Upload 6 files

agents.py

@@ -0,0 +1,188 @@
+import functools
+from langchain.agents import AgentExecutor, create_openai_tools_agent
+from langchain_core.messages import HumanMessage
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_openai import ChatOpenAI
+from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
+import tools
+from tools import tools
+import os
+import config
+
+
+os.environ["OPENAI_API_KEY"] = config.config("OPENAI_API_KEY")
+
+# initializing the GPT-4 Turbo model with no temperature variation
+llm = ChatOpenAI(temperature=0, model="gpt-4-turbo-preview")
+
+def create_agents(llm:ChatOpenAI, tools:list, system_prompt:str)->AgentExecutor:
+    # creating a chat prompt template
+    prompt = ChatPromptTemplate.from_messages([
+        ('system', system_prompt),
+        MessagesPlaceholder(variable_name='messages'),
+        MessagesPlaceholder(variable_name="agent_scratchpad")
+    ])
+
+    # creating an agent with specified tools and prompting template
+    agent = create_openai_tools_agent(llm, tools, prompt)
+    executor = AgentExecutor(agent=agent, tools=tools)
+    return executor
+
+# function to handle the agent invocation and return formatted state
+def agent_node(state, agent, name):
+    result = agent.invoke(state)
+    return {"messages": [HumanMessage(content=result["output"], name=name)]}
+
+# list of agents representing different coaching roles
+members = ["nutritionist", "workout_coach", "mental_health_coach","sleep_coach","hydration_coach",
+           "posture_and_ergonomics_coach","injury_prevention_and_recovery_coach"]
+
+# system prompt explaining the FIT.AI role and its tasks
+system_prompt = (
+    """
+    TASK:
+    You are "FIT.AI", an intelligent chatbot that answers questions about fitness and overall health. 
+    You also supervise and coordinate tasks among seven workers: {members}. 
+    Based on the user's request,  determine which worker should take the next action. 
+    Each worker is responsible for executing specific tasks and reporting back their findings and progress.
+    
+    Example session : 
+
+    User question : Hello, help me with a fitness and diet plan.
+    Thought : I should first ask the user their daily routine and then
+    search the web for the most optimal fitness and diet plan first.
+    Action : Search the web for optimal results.
+    Pause : You will take some time to think
+    You then output : Please provide your daily routine so as to tailor the plan accordingly.
+    """
+)
+
+# options for routing the next step in the flow
+options = ['FINISH'] + members
+
+# function definition for routing the tasks to agents
+function_def = {
+    "name": "route",
+    "description": "Select the next role.",
+    "parameters": {
+        "title": "routeSchema",
+        "type": "object",
+        "properties": {"next": {"title": "Next", "anyOf": [{"enum": options}]}},
+        "required": ["next"]
+    }
+}
+
+# creating the supervisor chain using the specified LLM and function definitions
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system_prompt),
+        MessagesPlaceholder(variable_name="messages"),
+        (
+            "system",
+            "Given the conversation above, who should act next?"
+            " Or should we FINISH? Select one of: {options}",
+        ),
+    ]).partial(options=str(options), members=", ".join(members))
+
+supervisor_chain = (
+    prompt
+    | llm.bind_functions(functions=[function_def], function_call="route")
+    | JsonOutputFunctionsParser()
+)
+
+# creating agents for each coach role with specified prompts and tools
+nutritionist_agent = create_agents(
+    llm,
+    tools,
+    """Your role is to act as a knowledgeable nutritionist. Provide practical dietary 
+    advice and create meal plans. Research the latest nutritional information and trends, 
+    and give personalized recommendations based on the user's needs and country of choice.
+    Utilize information from the workout coach to suggest a diet plan. Always mention any web/mobile applications for tracking 
+    calorie intake and identify potential food allergies. If no applications are found, 
+    provide useful tips. Respond in a friendly, informal tone."""
+)
+
+workout_coach_agent = create_agents(
+    llm,
+    tools,
+    """You are a workout coach. Based on the user's fitness goals and nutritionist's suggestions, 
+    create tailored workout plans. Provide exercise routines, tips for proper form, and motivation. 
+    Suggest home workout equipment along with online links to purchase them and useful fitness tracking applications or websites. 
+    Respond in a friendly, informal tone, offering positive affirmations and practical 
+    timelines for achieving goals."""
+)
+
+mental_health_coach_agent = create_agents(
+    llm,
+    tools,
+    """You are a mental health coach. Provide support and mindfulness strategies to improve 
+    mental well-being taking into account the user's dietary and workout plans. Research techniques 
+    and practices to help with mental health and offer insights into mental health disorders if queried.
+    Reccommend home tips based in the user's activity level. 
+    Suggest useful apps for maintaining mental stability. Respond in a friendly, informal tone."""
+)
+
+sleep_coach_agent = create_agents(
+    llm,
+    tools,
+    """You are a sleep coach. Provide tips for better sleep hygiene, suggest tools and techniques 
+    to improve sleep quality, and offer advice on optimizing sleep habits based on the 
+    user's daily routine and age . Mention any web or mobile applications for tracking sleep 
+    patterns and provide relaxation techniques. Respond in a friendly, informal tone."""
+)
+
+hydration_coach_agent = create_agents(
+    llm,
+    tools,
+    """You are a hydration coach. Help users maintain proper hydration levels by providing advice on water intake 
+    and the importance of staying hydrated. Suggest tools and techniques for tracking water consumption and offer 
+    tips for improving hydration habits based on the user's daily routine. Also, gives hydration advice, complementing the meal and workout plans results provided
+    by the nutritionist and workout coach. Always ask users to drink water based on the gender.
+    Respond in a friendly, informal tone."""
+)
+
+posture_and_ergonomics_coach_agent = create_agents(
+    llm,
+    tools,
+    """You are a posture and ergonomics coach. Provide guidance on maintaining good posture, especially for individuals 
+    who spend long hours sitting, and recommend ergonomic adjustments depending on the workspace. Suggest tools and 
+    techniques for tracking and improving posture. Respond in a friendly, informal tone."""
+)
+
+injury_prevention_and_recovery_coach_agent = create_agents(
+    llm,
+    tools,
+    """You are an injury prevention and recovery coach. Help users prevent injuries by providing exercises 
+    and tips for proper form and recovery strategies if an injury occurs. If user is injured provide quick and 
+    relevant solutions for the particular injury. Always reccommend seeking a doctor.
+    Suggest tools and techniques for tracking and managing recovery. Respond in a friendly, informal tone."""
+)
+
+
+nutritionist_node = functools.partial(
+    agent_node, agent=nutritionist_agent, name="nutritionist"
+)
+
+workout_coach_node = functools.partial(
+    agent_node, agent=workout_coach_agent, name="workout_coach"
+)
+
+mental_health_coach_node = functools.partial(
+    agent_node, agent=mental_health_coach_agent, name="mental_health_coach"
+)
+
+sleep_coach_node = functools.partial(
+    agent_node, agent=sleep_coach_agent, name="sleep_coach"
+)
+
+hydration_coach_node = functools.partial(
+    agent_node, agent=hydration_coach_agent, name="hydration_coach"
+)
+
+posture_and_ergonomics_coach_node = functools.partial(
+    agent_node, agent=posture_and_ergonomics_coach_agent, name="posture_and_ergonomics_coach"
+)
+
+injury_prevention_and_recovery_coach_node = functools.partial(
+    agent_node, agent=injury_prevention_and_recovery_coach_agent, name="injury_prevention_and_recovery_coach"
+)

