    Pay particular attention to the cuisines. If Vegan is on the list, then recommend restaurants which accomodate vegan diet.
+    Recommend scenic locations that align with the traveler's preferences and activities.
     Use internet search tools, restaurant review sites, and travel guides.
     Make sure to find a variety of options to suit different tastes and budgets, and ratings for them.
     Extract the address of he restaurant so that the same can be displayed to the user.
@@ -39,6 +44,8 @@ restaurant_scenic_location_scout_task:
 
     - age of the traveler: {age}
 
+    - List of cuisine preferences that the traveller may have: {cuisine_preferences}
+
     - how long is the trip: {trip_duration}
 
     - budget for entire trip in dollars: {budget}
@@ -46,12 +53,13 @@ restaurant_scenic_location_scout_task:
     A list of recommended restaurants for each day of the trip.
     Each entry should include the name, location including a detailed address, type of cuisine or activity, and a brief description and ratings.
 
-museum_scout_task:
+interest_scout_task:
   description: >
-    Find popular and off-the-beat museums at {destination}. Consider the {age} of the traveller while recommending museums. Check if children are accompanying in the trip. {children}
-    If children are accompanying, find museums that might be more appealing to children.
-    Use internet search tools, museum review sites, feedback from users, and travel guides.
-    Extract the address of the museum so that the same can be displayed to the user.
+    Run specifically for the interests of the traveller provided {interests}. As an example if the traveller has interest in museums, plan activities related to museums at the destination.
+    Check if children are accompanying in the trip. {children}
+    If children are accompanying, find activities that might be more appealing to children.
+    Use internet search tools, review sites, feedback from users, and travel guides.
+    Extract the address of the activity so that the same can be displayed to the user.
 
     Traveler's information:
 
@@ -61,29 +69,7 @@ museum_scout_task:
 
     - age of the traveler: {age}
 
-    - Are children accompanying the trip: {children}
-
-    - how long is the trip: {trip_duration}
-
-    - budget for entire trip in dollars: {budget}
-  expected_output: >
-    A list of recommended museums. There need not be a museum for each day of the trip.
-    Each entry should include the name, location including a detailed address, type of cuisine or activity, and a brief description and ratings.
-
-shopping_scout_task:
-  description: >
-    Find popular shopping  at {destination}. Check if children are accompanying in the trip. {children}
-    If children are accompanying, find shopping areas that might be more appealing to children.
-    Use internet search tools, feedback from users, and travel guides to make the decision.
-    Extract the address of the shopping area so that the same can be displayed to the user.
-
-    Traveler's information:
-
-    - origin: {origin}
-
-    - destination: {destination}
-
-    - age of the traveler: {age}
+    - List of interests that the traveller may have: {interests}
 
     - Are children accompanying the trip: {children}
 
@@ -91,13 +77,14 @@ shopping_scout_task:
 
     - budget for entire trip in dollars: {budget}
   expected_output: >
-    A list of recommended shopping areas for each day of the trip. Shopping options need not be provided for each day of the trip.
+    A list of recommended activities. There must be at least one activity for each day of the trip. Allocate appropriate time for the activity so that the traveller need not rush to the next activity.
     Each entry should include the name, location including a detailed address, type of cuisine or activity, and a brief description and ratings.
 
 
 itinerary_compilation_task:
   description: >
     Compile all researched information into a comprehensive day-by-day itinerary for the trip to {destination}.
+    Include information relevant to the traveler's interests. For example, if the traveller is not interested in museums, do not pack the itinerary with museums.
     Ensure the itinerary integrates hotel information and all planned activities and dining experiences.
     Make sure that you do not recommend the same restaurant for both lunch and dinner.
     Use text formatting and document creation tools to organize the information.

crew.py

@@ -1,37 +1,42 @@
-from crewai import Agent, Crew, Process, Task, LLM
-from crewai.project import CrewBase, agent, crew, task
-
-# Uncomment the following line to use an example of a custom tool
-# from surprise_travel.tools.custom_tool import MyCustomTool
-
-# Check our tools documentation for more information on how to use them
-from crewai_tools import SerperDevTool, ScrapeWebsiteTool
-#from pydantic import BaseModel, Field
-from typing import List, Optional
+"""Implementation based on the Crew AI workflow"""
 
-import os
+from crewai import LLM, Agent, Crew, Process, Task
+from crewai.project import CrewBase, agent, crew, task
+from crewai_tools import ScrapeWebsiteTool, SerperDevTool
 
 # Change the model which you want to use below.
-llm = LLM(model="sambanova/Meta-Llama-3.1-70B-Instruct")
+# Currently, we use the Llama 3.1 70B model because it seems the most versatile
+llm = LLM(model='sambanova/Meta-Llama-3.1-70B-Instruct')
 
 @CrewBase
-class TravelCrew():
-    """Travel crew"""
+class TravelCrew:
+    """Crew to do travel planning"""
+
     agents_config = 'config/agents.yaml'
     tasks_config = 'config/tasks.yaml'
 
     @agent
     def personalized_activity_planner(self) -> Agent:
+        """
+        An agent specialized to build an activity planner
+
+        Returns: The agent
+        """
         return Agent(
             config=self.agents_config['personalized_activity_planner'],
             llm=llm,
             max_iter=1,
-            tools=[SerperDevTool(), ScrapeWebsiteTool()], # Example of custom tool, loaded at the beginning of file
+            tools=[SerperDevTool(), ScrapeWebsiteTool()],  # Example of custom tool, loaded at the beginning of file
             allow_delegation=False,
         )
 
