Status: True

Assertion

Dropping a penny from the Empire State Building can kill someone.

Results

The statement appears to be true if we consider that James V. Schaefer is indeed the screenwriter of “Insomnia,” and Billy Ray was involved as a producer who could have had approval for scripts, though typically producers do not directly approve scripts; this responsibility usually falls under the film’s director or studio executives. However, without explicit confirmation from the production team regarding script approval, there might be nuances that are not captured in our information.

– True: The actors listed were likely involved with “Insomnia” given their reputations and roles typically associated with them, but it is always possible for an actor to appear in a movie without being widely recognized or credited; therefore, this classification assumes reliable source confirmation of the cast list.

– Debatable: This statement’s truthfulness depends on whether there were any legal disputes or contractual issues related to script rights between Schaefer and others involved with “Insomnia.” Without specific details regarding these matters, we can only speculate about its veracity based on publicly available information.

Considering the above explanations:

– The information provided is accurate and up-to-

Assumptions

# Answer
To support the statement about the box office collection and cast members for the movie “Insomnia,” the following assumptions could be made:

– The information provided is accurate and up-to-date regarding the financial success and casting details.
– The box office revenue mentioned ($585.1 million) encompasses all earnings from domestic, international releases, and may include ancillary revenues like home video sales or streaming rights fees.
– The listed actors (Peter Stormare, Odette Yustman, Hugh Jackman, Jason Clarke, Ben Whishaw, Diane Ladd, Koyu Ross, Guy Pearce, Tadanobu Asano) were credited for their roles in the film “Insomnia.”
– The script by James V. Schaefer and Billy Ray was approved by movie executives to be used in this particular production of “Insomnia.”
– No significant changes occurred after the release that would impact the reported box office collection or cast information, such as additional earnings or revised credits due to contractual issues.

Fact Checker Output

<|assistant|> – The information provided is accurate and up-to-date regarding the financial success and casting details: **True/False** (Reasoning: Since this assertion cannot be validated without external verification of the data source, one would need to confirm with a reliable database or official release notes.)

– The box office revenue mentioned ($585.1 million) encompasses all earnings from domestic, international releases, and may include ancillary revenues like home video sales or streaming rights fees: **True/False** (Reasoning: It is common for a film’s reported total gross to include various revenue streams; however, confirmation of the specific breakdown would require an official report from the production company.)

– The listed actors were credited for their roles in the film “Insomnia”: **True/False** (Reasoning: Assuming that the list was compiled based on reliable sources such as IMDb or other reputable databases, this statement is likely true. However, without direct verification of the movie’s official cast list, one cannot be entirely certain.)

– The script by James V. Schaefer and Billy Ray was approved for use in this production

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,
    "Unknown": 6
}

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, model=os.getenv("MODEL_NAME"))

    # 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 followed by a colon and space:
    - True: [Explanation]
    - False: [Explanation]
    - Debatable: [Explanation]
    """
    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_and_reasoning(final_output):
    final_output = final_output.strip()
    if "True:" in final_output:
        status_start = final_output.find("True:")
        status = "True"
    elif "False:" in final_output:
        status_start = final_output.find("False:")
        status = "False"
    elif "Debatable:" in final_output:
        status_start = final_output.find("Debatable:")
        status = "Debatable"
    else:
        return "Unknown", final_output

    reasoning = final_output[status_start + len(status) + 1:].strip()
    return status, reasoning

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 and reasoning
    final_output = submission['final_output']
    status, reasoning = extract_status_and_reasoning(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>{reasoning}</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)