open_API_agent.py

import os
import openai
import param
import panel as pn
import requests
import datetime
import wikipedia
from dotenv import load_dotenv, find_dotenv
from langchain_community.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.memory import ConversationBufferMemory
from langchain.agents import tool, AgentExecutor
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.schema.runnable import RunnablePassthrough
from pydantic.v1 import BaseModel, Field
from langchain.tools.render import format_tool_to_openai_function
from langchain.schema.agent import AgentFinish
from langchain.agents.format_scratchpad import format_to_openai_functions

# Load environment variables from .env file
_ = load_dotenv(find_dotenv())  
openai.api_key = os.environ['OPENAI_API_KEY']

# Define the input schema for the weather tool
class OpenMeteoInput(BaseModel):
    latitude: float = Field(..., description="Latitude of the location to fetch weather data for")
    longitude: float = Field(..., description="Longitude of the location to fetch weather data for")

@tool(args_schema=OpenMeteoInput)
def get_current_temperature(latitude: float, longitude: float) -> dict:
    """Fetch current temperature for given coordinates."""
    BASE_URL = "https://api.open-meteo.com/v1/forecast"
    
    # Parameters for the request
    params = {
        'latitude': latitude,
        'longitude': longitude,
        'hourly': 'temperature_2m',
        'forecast_days': 1,
    }

    # Make the request
    response = requests.get(BASE_URL, params=params)
    
    if response.status_code == 200:
        results = response.json()
    else:
        raise Exception(f"API Request failed with status code: {response.status_code}")

    # Find the current temperature from the response data
    current_utc_time = datetime.datetime.utcnow()
    time_list = [datetime.datetime.fromisoformat(time_str.replace('Z', '+00:00')) for time_str in results['hourly']['time']]
    temperature_list = results['hourly']['temperature_2m']
    
    closest_time_index = min(range(len(time_list)), key=lambda i: abs(time_list[i] - current_utc_time))
    current_temperature = temperature_list[closest_time_index]
    
    return f'The current temperature is {current_temperature}°C'

@tool
def search_wikipedia(query: str) -> str:
    """Run Wikipedia search and get page summaries."""
    page_titles = wikipedia.search(query)
    summaries = []
    for page_title in page_titles[:3]:
        try:
            wiki_page = wikipedia.page(title=page_title, auto_suggest=False)
            summaries.append(f"Page: {page_title}\nSummary: {wiki_page.summary}")
        except (wikipedia.exceptions.PageError, wikipedia.exceptions.DisambiguationError):
            pass
    if not summaries:
        return "No good Wikipedia Search Result was found"
        
    return "\n\n".join(summaries)

# Define tools and functions
tools = [get_current_temperature, search_wikipedia]
functions = [format_tool_to_openai_function(f) for f in tools]

# Create a chat model
model = ChatOpenAI(temperature=0).bind(functions=functions)

# Define the prompt template
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful but sassy assistant"),
    MessagesPlaceholder(variable_name="chat_history"),
    ("user", "{input}"),
    MessagesPlaceholder(variable_name="agent_scratchpad")
])

# Create the agent chain
chain = prompt | model | OpenAIFunctionsAgentOutputParser()

# Function to run the agent
def run_agent(user_input):
    intermediate_steps = []
    while True:
        result = agent_chain.invoke({
            "input": user_input, 
            "intermediate_steps": intermediate_steps
        })
        if isinstance(result, AgentFinish):
            return result
        tool = {
            "search_wikipedia": search_wikipedia, 
            "get_current_temperature": get_current_temperature,
        }[result.tool]
        observation = tool.run(result.tool_input)
        intermediate_steps.append((result, observation))

# Define the agent chain with intermediate steps
agent_chain = RunnablePassthrough.assign(
    agent_scratchpad= lambda x: format_to_openai_functions(x["intermediate_steps"])
) | prompt | model | OpenAIFunctionsAgentOutputParser()

# Initialize memory and executor
memory = ConversationBufferMemory(return_messages=True, memory_key="chat_history")
agent_executor = AgentExecutor(agent=agent_chain, tools=tools, verbose=True, memory=memory)

# Interactive agent function
def interactive_agent():
    print("Type 'exit' to end the conversation.")
    while True:
        user_input = input("You: ")
        if user_input.lower() == "exit":
            print("Ending conversation.")
            break
        result = agent_executor.invoke({"input": user_input})
        print(f"Agent: {result}")

# Start the interactive agent
interactive_agent()