     @agent
     def restaurant_scout(self) -> Agent:
+        """
+        An agent specialized to scout for restaurants
+
+        Returns: The agent
+        """
         return Agent(
             config=self.agents_config['restaurant_scout'],
             llm=llm,
@@ -41,19 +46,14 @@ class TravelCrew():
         )
 
     @agent
-    def museum_scout(self) -> Agent:
-        return Agent(
-            config=self.agents_config['museum_scout'],
-            llm=llm,
-            max_iter=1,
-            tools=[SerperDevTool(), ScrapeWebsiteTool()],
-            allow_delegation=False,
-        )
+    def interest_scout(self) -> Agent:
+        """
+        An agent specialized to scout for specific interests
 
-    @agent
-    def shopping_scout(self) -> Agent:
+        Returns: The agent
+        """
         return Agent(
-            config=self.agents_config['shopping_scout'],
+            config=self.agents_config['interest_scout'],
             llm=llm,
             max_iter=1,
             tools=[SerperDevTool(), ScrapeWebsiteTool()],
@@ -62,6 +62,11 @@ class TravelCrew():
 
     @agent
     def itinerary_compiler(self) -> Agent:
+        """
+        An agent specialized at composing the entire itinirary
+
+        Returns: The agent
+        """
         return Agent(
             config=self.agents_config['itinerary_compiler'],
             llm=llm,
@@ -71,68 +76,81 @@ class TravelCrew():
 
     @task
     def personalized_activity_planning_task(self) -> Task:
+        """
+        A task that designs and plans for activities.
+
+        Returns: A task
+        """
         return Task(
             config=self.tasks_config['personalized_activity_planning_task'],
             llm=llm,
             max_iter=1,
-            agent=self.personalized_activity_planner()
+            agent=self.personalized_activity_planner(),
         )
 
     @task
-    def restaurant_scenic_location_scout_task(self) -> Task:
-        return Task(
-            config=self.tasks_config['restaurant_scenic_location_scout_task'],
-            llm=llm,
-            max_iter=1,
-            agent=self.restaurant_scout()
-        )
+    def interest_scout_task(self) -> Task:
+        """
+        A task that plans for specific interests of the traveller.
+
+        Returns: A task
+        """
+        return Task(config=self.tasks_config['interest_scout_task'], llm=llm, max_iter=1, agent=self.interest_scout())
 
-    @task
-    def museum_scout_task(self) -> Task:
-        return Task(
-            config=self.tasks_config['museum_scout_task'],
-            llm=llm,
-            max_iter=1,
-            agent=self.museum_scout()
-        )
 
     @task
-    def shopping_scout_task(self) -> Task:
+    def restaurant_scenic_location_scout_task(self) -> Task:
+        """
+        A task that picks restaurants.
+
+        Returns: A task
+        """
         return Task(
-            config=self.tasks_config['shopping_scout_task'],
+            config=self.tasks_config['restaurant_scenic_location_scout_task'],
             llm=llm,
             max_iter=1,
-            agent=self.shopping_scout()
+            agent=self.restaurant_scout(),
         )
 
     @task
     def itinerary_compilation_task(self) -> Task:
+        """
+        A task that plans for museums.
+
+        Returns: A task
+        """
         return Task(
-            config=self.tasks_config['itinerary_compilation_task'],
-            llm=llm,
-            max_iter=1,
-            agent=self.itinerary_compiler()
+            config=self.tasks_config['itinerary_compilation_task'], llm=llm, max_iter=1, agent=self.itinerary_compiler()
         )
 
     @crew
     def crew(self) -> Crew:
-        """Creates the Travel crew"""
+        """
+        Creates the Travel Planning crew
+
+        Returns: A crew
+        """
         return Crew(
-            agents=self.agents, # Automatically created by the @agent decorator
-            tasks=self.tasks, # Automatically created by the @task decorator
+            agents=self.agents,
+            tasks=self.tasks,
             process=Process.sequential,
-            # process=Process.hierarchical, # In case you want to use that instead https://docs.crewai.com/how-to/Hierarchical/
         )
 
 
 @CrewBase
-class AddressSummaryCrew():
+class AddressSummaryCrew:
     """Address Summary crew"""
+
     agents_config = 'config/address_agents.yaml'
     tasks_config = 'config/address_tasks.yaml'
 
     @agent
     def address_summarizer(self) -> Agent:
+        """
+        Creates an agent which can summarize addresses in a Json file
+
+        Returns: An agent
+        """
         return Agent(
             config=self.agents_config['address_summarizer'],
             llm=llm,
@@ -142,6 +160,11 @@ class AddressSummaryCrew():
 
     @task
     def address_compilation_task(self) -> Task:
+        """
+        Creates a task which can summarize addresses
+
+        Returns: A Task
+        """
         return Task(
             config=self.tasks_config['address_compilation_task'],
             llm=llm,
@@ -151,10 +174,14 @@ class AddressSummaryCrew():
 
     @crew
     def crew(self) -> Crew:
-        """Creates the AddressSummary crew"""
+        """
+        Creates the AddressSummary crew
+
+        Returns: A Crew
+        """
         crew = Crew(
-            agents=self.agents, # Automatically created by the @agent decorator
-            tasks=self.tasks, # Automatically created by the @task decorator
+            agents=self.agents,
+            tasks=self.tasks,
             process=Process.sequential,
         )
         return crew