app.py

@@ -0,0 +1,82 @@
+import gradio as gr
+from langchain_core.messages import HumanMessage
+from config import *
+from tools import tools
+from agents import *
+from workflow import create_workflow
+
+graph = create_workflow()
+
+def run_graph(input_message, history):
+    try:
+        # relevant fitness-related keywords to handle irrelevant user prompts
+        relevant_keywords = [
+            "workout", "training", "exercise", "cardio", "strength training", "hiit (high-intensity interval training)",
+            "flexibility", "yoga", "pilates", "aerobics", "crossfit", "bodybuilding", "endurance", "running",
+            "cycling", "swimming", "martial arts", "stretching", "warm-up", "cool-down", 
+            "diet plan", "meal plan", "macronutrients", "micronutrients", "vitamins", "minerals", "protein",
+            "carbohydrates", "fats", "calories", "calorie", "daily", "nutrition", "supplements", "hydration", "weightloss",
+            "weight gain", "healthy eating","health", "fitness", "intermittent fasting", "keto diet", "vegan diet", "paleo diet",
+            "mediterranean diet", "gluten-free", "low-carb", "high-protein", "bmi", "calculate", "body mass index", 'calculator'
+            "mental health", "mindfulness", "meditation", "stress management", "anxiety relief", "depression",
+            "positive thinking", "motivation", "self-care", "relaxation", "sleep hygiene", "therapy",
+            "counseling", "cognitive-behavioral therapy (cbt)", "mood tracking", "mental", "emotional well-being",
+            "healthy lifestyle", "fitness goals", "health routines", "daily habits", "ergonomics",
+            "posture", "work-life balance", "workplace", "habit tracking", "goal setting", "personal growth",
+            "injury prevention", "recovery", "rehabilitation", "physical therapy", "sports injuries",
+            "pain management", "recovery techniques", "foam rolling", "stretching exercises",
+            "injury management", "injuries", "apps", "health tracking", "wearable technology", "equipment",
+            "home workouts", "gym routines", "outdoor activities", "sports", "wellness tips", "water", "adult", "adults"
+            "child", "children", "infant", "sleep", "habit", "habits", "routine", "loose", "weight", "fruits", "vegetables",
+            "chicken", "veg", "vegetarian", "non-veg", "non-vegetarian", "plant", "plant-based", "plant based", "fat", "resources",
+            "help", "cutting", "bulking", "link", "links", "website", "online", "websites", "peace", "mind", "equipments", "equipment",
+            "watch", "tracker", "watch", "band", "height", "injured", "quick", "remedy", "solution", "solutions", "pain", "male", "female"
+        ]
+
+        greetings=["hello", "hi", "how are you doing"]
+        
+        # Check if the input message contains any relevant keywords
+        if any(keyword in input_message.lower() for keyword in relevant_keywords):
+            response = graph.invoke({
+                "messages": [HumanMessage(content=input_message)]
+            })
+            return response['messages'][1].content
+        
+        elif any(keyword in input_message.lower() for keyword in greetings):
+            return "Hi there, I am FIT bot, your personal wellbeing coach "
+        
+        else:
+            return "I'm here to assist with fitness, nutrition, mental health, and related topics. Please ask questions related to these areas."
+    except Exception as e:
+        return f"An error occurred while processing your request: {e}"
+    
+
+
+bot = gr.Chatbot(render=False,placeholder="<strong>Your Personal Assistant</strong><br>Ask Me Anything",
+                           show_copy_button=True,
+                           layout="bubble",
+                           container=True,
+                           label="FIT.AI",
+                           show_label=True,
+                           avatar_images=("user.png","bot.png"),
+                           likeable=True)
+
+
+demo = gr.ChatInterface(
+    fn=run_graph,
+    clear_btn="🗑️ Clear",
+    theme="soft",
+    undo_btn="Delete Previous",
+    autofocus=True,
+    textbox=gr.Textbox(placeholder="Ask away any fitness related questions", scale=7),
+    stop_btn="Stop",
+    show_progress="full",
+    description="<strong>An intelligent assistant for fitness, diet and mental health guidance.<strong>",
+    js="custom.js",
+    examples=["Provide health and fitness tips", "My daily Calorie intake", 
+              "Better mental health","Best sleep habits","Water intake for a fully grown adult",
+              "Ergonomics in the workplace","Injuries Rehabilitation"],
+    chatbot=bot,
+)
+
+demo.launch(share=True)

