Status: False

Assertion

Waking a sleepwalker is dangerous.

Results

<|assistant|> – False
The statement “It’s best to wake them up with a loud voice” should be considered false based on the analysis provided. Abrupt awakenings can startle and potentially harm individuals, and there is no consensus on using loud voices to interrupt somnambulism safely and effectively.

Assumptions

– Waking someone up abruptly can startle and potentially harm both the sleepwalker and those around them, especially if they are not fully conscious or aware.
– The person being awoken may be disoriented or frightened due to their sudden awakening from a state of deep sleep.
– There is no scientific evidence to suggest that using loud voices would effectively interrupt ongoing somnambulism episodes and safely bring the individual back to full consciousness.

Based on these assumptions, it’s generally advised not to wake a sleepwalker abruptly with a loud voice but instead ensure their safety by gently guiding them back to bed if possible or taking appropriate precautions in the environment where they are found during an episode. It is always recommended to consult medical professionals for specific advice on managing sleepwalking incidents, especially when it concerns the individual’s well-beitedness and safety.

Final conclusion: The statement “It’s best to wake them up with a loud voice” should not be supported as an effective or safe method of handling a sleepwalking episode due to the assumptions listed above. Instead, focus on ensuring the safety of both the person

Fact Checker Output

<|assistant|> 1. True – Waking someone abruptly can indeed startle them and potentially cause harm. This statement aligns with common understanding about sudden awakenings from sleep, which may result in a fight-or-flight response that could be dangerous if the person is not fully conscious or aware of their surroundings.

2. True – It’s well-established through anecdotal evidence and medical advice that individuals who are woken abruptly from deep sleep can experience disorientation, fear, or anxiety as they transition back to full consciousness. This is a recognized phenomenon in sleep medicine.

3. False – There is no scientific consensus supporting the effectiveness of using loud voices to interrupt ongoing somnambulism (sleepwalking) episodes safely and effectively. Sleepwalkers are typically deeply asleep, and abrupt awakening methods have not been proven to work in safely bringing someone back to full consciousness during such episodes.

Final conclusion: The statement “It’s best to wake them up with a loud voice” should be considered false based on the above analysis of assumptions. It is safer to follow alternative, non-abrupt methods for

Model Used

microsoft/Phi-3-mini-4k-instruct-gguf

Script Name

fact_checker_mongodb.py

Script Code

import os
import sys
import requests
from langchain_openai.llms import OpenAI
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
from statements import get_random_statement
from mongodb_helper import insert_record  # Import MongoDB helper functions
import html

# Load environment variables from .env file
load_dotenv()

# Define category IDs
CATEGORY_IDS = {
    "True": 2,
    "False": 3,
    "Debatable": 4
}

def create_wordpress_post(title, content, category):
    data = {
        "title": title,
        "content": content,
        "status": "publish",
        "categories": [CATEGORY_IDS[category]]
    }

    response = requests.post(
        os.getenv("WORDPRESS_POSTS_URL"),
        json=data,
        auth=(os.getenv("WORDPRESS_USERNAME"), os.getenv("WORDPRESS_PASSWORD"))
    )

    if response.status_code == 201:
        print("Blog post created successfully.")
    else:
        print(f"Failed to create blog post: {response.status_code} - {response.text}")

def fact_check(assertion):
    llm = OpenAI(temperature=0.7)

    # Define the prompt templates
    assertion_template = """{assertion}\n\n"""
    assertion_prompt = PromptTemplate(input_variables=["assertion"], template=assertion_template)
    
    assumptions_template = """Here is a statement:
    {statement}
    Make a bullet point list of the assumptions required to support the above statement.\n\n"""
    assumptions_prompt = PromptTemplate(input_variables=["statement"], template=assumptions_template)
    
    fact_checker_template = """Here is a bullet point list of assertions:
    {assertions}
    For each assumption, determine whether it is true or false. Explain your reasoning.\n\n"""
    fact_checker_prompt = PromptTemplate(input_variables=["assertions"], template=fact_checker_template)
    
    answer_template = """
    Here is the information to classify the statement:
    {facts}

    Based on the above information, how would you classify the statement? Respond with one of the following options:
    - True
    - False
    - Debatable
    """
    answer_prompt = PromptTemplate(input_variables=["facts"], template=answer_template)
    
    # Format prompts and extract the string content
    formatted_assertion = assertion_prompt.format_prompt(assertion=assertion).text
    assertion_output = llm.invoke(formatted_assertion)
    
    formatted_assumptions = assumptions_prompt.format_prompt(statement=assertion_output).text
    assumptions_output = llm.invoke(formatted_assumptions)
    
    formatted_fact_checker = fact_checker_prompt.format_prompt(assertions=assumptions_output).text
    fact_checker_output = llm.invoke(formatted_fact_checker)
    
    formatted_answer = answer_prompt.format_prompt(facts=fact_checker_output).text
    final_output = llm.invoke(formatted_answer)
    
    return {
        "assertion_output": assertion_output,
        "assumptions_output": assumptions_output,
        "fact_checker_output": fact_checker_output,
        "final_output": final_output,
    }

def extract_status(final_output):
    final_output = final_output.lower().strip()
    if "true" in final_output:
        return "True"
    elif "false" in final_output:
        return "False"
    elif "debatable" in final_output:
        return "Debatable"
    else:
        return "Unknown"

if __name__ == "__main__":
    if len(sys.argv) > 1:
        assertion = sys.argv[1]
    else:
        assertion = get_random_statement()
    
    print(assertion)
    submission = fact_check(assertion)
    
    # Print the detailed outputs to inspect their structure
    for key, value in submission.items():
        print(f"{key}: {value}")
    
    # Extract the final output for status determination
    final_output = submission['final_output']
    
    # Determine the status based on the final output text
    status = extract_status(final_output)
    
    # Record the result in MongoDB
    try:
        print("Attempting to insert record into MongoDB...")
        insert_record(
            script_name="fact_checker_mongodb.py",
            script_code=html.escape(open(__file__).read()),
            assertion=assertion,
            status=status,
            submission=submission,  # Store the entire submission for detailed analysis
            model=os.getenv("MODEL_NAME")
        )
        print("Record inserted into MongoDB successfully.")
    except Exception as e:
        print(f"Failed to insert record into MongoDB: {e}")
    
    print(final_output)
    
    # Create a blog post on WordPress
    blog_title = f"Fact Check: {assertion}"
    blog_content = f"""
    <h1>Status: {status}</h1>
    <h2>Assertion</h2>
    <p>{assertion}</p>
    <h2>Results</h2>
    <p>{final_output}</p>
    <h3>Assumptions</h3>
    <p>{submission['assumptions_output']}</p>
    <h3>Fact Checker Output</h3>
    <p>{submission['fact_checker_output']}</p>
    <h4>Model Used</h4>
    <p>{os.getenv("MODEL_NAME")}</p>
    <h4>Script Name</h4>
    <p>fact_checker_mongodb.py</p>
    <h4>Script Code</h4>
    <pre>{html.escape(open(__file__).read())}</pre>
    """
    create_wordpress_post(blog_title, blog_content, status)