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import gradio as gr
import requests
from datasets import load_dataset
from transformers import pipeline
# Load the dataset
dataset = load_dataset("viber1/indian-law-dataset")['train']
# Load a pre-trained language model for question-answering
qa_model = pipeline("question-answering", model="deepset/roberta-base-squad2")
def get_answer_from_api(query):
# Use CourtListener API to get legal information
base_url = "https://www.courtlistener.com/api/rest/v4/search/"
headers = {
"Authorization": "Token 9c70738ed9eb3cce4f3782a91c7c8a218c180b89" # Replace with your actual API token
}
params = {
"q": query,
"page_size": 1 # Limit the number of results returned
}
try:
response = requests.get(base_url, headers=headers, params=params)
response.raise_for_status() # Raise an error for bad responses
results = response.json()
# Check if there are any results
if results.get('count', 0) > 0:
return results['results'][0]['case_name'] # Adjust based on actual response structure
else:
return None # No results found
except requests.RequestException as e:
print(f"API request failed: {e}") # Print the error message for debugging
return None # Return None if there was an error
def get_answer_from_dataset(query):
# Look for an answer in the dataset
for entry in dataset:
if query.lower() in entry['Instruction'].lower():
return entry['Response']
return None # No answer found in the dataset
def get_answer_from_model(query):
# Use the pre-trained model to generate an answer
context = " ".join([entry['Response'] for entry in dataset]) # Combine all responses from dataset
result = qa_model(question=query, context=context)
return result['answer'] if result['score'] > 0.2 else None # Return answer if confidence score is high
def respond(query):
# First, try to get the answer from the API
answer = get_answer_from_dataset(query)
if answer:
return answer # Return if found in API
# If not found, look in the dataset
answer = get_answer_from_model(query)
if answer:
return answer # Return if found in dataset
# If still no answer, use the model
return get_answer_from_api(query)
# Gradio interface
demo = gr.Interface(
fn=respond,
inputs="text",
outputs="text",
title="AI Legal Assistant",
description="Ask your legal queries regarding Indian laws"
)
if _name_ == "_main_":
demo.launch() |