app.py

import spaces
import bm25s
import gradio as gr
import json
import Stemmer  # from PyStemmer
import time
import torch
# from retrieval import *
import os
from transformers import AutoTokenizer, AutoModel, pipeline , AutoModelForSequenceClassification, AutoModelForCausalLM
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import pandas as pd
import torch.nn.functional as F
from datasets import concatenate_datasets, load_dataset, load_from_disk
from huggingface_hub import hf_hub_download
from contextual import ContextualAI
from openai import AzureOpenAI
from datetime import datetime
import sys

"""
# to switch:
device to cuda
enable bfloat16

"""



sandbox_api_key=os.getenv('AI_SANDBOX_KEY')
sandbox_endpoint="https://api-ai-sandbox.princeton.edu/"
sandbox_api_version="2024-02-01"

def text_prompt_call(model_to_be_used, system_prompt, user_prompt ):
    client_gpt = AzureOpenAI(
        api_key=sandbox_api_key,
        azure_endpoint = sandbox_endpoint,   
        api_version=sandbox_api_version # current api version not in preview
    )
    response = client_gpt.chat.completions.create(
        model=model_to_be_used,
        temperature=0.7, # temperature = how creative/random the model is in generating response - 0 to 1 with 1 being most creative
        max_tokens=1000, # max_tokens = token limit on context to send to the model
        messages=[
            {"role": "system", "content": system_prompt}, # describes model identity and purpose
            {"role": "user", "content": user_prompt}, # user prompt
        ]
    )
    return response.choices[0].message.content



api_key = os.getenv("contextual_apikey")
base_url = "https://api.contextual.ai/v1"
rerank_api_endpoint = f"{base_url}/rerank"
reranker = "ctxl-rerank-en-v1-instruct"
client = ContextualAI (api_key = api_key, base_url = base_url)


#instruction_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")
#instruction_tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

def update_instruction(query):
    system_prompt_instructions = """You are given a query and an instruction. Modify the instruction to prioritize the types of documents the query specifies. If the query asks for specific details (e.g., court level, timeframe, citation importance), incorporate those details into the instruction while maintaining its original structure. If the query does not specify particular document preferences, return "not applicable."

Example 1

Query: Find me older appellate court opinions on whether officers can always order passengers out of a car.
Instruction: Prioritize older appellate court opinions

Example 2

Query: Show me recent Supreme Court rulings on digital privacy rights.
Output: Prioritize recent Supreme Court opinions. 

Example 3

Query: Find legal opinions on self-defense laws.
Output: not applicable

Example 4
Query: Locate federal district court rulings from the last five years on employer vaccine mandates.
Output: Prioritize federal district court rulings from the last five years.

Example 5

Query: Show me influential appellate court decisions on contract interpretation.
Output: Prioritize influential appellate court decisions.

Example 6

Query: Find state supreme court cases that discuss the necessity of search warrants for vehicle searches.
Output: Prioritize state supreme court cases on search warrants for vehicle searches.

Example 7

Query: Show me legal opinions about landlord-tenant disputes.
Output: not applicable
"""


    """
    messages = [{"role": "system", "content": system_prompt_instructions}]
    messages.append({"role": "user", "content": "Query: " + query})
    example = instruction_tokenizer.apply_chat_template(messages, add_generation_prompt = True, tokenize=True,pad_to_multiple_of=8, do_pan_and_scan=True, return_tensors="pt")
    out = instruction_model.generate(example, max_new_tokens=50)
    updated = instruction_tokenizer.decode(out[0])
    updated = updated.split("<|im_start|>assistant")[-1].split("<|im_end|>")[0].strip()
    """

    updated = text_prompt_call("gpt-4o", system_prompt_instructions, query)

    print ("UPDATED INSTRUCTION HERE", updated)
    if updated == "not applicable":
        return "Prioritize Supreme Court opinions or opinions from higher courts. More recent, highly cited and published documents should also be weighted higher."

    return updated
    # oh god






def rerank_with_contextual_AI(results):
    instruction = "Prioritize Supreme Court opinions or opinions from higher courts. More recent, highly cited and published documents should also be weighted higher."
    #instruction = rerank_instruction
    query = results[0]["query"]
    docs = [i["text"] for i in results]
    metadata = [i["meta_data"] for i in results]

