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import json

import os

import azure.identity

import openai

from dotenv import load_dotenv

# Setup the OpenAI client to use either Azure, OpenAI.com, or Ollama API

load_dotenv(override=True)

API_HOST = os.getenv("API_HOST", "azure")

if API_HOST == "azure":

token_provider = azure.identity.get_bearer_token_provider(

azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"

)

client = openai.OpenAI(

base_url=f"{os.environ['AZURE_OPENAI_ENDPOINT'].rstrip('/')}/openai/v1/",

api_key=token_provider,

)

MODEL_NAME = os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"]

elif API_HOST == "ollama":

client = openai.OpenAI(base_url=os.environ["OLLAMA_ENDPOINT"], api_key="nokeyneeded")

MODEL_NAME = os.environ["OLLAMA_MODEL"]

else:

client = openai.OpenAI(api_key=os.environ["OPENAI_KEY"])

MODEL_NAME = os.environ["OPENAI_MODEL"]

tools = [

{

"type": "function",

"name": "lookup_weather",

"description": "Lookup the weather for a given city name or zip code.",

"parameters": {

"type": "object",

"properties": {

"city_name": {

"type": "string",

"description": "The city name",

},

"zip_code": {

"type": "string",

"description": "The zip code",

},

},

"additionalProperties": False,

},

},

{

"type": "function",

"name": "lookup_movies",

"description": "Lookup movies playing in a given city name or zip code.",

"parameters": {

"type": "object",

"properties": {

"city_name": {

"type": "string",

"description": "The city name",

},

"zip_code": {

"type": "string",

"description": "The zip code",

},

},

"additionalProperties": False,

},

},

]

# ---------------------------------------------------------------------------

# Tool (function) implementations

# ---------------------------------------------------------------------------

def lookup_weather(city_name: str | None = None, zip_code: str | None = None) -> str:

"""Looks up the weather for given city_name and zip_code."""

location = city_name or zip_code or "unknown"

# In a real implementation, call an external weather API here.

return {

"location": location,

"condition": "rain showers",

"rain_mm_last_24h": 7,

"recommendation": "Good day for indoor activities if you dislike drizzle.",

}

def lookup_movies(city_name: str | None = None, zip_code: str | None = None) -> str:

"""Returns a list of movies playing in the given location."""

location = city_name or zip_code or "unknown"

# A real implementation could query a cinema listings API.

return {

"location": location,

"movies": [

{"title": "The Quantum Reef", "rating": "PG-13"},

{"title": "Storm Over Harbour Bay", "rating": "PG"},

{"title": "Midnight Koala", "rating": "R"},

],

}

tool_mapping = {

"lookup_weather": lookup_weather,

"lookup_movies": lookup_movies,

}

# ---------------------------------------------------------------------------

# Conversation loop

# ---------------------------------------------------------------------------

messages = [

{"role": "system", "content": "You are a tourism chatbot."},

{"role": "user", "content": "Is it rainy enough in Sydney to watch movies and which ones are on?"},

]

print(f"Model: {MODEL_NAME} on Host: {API_HOST}\n")

while True:

print("Calling model...\n")

response = client.responses.create(

model=MODEL_NAME,

input=messages, # includes prior tool outputs

tools=tools,

tool_choice="auto",

store=False,

)

tool_calls = [item for item in response.output if item.type == "function_call"]

# If the assistant returned standard content with no tool calls, we're done.

if not tool_calls:

print("Assistant:")

print(response.output_text)

break

# Append the function call items from response output

messages.extend(response.output)

# Execute each requested tool sequentially.

for tool_call in tool_calls:

fn_name = tool_call.name

raw_args = tool_call.arguments or "{}"

print(f"Tool request: {fn_name}({raw_args})")

target_tool = tool_mapping.get(fn_name)

parsed_args = json.loads(raw_args)

tool_result = target_tool(**parsed_args)

tool_result_str = json.dumps(tool_result)

# Provide the tool output back to the model

messages.append(

{

"type": "function_call_output",

"call_id": tool_call.call_id,

"output": tool_result_str,

}

)