Status: True
Assertion
Lightning never strikes the same place twice.
Results
The statement is accurate. Lightning strikes are indeed caused by an electrical discharge between regions of opposite charges within a thunderstorm cloud or between the cloud base and the ground, with various factors influencing their location. Thunderstorms vary in intensity, duration, and spatial extent based on atmospheric conditions at formation time. The unpredictability of lightning strikes arises from complex interactions among meteorological factors within thunderstorms.
Assumptions
<|assistant|> – Lightning strikes are caused by an electrical discharge between positively charged upper regions (anticyclones) and negatively charged lower regions (cyclones) in a thunderstorm cloud, or between the cloud base and the ground.
– The exact location of each lightning strike depends on factors such as the distribution of these charges within the storm cloud, local terrain, and surrounding environment.
– Thunderstorms are temporary weather systems with specific atmospheric conditions that can vary widely in terms of intensity, duration, and spatial extent.
– Lightning strikes are highly unpredictable events influenced by complex interactions between various meteorological factors within a thunderstorm.
– Multiple lightning strikes occurring at nearby locations during an intense thunderstorm is possible due to the dynamic nature of storm systems.
– Instances where lightning has struck the same location more than once in a short period are extremely rare, but not entirely impossible, based on historical observations and scientific data.
– The probability of multiple strikes at the same location within a single thunderstorm system is influenced by factors such as storm strength, size, charge distribution, terrain features, and prevailomed atmos
Fact Checker Output
explanation: – True. Lightning strikes are indeed caused by an electrical discharge between regions of opposite charges within a thunderstorm cloud or between the cloud base and the ground. This happens when there’s a separation of positive and negative charges, leading to an intense electric field that results in lightning.
– True. The exact location where a lightning strike occurs can vary due to several factors including charge distribution within the storm cloud, local topography, and other environmental variables like humidity and temperature gradients. These factors influence how electrical charges build up and discharge during a thunderstorm.
– True. Thunderstorms are transient meteorological phenomena characterized by specific atmospheric conditions such as moisture, instability, and wind shear. Their intensity, duration, and spatial extent can vary significantly from one event to another based on the prevailing weather patterns at the time of their formation.
– True. The unpredictability of lightning strikes is a result of complex interactions between various meteorological factors within thunderstorms. These include charge distribution, storm dynamics (like updrafts and downdrafts), temperature gradients
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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