    # rewrite instruction if applicable
    instruction = update_instruction(query)

    rerank_response = client.rerank.create(
        query = query,
        instruction = instruction,
        documents = docs,
        metadata = metadata,
        model = reranker
    ).to_dict()
    print (rerank_response)
    # {'results': [{'index': 3, 'relevance_score': 0.39700255}, {'index': 2, 'relevance_score': 0.38903061}, {'index': 10, 'relevance_score': 0.36989796}, {'index': 8, 'relevance_score': 0.36830357}, {'index': 1, 'relevance_score': 0.36415816}, {'index': 11, 'relevance_score': 0.35778061}, {'index': 0, 'relevance_score': 0.35586735}, {'index': 4, 'relevance_score': 0.32589286}, {'index': 12, 'relevance_score': 0.32589286}, {'index': 7, 'relevance_score': 0.30931122}, {'index': 9, 'relevance_score': 0.30739796}, {'index': 13, 'relevance_score': 0.29145408}, {'index': 5, 'relevance_score': 0.2755102}, {'index': 6, 'relevance_score': 0.27295918}]}

    #ok, what next?
    reranked_docs = []
    for i in rerank_response["results"]:
        reranked_docs.append(results[i["index"]])
        reranked_docs[-1]["relevance_score"] = i["relevance_score"]
    return reranked_docs

def format_metadata_for_reranking(metadata):
    try:
        out =  metadata["case_name"] + ", " + metadata["court_short_name"] + ", " + "year: " + metadata["date_filed"] + " citation count: " + str(metadata["citation_count"]) + ", precedential status " + metadata["precedential_status"]
    except:
        out = ""
    return out


def format_metadata_as_str(metadata):
    try:
        out =  metadata["case_name"] + ", " + metadata["court_short_name"] + ", " + metadata["date_filed"] + ", precedential status " + metadata["precedential_status"]
    except:
        out = ""
    return out

def show_user_query(user_message, history):
    '''
    Displays user query in the chatbot and removes from textbox.
    :param user_message: user query inputted.
    :param history: 2D array representing chatbot-user conversation.
    :return:
    '''
    return "", history + [[user_message, None]]



def run_extractive_qa(query, contexts):
    extracted_passages = extractive_qa([{"question": query, "context": context} for context in contexts])        
    return extracted_passages


@spaces.GPU(duration=15)
def respond_user_query(history):
    '''
    Overwrite the value of current pairing's history with generated text
    and displays response character-by-character with some lag.
    :param history: 2D array of chatbot history filled with user-bot interactions
    :return: history updated with bot's latest message.
    '''
    start_time_global = time.time()

    query = history[0][0]
    start_time_global = time.time()

    responses = run_retrieval(query)
    print("--- run retrieval: %s seconds ---" % (time.time() - start_time_global))
    #print (responses)

    contexts = [individual_response["text"] for individual_response in responses][:NUM_RESULTS]
    extracted_passages = run_extractive_qa(query, contexts)

    for individual_response, extracted_passage in zip(responses, extracted_passages):
        start, end = extracted_passage["start"], extracted_passage["end"]
        # highlight text
        text = individual_response["text"]
        text = text[:start] + " **" + text[start:end] + "** " + text[end:]

        # display queries in interface
        formatted_response = "##### "
        if individual_response["meta_data"]:
            formatted_response += individual_response["meta_data"]
        else:
            formatted_response += individual_response["opinion_idx"]
        formatted_response += "\n" + text + "\n\n"
        history = history + [[None, formatted_response]]
    print("--- Extractive QA: %s seconds ---" % (time.time() - start_time_global))

    return [history, responses]

def switch_to_reviewing_framework():
    '''
    Replaces textbox for entering user query with annotator review select.
    :return: updated visibility for textbox and radio button props.
    '''
    return gr.Textbox(visible=False), gr.Dataset(visible=False), gr.Textbox(visible=True, interactive=True), gr.Button(visible=True)

def reset_interface():
    '''
    Resets chatbot interface to original position where chatbot history,
     reviewing is invisbile is empty and user input textbox is visible.
    :return: textbox visibility, review radio button invisibility,
    next_button invisibility, empty chatbot
    '''

