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import json
import os
from dotenv import load_dotenv
from openai import OpenAI
from tqdm import tqdm
from scripts.regulatory_change_foundation import CONTEXT_CATEGORIES
from scripts.utility_functions import render_prompt
# Load environment variables from .env file
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
openai_client = OpenAI(api_key=api_key)
def create_prompt_without_nlp_insights(text):
return render_prompt(text, include_nlp=False)
def classify_changes_without_nlp_insights(text_content, subtitle):
"""Classify changes in text chunks using OpenAI."""
chunks = text_content.split("\n\n")
results = []
for chunk in chunks:
response = openai_client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{
"role": "system",
"content": "You are a legal expert specializing in analyzing German regulatory documents with a focus on identifying regulatory changes. Only return JSON output.",
},
{"role": "user", "content": create_prompt_without_nlp_insights(chunk)},
],
temperature=0.7,
max_tokens=1024,
)
try:
result = json.loads(response.choices[0].message.content)
if result.get("changes_detected", False):
result["location"] = {"subtitle": subtitle} # Use subtitle as location
result["source_text"] = chunk
results.append(result)
except json.JSONDecodeError:
continue
return results
def llm_regulatory_change_detector_without_nlp_insights(hierarchical_structure):
if hierarchical_structure:
analysis_summary = {
"total_changes_detected": 0,
"changes_by_type": {"additions": 0, "deletions": 0, "modifications": 0},
}
subtitles = {}
# Iterate over sections and analyze content
for section in tqdm(
hierarchical_structure["sections"], desc="Analyzing Sections"
):
subtitle = section["subtitle"]
content = section["content"]
if isinstance(content, list):
content = "\n".join(content)
# Detect changes for this subtitle
changes = classify_changes_without_nlp_insights(content, subtitle)
# Update analysis summary
for change in changes:
analysis_summary["total_changes_detected"] += len(
change["classifications"]
)
for classification in change["classifications"]:
change_type = classification["change_type"]
analysis_summary["changes_by_type"][f"{change_type}s"] += 1
# Group changes by subtitle
subtitles[subtitle] = []
for change in changes:
for classification in change["classifications"]:
change_subtype = (
"context"
if classification["change"] in CONTEXT_CATEGORIES
else "scope"
)
subtitles[subtitle].append(
{
"change": classification["change"],
"change_type": classification["change_type"],
"change_subtype": change_subtype,
"relevant_text": classification["relevant_text"],
"explanation": classification["explanation"],
}
)
# Combine analysis summary and grouped changes
final_output = {"analysis_summary": analysis_summary, "results": subtitles}
return final_output
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