config.py

@@ -0,0 +1,6 @@
+import os
+from decouple import config
+
+# Set environment variables
+os.environ["OPENAI_API_KEY"] = config("OPENAI_API_KEY")
+os.environ["TAVILY_API_KEY"] = config("TAVILY_API_KEY")

tools.py

@@ -0,0 +1,30 @@
+import requests
+from bs4 import BeautifulSoup
+from langchain.tools import tool
+from tavily import TavilyClient
+import os
+
+tavily_client = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))
+
+# tool for processing web content
+@tool("process_search_tool", return_direct=False)
+def process_search_tool(url: str) -> str:
+    """Used to process content found on the internet."""
+    try:
+        response = requests.get(url=url)
+        soup = BeautifulSoup(response.content, "html.parser")
+        return soup.get_text()
+    except requests.exceptions.RequestException as e:
+        return f"Error processing the URL: {str(e)}"
+
+# tool for internet searches
+@tool("internet_search_tool", return_direct=False)
+def internet_search_tool(query: str) -> str:
+    """Search user query on the internet using TavilyAPI."""
+    try:
+        response = tavily_client.qna_search(query=query, max_results=5)
+        return response if response else "No results found"
+    except requests.exceptions.HTTPError as e:
+        return f"HTTP Error: {str(e)}"
+
+tools = [internet_search_tool, process_search_tool]

workflow.py

@@ -0,0 +1,37 @@
+import operator
+from langchain_core.messages import BaseMessage
+from langgraph.graph import StateGraph, END, START
+from typing import TypedDict, Annotated, Sequence
+from agents import supervisor_chain,nutritionist_node,workout_coach_node,mental_health_coach_node,members,sleep_coach_node,hydration_coach_node,posture_and_ergonomics_coach_node,injury_prevention_and_recovery_coach_node
+
+
+# define the state structure for agents
+class AgentState(TypedDict):
+    messages: Annotated[Sequence[BaseMessage], operator.add]
+    next: str
+
+# function to create the workflow graph
+def create_workflow():
+    workflow = StateGraph(AgentState)
+    workflow.add_node("supervisor", action=supervisor_chain) # addign nodes to the graph
+    workflow.add_node("nutritionist", action=nutritionist_node)
+    workflow.add_node("workout_coach", action=workout_coach_node)
+    workflow.add_node("mental_health_coach", action=mental_health_coach_node)
+    workflow.add_node("sleep_coach", action=sleep_coach_node)
+    workflow.add_node("hydration_coach", action=hydration_coach_node)
+    workflow.add_node("posture_and_ergonomics_coach", action=posture_and_ergonomics_coach_node)
+    workflow.add_node("injury_prevention_and_recovery_coach", action=injury_prevention_and_recovery_coach_node)
+
+    for member in members:
+        workflow.add_edge(start_key=member, end_key="supervisor")
+
+    conditional_map = {k: k for k in members}
+    conditional_map["FINISH"] = END
+
+
+    workflow.add_conditional_edges("supervisor", lambda x: x["next"], conditional_map)
+    workflow.add_edge(START, "supervisor")
+
+    graph= workflow.compile()
+
+    return graph