app.py

import streamlit as st
from bs4 import BeautifulSoup
import requests
import os
import time
from openai import OpenAI
import google.generativeai as genai

genai.configure(api_key='AIzaSyBE9XAwJiAs6xY2UukvGYsy0ghtxA1F2q8')

generation_config = {
  "temperature": 0,
  "top_p": 0.95,
  "top_k": 64,
  "max_output_tokens": 8192,
  "response_mime_type": "text/plain",
}
     
headers = {
    "User-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}

proxies = {"http": os.getenv("HTTP_PROXY")}



@st.cache_data(ttl=3600)
def search_legal_cases(query, num_results=10):
    url = "https://scholar.google.com/scholar?hl=en&as_sdt=6"
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.3"
    }

    params = {
        "q": query,
        "hl": "en",
        "num": num_results,
        "as_sdt": "4",  # This parameter filters the search results to legal cases
    }

    response = requests.get(url, proxies=proxies, headers=headers, params=params)
    soup = BeautifulSoup(response.text, "html.parser")

    results = []
    for result in soup.find_all("div", class_="gs_ri"):
        title = result.find("h3", class_="gs_rt").text
        base_url = "https://scholar.google.com"
        link = base_url + result.find("a")["href"]
        citation = result.find("div", class_="gs_a").text.replace(" - Google Scholar", "")
        results.append((title, link, citation))

    return results

@st.cache_data(ttl=3600)
def extract_text_from_link(url):
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.3"
    }
    
    response = requests.get(url, headers=headers, proxies=proxies)
    soup = BeautifulSoup(response.content, "html.parser")
    
    text = soup.get_text(separator="\n")
    return text


# @st.cache_data(ttl=3600)
# def get_summary(text):
#    client = OpenAI(api_key='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')

#    completion = client.chat.completions.create(
#      model="gpt-4o",
#      messages=[
#        {"role": "system", "content": f'''You are a law professor specialized in legal writing and legal research. 
#                    When presented with a case by a user please summarize it according to the following requirements:
#                    Name of the court.
#                    Facts (name of the parties, what happened factually).
#                    Procedural history (what happened in the past procedurally, what were prior judgements). 
#                    Issues (what is in dispute).
#                    Holding (the applied rule of law). 
#                    Rationale (reasons for the holding).
#                    Decision (what did the court decide, e.g. affirmed, overruled).
#                    Other opinions (if there are any dissenting or concurring opinions, summarize majority opinion, dissenting opinion and concurring opinion).
#                    Cases cited (which cases the court cited and how it treated them).'''},
#        {"role": "user", "content": f"Please summarize this case according to the instructions: {text}. "}
#      ]
#    )
   
#    return completion.choices[0].message.content


def get_summary(text):
    model = genai.GenerativeModel('gemini-1.5-flash', generation_config=generation_config)
    response = model.generate_content(f'''You are a law professor specialized in legal writing and legal research. 
                   When presented with a case by a user please summarize it according to the following requirements:
                   Name of the court.
                   Facts (name of the parties, what happened factually).
                   Procedural history (what happened in the past procedurally, what were prior judgements). 
                   Issues (what is in dispute).
                   Holding (the applied rule of law). 
                   Rationale (reasons for the holding).
                   Decision (what did the court decide, e.g. affirmed, overruled).
                   Other opinions (if there are any dissenting or concurring opinions, summarize majority opinion, dissenting opinion and concurring opinion).
                   Cases cited (which cases the court cited and how it treated them).
                   Here is the text of the court decision: {text}''', 
                                      stream=False)
    return response

       
st.write("\n")
st.write("\n")
search_query = st.text_input("case name, e.g. brown v board supreme, 372 US 335, google v oracle appeal")

if search_query:
    with st.spinner("Searching for cases..."):
        results = search_legal_cases(search_query)
    if results:
        title, link, citation = results[0]
        st.write("Title:\n", title)
        #st.write("Link:\n", link)
        st.write("Citation:\n", citation)
        #with st.spinner("Extracting text from case / Generating summary"):
        text = extract_text_from_link(link)
        #st.write(text)  # Optionally display the extracted text
        
        summary = get_summary(text)
        #st.write(response)
        st.write(summary.text)
        #for chunk in summary:
          #st.write(chunk.text)
          #st.write("_"*80)

        
    else:
        st.write("No results found.")