Status: True
Assertion
Legalising drugs would reduce crime rates.
Results
The primary motivations behind criminal behavior related to drugs often include economic and social factors. Financial gain can drive individuals into illegal drug trades, while peer pressure and community norms may influence others to engage in drug-related activities. However, it’s important to note that not all criminals involved with drugs are solely motivated by these factors; there are various other reasons as well. This classification is based on the provided information regarding criminal behavior related to drugs and their underlying causes.
Assumptions
Supporting Assumptions:
– The primary motivations behind criminal behavior related to drugs are economic (e.g., financial gain from illegal trade) and social (e.g., peer pressure or community norms).
– Legalizing drugs would effectively eliminate these motivations by removing the black market for illicit drug sales.
– Government regulation of legalized drugs can successfully ensure product quality, safety, and prevent abuse without creating additional unintended consequences (e.g., an increase in substance misuse).
– The benefits gained from reallocating law enforcement resources to other types of crime would outweigh potential negative impacts of drug use on society.
– Legalization does not lead to increased accessibility and availability of drugs, resulting in higher rates of addiction or related health issues.
– Social programs and education initiatives are implemented alongside legalization measures to promote responsible drug use and prevent substance abuse disorders.
– Economic factors such as stable financial conditions and effective social welfare systems do not significantly influence crime rates independently from drug policy changes.
– The cultural attitudes towards drugs shift positively, reducing the stigmatization of users
Fact Checker Output
<|assistant|> – **True**: The primary motivations behind criminal behavior related to drugs often include economic and social factors. Financial gain can drive individuals into illegal drug trades, while peer pressure and community norms may influence others to engage in drug-related activities. However, it’s important to note that not all criminals involved with drugs are solely motivated by these factors; there are various other reasons as well.
– **False**: While legalizing drugs might aim to eliminate the black market and its associated criminal activity, this assumption oversimplifies the issue. Legalization does not guarantee complete elimination of illegal activities related to drugs since some people may still participate in illicit drug trade due to addiction or other motivations. However, legalization could reduce these activities by decreasing their profitability and social acceptance.
– **False**: Government regulation can ensure product quality and safety; however, it’s challenging to predict precisely the consequences without real-world implementation data. While regulatory measures like licensing, age restrictions, and taxation could help prevent abuse, unintended negative effects might arise as well, such as new forms of black market
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)
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