    # remove tmp highlighted word documents
    #for fn in os.listdir("tmp-docs"):
    #    os.remove(os.path.join("tmp-docs", fn))
    return gr.Textbox(visible=True), gr.Button(visible=False), gr.Textbox(visible=False, value=""), None, gr.JSON(visible=False, value=[]), gr.Dataset(visible=True)

###################################################
def mark_like(response_json, like_data: gr.LikeData):
    index_of_msg_reviewed = like_data.index[0] - 1  # 0-indexing
    # add liked information to res
    response_json[index_of_msg_reviewed]["is_msg_liked"] = like_data.liked
    return response_json

"""
def save_json(name: str, greetings: str) -> None:

"""
def register_review(history, additional_feedback, response_json):
    '''
    Writes user review to output file.
    :param history: 2D array representing bot-user conversation so far.
    :return: None, writes to output file.
    ''' 

    res = { "user_query": history[0][0], 
        "responses": response_json,
        "timestamp": datetime.now().strftime('%Y-%m-%d %H:%M:%S'), 
        "additional_feedback": additional_feedback
    }
    print (res)


# load search functionality here


def load_bm25():
    stemmer = Stemmer.Stemmer("english")
    retriever = bm25s.BM25.load("NJ_index_LLM_chunking", mmap=False)
    return retriever, stemmer # titles

def run_bm25(query):
    query_tokens = bm25s.tokenize(query, stemmer=stemmer)
    results, scores = retriever.retrieve(query_tokens, k=5)
    return results[0]

def load_faiss_index(embeddings):
    nb, d = embeddings.shape # database size, dimension
    faiss_index = faiss.IndexFlatL2(d)   # build the index
    faiss_index.add(embeddings) # add vectors to the index
    return faiss_index

#@spaces.GPU(duration=10)
def run_dense_retrieval(query):
    if "NV" in model_name:
        query_prefix = "Instruct: Given a question, retrieve passages that answer the question\nQuery: "
        max_length = 32768
        print (query)
        with torch.no_grad():
            query_embeddings = model.encode([query], instruction=query_prefix, max_length=max_length)
            query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
        query_embeddings = query_embeddings.cpu().numpy()
    return query_embeddings


def load_NJ_caselaw():
    if os.path.exists("/scratch/gpfs/ds8100/datasets/NJ_opinions_modernbert_splitter.jsonl"):
        df = pd.read_json("/scratch/gpfs/ds8100/datasets/NJ_opinions_modernbert_splitter.jsonl", lines=True)
    else:
        df = pd.read_json("NJ_opinions_modernbert_splitter.jsonl", lines=True)
    titles, chunks = [],[]

    for i, row in df.iterrows():
        texts = [i for i in row["texts"] if len(i.split()) > 25 and len(i.split()) < 750]
        texts = [" ".join(i.strip().split()) for i in texts]
        chunks.extend(texts)
        titles.extend([row["id"]] * len(texts))
    ids = list(range(len(titles)))
    assert len(ids) == len(titles) == len(chunks)
    return ids, titles, chunks


def run_retrieval(query):
    query = " ".join(query.split())

    print ("query", query)
    """
    indices_bm25 = run_bm25(query)
    scores_embeddings, indices_embeddings = run_dense_retrieval(query)
    indices = list(set(indices_bm25).union(indices_embeddings))
    #docs = [{"id":i, "text":chunks[i]} for i in indices]
    docs = [chunks[i] for i in indices]
    results_reranking = rerank(query, docs, indices) #results = [{"doc":docs[i], "score":probs[i], "id":indices[i]} for i in argsort]
    """
    start_time = time.time()
    query_embeddings = run_dense_retrieval(query)
    np.save("test_query_embeddings", query_embeddings)
    print("--- Nvidia Embedding: %s seconds ---" % (time.time() - start_time))
    D, I = faiss_index.search(query_embeddings, 45)
    print("--- Faiss retrieval: %s seconds ---" % (time.time() - start_time))

    scores_embeddings = D[0]
    indices_embeddings = I[0]

    docs = [chunks[i] for i in indices_embeddings]
    results = [{"id":i, "score":j} for i,j in zip(indices_embeddings, scores_embeddings)]

    out_dict = []
    covered = set()
    for item in results:
        tmp = {}
        index = item["id"]
        tmp["query"] = query
        tmp["index"] = index #indices[index]
        tmp["NV_score"] = item["score"]
        tmp["opinion_idx"] = str(titles[index])

