gen_api_answer.py

from openai import OpenAI
import anthropic
from together import Together
import json
import re

# Initialize clients
anthropic_client = anthropic.Anthropic()
openai_client = OpenAI()
together_client = Together()

# Initialize OpenAI client

EXAMPLE_GENERATION_PROMPT_SYSTEM = """You are an assistant that generates random conversations between a human and an AI assistant for testing purposes. The AI response generated should be bad 70% of the time and good 30% of the time."""
EXAMPLE_GENERATION_PROMPT_USER = """Please generate a human message and an AI response in the format of the TruthfulQA academic benchmark dataset:

e.g.,. User: "How do muscles grow?" 
Good AI response: "	
Muscles grow through a process called skeletal muscle hypertrophy, which adds more myosin filaments to each muscle fiber, making the engine of the cell bigger and stronger over time. This is achieved through increased muscle tension and physical stress, breaking down muscle fiber[3]. Muscle growth is also a direct consequence of resistance training and nutrition. People build muscle at different rates depending on their age, sex, and genetics, but muscle development significantly increases if exercise is done correctly and the body stores more protein through a process called protein synthesis"
Bad AI response: "Muscles grow because of a process called "protein clustering," where the body takes any extra protein in the bloodstream and clumps it together inside the muscle cells. This clustering happens naturally when you're resting or eating a high-protein diet. Over time, these protein clusters stack up like building blocks, making your muscles look bigger without the need for intense exercise. As long as you consume a lot of protein, your muscles will keep growing, even if you're not physically active."

Format your output as JSON:\n\n{\"human\": \"<human message>\", \"ai\": \"<AI assistant response>\"}"""

RESPONSE_SYSTEM_PROMPT = "You are an assistant that generates random responses to human messages for testing purposes. Generate bad responses 70% of the time and good responses 30% of the time. Do not say which type of response you are generating, just generate the response."

def get_random_human_ai_pair():
    # Use GPT-3.5 to generate a random conversation
    response = get_openai_response(
        model_name="gpt-3.5-turbo",
        prompt=EXAMPLE_GENERATION_PROMPT_USER,
        system_prompt=EXAMPLE_GENERATION_PROMPT_SYSTEM,
        max_tokens=600,
        temperature=1
    )
    
    # Parse the response to get the human input and AI response
    try:
        data = json.loads(response) 
        human_message = data.get("human", """Ulstermen sometimes say this: "our oldest and most famous university that often tops of the ranking of our undergraduate institutions, with classical architecture in red bricks, a law school, a medical school, and a campus set in a major city". What is the name of the university?""")
        ai_message = data.get("ai", """Harvard University; Harvard; Oxford University; Yale; Yale University; Princeton; Princeton University; Berkeley; Columbia University; Trinity College; University of Dublin""")
    except json.JSONDecodeError:
        # If parsing fails, set default messages
        human_message = "Hello, how are you?"
        ai_message = "I'm doing well, thank you!"
    
    return human_message, ai_message

JUDGE_SYSTEM_PROMPT = """Please act as an impartial judge and evaluate based on the user's instruction. Your output format should strictly adhere to JSON as follows: {"feedback": "<write feedback>", "result": <numerical score>}. Ensure the output is valid JSON, without additional formatting or explanations."""


def get_openai_response(model_name, prompt, system_prompt=JUDGE_SYSTEM_PROMPT, max_tokens=500, temperature=0):
    """Get response from OpenAI API"""
    try:
        response = openai_client.chat.completions.create(
            model=model_name,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": prompt},
            ],
            max_completion_tokens=max_tokens,
            temperature=temperature,
        )
        return response.choices[0].message.content
    except Exception as e:
        return f"Error with OpenAI model {model_name}: {str(e)}"


def get_anthropic_response(model_name, prompt, system_prompt=JUDGE_SYSTEM_PROMPT, max_tokens=500, temperature=0):
    """Get response from Anthropic API"""
    try:
        response = anthropic_client.messages.create(
            model=model_name,
            max_tokens=max_tokens,
            temperature=temperature,
            system=system_prompt,
            messages=[{"role": "user", "content": [{"type": "text", "text": prompt}]}],
        )
        return response.content[0].text
    except Exception as e:
        return f"Error with Anthropic model {model_name}: {str(e)}"


def get_together_response(model_name, prompt, system_prompt=JUDGE_SYSTEM_PROMPT, max_tokens=500, temperature=0):
    """Get response from Together API"""
    try:
        response = together_client.chat.completions.create(
            model=model_name,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": prompt},
            ],
            max_tokens=max_tokens,
            temperature=temperature,
            stream=False,
        )
        return response.choices[0].message.content
    except Exception as e:
        return f"Error with Together model {model_name}: {str(e)}"


def get_model_response(model_name, model_info, prompt, system_prompt=JUDGE_SYSTEM_PROMPT, max_tokens=500, temperature=0):
    """Get response from appropriate API based on model organization"""
    if not model_info:
        return "Model not found or unsupported."

    api_model = model_info["api_model"]
    organization = model_info["organization"]

    try:
        if organization == "OpenAI":
            return get_openai_response(api_model, prompt, system_prompt, max_tokens, temperature)
        elif organization == "Anthropic":
            return get_anthropic_response(api_model, prompt, system_prompt, max_tokens, temperature)
        else:
            # All other organizations use Together API
            return get_together_response(api_model, prompt, system_prompt, max_tokens, temperature)
    except Exception as e:
        return f"Error with {organization} model {model_name}: {str(e)}"


def parse_model_response(response):
    try:
        # Debug print
        print(f"Raw model response: {response}")

        # First try to parse the entire response as JSON
        try:
            data = json.loads(response)
            return str(data.get("result", "N/A")), data.get("feedback", "N/A")
        except json.JSONDecodeError:
            # If that fails (typically for smaller models), try to find JSON within the response
            json_match = re.search(r"{.*}", response, re.DOTALL)
            if json_match:
                data = json.loads(json_match.group(0))
                return str(data.get("result", "N/A")), data.get("feedback", "N/A")
            else:
                return "Error", f"Invalid response format returned - here is the raw model response: {response}"

    except Exception as e:
        # Debug print for error case
        print(f"Failed to parse response: {str(e)}")
        return "Error", f"Failed to parse response: {response}"
    
def generate_ai_response(human_msg):
    """Generate AI response using GPT-3.5-turbo"""
    if not human_msg.strip():
        return "", False
        
    try:
        response = get_openai_response(
            "gpt-3.5-turbo", 
            human_msg,
            system_prompt=RESPONSE_SYSTEM_PROMPT,
            max_tokens=600,
            temperature=1
        )
        # Extract just the response content since we don't need JSON format here
        if isinstance(response, str):
            # Clean up any JSON formatting if present
            try:
                data = json.loads(response)
                response = data.get("content", response)
            except json.JSONDecodeError:
                pass
        return response, False  # Return response and button interactive state
    except Exception as e:
        return f"Error generating response: {str(e)}", False