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from transformers import pipeline
import gradio as gr
# Load pre-trained pipelines
try:
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
ner = pipeline("ner", model="Davlan/bert-base-multilingual-cased-ner-hrl", aggregation_strategy="simple")
except Exception as e:
summarizer = None
ner = None
print("Error loading models:", e)
# Nigerian law reference (basic keyword-to-punishment mapping)
crime_punishment_map = {
"theft": {"law": "Criminal Code Act, Section 390", "punishment": "Up to 3 years imprisonment"},
"armed robbery": {"law": "Robbery and Firearms Act, Section 1", "punishment": "Death penalty or life imprisonment"},
"internet fraud": {"law": "Cybercrime Act, 2015", "punishment": "Minimum of 7 years imprisonment"},
"rape": {"law": "Criminal Law of Lagos State, Section 260", "punishment": "Life imprisonment"},
"murder": {"law": "Criminal Code Act, Section 319", "punishment": "Death penalty"},
"assault": {"law": "Criminal Code Act, Section 351", "punishment": "1 year imprisonment"}
}
def classify_crime(text):
text = text.lower()
for crime in crime_punishment_map:
if crime in text:
return crime, crime_punishment_map[crime]
return "unknown", {
"law": "N/A",
"punishment": "No specific punishment found. Manual review required."
}
# Main analysis function with full error handling
def analyze_text(text):
try:
if not text.strip():
return "No text provided.", [], {"Crime Type": "N/A", "Applicable Law": "N/A", "Recommended Punishment": "N/A"}
summary = summarizer(text, max_length=80, min_length=30, do_sample=False)[0].get("summary_text", "Summary failed.")
entities = ner(text)
crime_type, law_info = classify_crime(text)
return summary, entities, {
"Crime Type": crime_type.title() if crime_type != "unknown" else "Unknown",
"Applicable Law": law_info["law"],
"Recommended Punishment": law_info["punishment"]
}
except Exception as e:
return f"⚠️ An error occurred: {str(e)}", [], {
"Crime Type": "Error",
"Applicable Law": "Error",
"Recommended Punishment": "Error"
}
# Launch app
gr.Interface(
fn=analyze_text,
inputs=gr.Textbox(lines=12, label="Paste Criminal Case Text"),
outputs=[
gr.Textbox(label="Summary"),
gr.JSON(label="Extracted Entities"),
gr.JSON(label="Legal Analysis / Recommended Punishment")
],
title="JusticeAI - Legal Case Analyzer",
description="Paste any criminal case report. This AI will summarize it, extract important entities, and recommend the legal punishment based on Nigerian law."
).launch() |