        # only recover one paragraph / opinion
        if tmp["opinion_idx"] in covered:
            continue
        covered.add(tmp["opinion_idx"])

        if tmp["opinion_idx"] in metadata:
            tmp["meta_data"] = format_metadata_for_reranking(metadata[tmp["opinion_idx"]])
        else:
            tmp["meta_data"] = ""        
        # so far so good
        tmp["text"] = chunks[tmp["index"]]
        out_dict.append(tmp)
    print (out_dict)
    # and now, rerank
    #out_dict = rerank_with_contextual_AI(out_dict)
    return out_dict


NUM_RESULTS = 5
model_name = 'nvidia/NV-Embed-v2' 

device = torch.device("cuda")
#device = torch.device("cpu")
#device = torch.device("mps")

extractive_qa = pipeline("question-answering", model="ai-law-society-lab/extractive-qa-model", tokenizer="FacebookAI/roberta-large", device_map="auto", token=os.getenv('hf_token'))
ids, titles, chunks = load_NJ_caselaw()

#@profile
def profiling_faiss_index():
    ds = load_dataset("ai-law-society-lab/NJ_embeddings", token=os.getenv('hf_token'))["train"]
    print (sys.getsizeof(ds))
    ds = ds.with_format("np")
    print (sys.getsizeof(ds))
    print (ds)
    faiss_index = load_faiss_index(ds["embeddings"])
    #ds.add_faiss_index(column='embeddings')
    #print (sys.getsizeof(faiss_index))

    return faiss_index
faiss_index = profiling_faiss_index()


with open("NJ_caselaw_metadata.json") as f:
    metadata = json.load(f)

 

def load_embeddings_model(model_name = "intfloat/e5-large-v2"):
    if "NV" in model_name:
        model = AutoModel.from_pretrained('nvidia/NV-Embed-v2', trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto")
        #model = AutoModel.from_pretrained('nvidia/NV-Embed-v2', trust_remote_code=True, torch_dtype=torch.float16, device_map="auto")        
        model.eval()
        return model

if "NV" in model_name:
    model = load_embeddings_model(model_name=model_name)


examples = ["Can officers always order a passenger out of a car?","Find me briefs about credential searches", "Can police search an impounded car without a warrant?", "State is arguing State v. Carty is not good law"]

css = """
.svelte-i3tvor {visibility: hidden}
.row.svelte-hrj4a0.unequal-height {
    align-items: stretch !important
}
"""

with gr.Blocks(css=css, theme = gr.themes.Monochrome(primary_hue="pink",)) as demo:
    chatbot = gr.Chatbot(height="45vw", autoscroll=False)
    query_textbox = gr.Textbox()
    #rerank_instruction = gr.Textbox(label="Rerank Instruction Prompt", value="If not otherwise specified in the query, prioritize Supreme Court opinions or opinions from higher courts. More recent, highly cited and published documents should also be weighted higher, unless otherwise specified in the query.")
    examples = gr.Examples(examples, query_textbox)
    response_json = gr.JSON(visible=False, value=[])
    print (response_json)
    chatbot.like(mark_like, response_json, response_json) 

    feedback_textbox = gr.Textbox(label="Additional feedback?", visible=False)
    next_button = gr.Button(value="Submit Feedback", visible=False)

    query_textbox.submit(show_user_query, [query_textbox, chatbot], [query_textbox, chatbot], queue=False).then(
        respond_user_query, chatbot, [chatbot, response_json]).then(
        switch_to_reviewing_framework, None, [query_textbox, examples.dataset, feedback_textbox, next_button]
    ) 

    # Handle page reset and review save in database
    next_button.click(register_review, [chatbot, feedback_textbox, response_json], None).then(
        reset_interface, None, [query_textbox, next_button, feedback_textbox, chatbot, response_json, examples.dataset])

# Launch application
demo.launch()