app.py

import os
import re
import httpx
import spaces
import openai
from openai import OpenAI
import gradio as gr

# Set Hugging Face API (needed for gated models)
hf_api_key = os.environ.get('HF_API_KEY')
client = OpenAI()  # Assumes OPENAI_API_KEY is set in environment

# Default system prompt
prompt = """
You run in a loop of Thought, Action, PAUSE, Observation.
At the end of the loop you output an Answer
Use Thought to describe your thoughts about the question you have been asked.
Use Action to run one of the actions available to you - then return PAUSE.
Observation will be the result of running those actions.

Your available actions are:

calculate:
e.g. calculate: 4 * 7 / 3
Runs a calculation and returns the number - uses Python so be sure to use floating point syntax if necessary

average_dog_weight:
e.g. average_dog_weight: Collie
returns average weight of a dog when given the breed

Example session:

Question: How much does a Bulldog weigh?
Thought: I should look the dogs weight using average_dog_weight
Action: average_dog_weight: Bulldog
PAUSE

You will be called again with this:

Observation: A Bulldog weights 51 lbs

You then output:

Answer: A bulldog weights 51 lbs
""".strip()

class Agent:
    def __init__(self, system=""):
        self.system = system
        self.messages = []
        if self.system:
            self.messages.append({"role": "system", "content": system})

    def __call__(self, message):
        self.messages.append({"role": "user", "content": message})
        result = self.execute()
        self.messages.append({"role": "assistant", "content": result})
        return result

    def execute(self):
        completion = client.chat.completions.create(
                        model="gpt-4o", 
                        temperature=0,
                        messages=self.messages)
        return completion.choices[0].message.content

# Tools
def calculate(what):
    return eval(what)

def average_dog_weight(name):
    if name in "Scottish Terrier": 
        return("Scottish Terriers average 20 lbs")
    elif name in "Border Collie":
        return("a Border Collies average weight is 37 lbs")
    elif name in "Toy Poodle":
        return("a toy poodles average weight is 7 lbs")
    else:
        return("An average dog weights 50 lbs")

# Available actions
known_actions = {
    "calculate": calculate,
    "average_dog_weight": average_dog_weight
}

# First manual example, use of LLM agent answering a query
abot = Agent(prompt)

result = abot("How much does a toy poodle weigh?")
print(result)

result = average_dog_weight("Toy Poodle")

print(result)

next_prompt = "Observation: {}".format(result)

abot(next_prompt)

print(abot.messages)

# Second manual example, use of LLM agent answering a query
abot = Agent(prompt)

question = """I have 2 dogs, a border collie and a scottish terrier. \
What is their combined weight"""
abot(question)

next_prompt = "Observation: {}".format(average_dog_weight("Border Collie"))
print(next_prompt)

abot(next_prompt)

next_prompt = "Observation: {}".format(average_dog_weight("Scottish Terrier"))
print(next_prompt)

abot(next_prompt)

next_prompt = "Observation: {}".format(eval("37 + 20"))
print(next_prompt)

abot(next_prompt)

action_re = re.compile('^Action: (\w+): (.*)$')   # python regular expression to selection action

@spaces.GPU(duration=120) # Designed to be effect-free in non-ZeroGPU environments, ensuring compatibility across different setups.
def query(question, max_turns=5):
    i = 0
    # Create agent based on default system prompt
    bot = Agent(prompt)
    next_prompt = question
    while i < max_turns:
        i += 1
        result = bot(next_prompt)
        print(result)
        actions = [action_re.match(a) for a in result.split('\n') if action_re.match(a)]
        if actions:
            # There is an action to run
            action, action_input = actions[0].groups()
            if action not in known_actions:
                raise Exception("Unknown action: {}: {}".format(action, action_input))
            print(" -- running {} {}".format(action, action_input))
            observation = known_actions[action](action_input)
            print("Observation:", observation)
            next_prompt = "Observation: {}".format(observation)
        else:
            return result
    return "Max turns reached"


# Gradio interface
def process_question(question):
    return query(question)

iface = gr.Interface(
    fn=process_question,
    inputs=gr.Textbox(label="Enter your question"), # e.g. I have 2 dogs, a border collie and a scottish terrier. What is their combined weight
    outputs=gr.Textbox(label="Answer"),
    title="Dog Weight Calculator",
    description="Ask about dog weights or perform calculations."
)

if __name__ == "__main__":
    iface.launch()