Delete AI_legal

AI_legal/.streamlit/config.toml

@@ -1,6 +0,0 @@
-[theme]
-base="dark"
-primaryColor="#4ba3ff"
-backgroundColor="#0e1721"
-secondaryBackgroundColor="#142433"
-font = "sans serif"

AI_legal/Ingest.py

@@ -1,61 +0,0 @@
-import ray
-import logging
-from langchain_community.document_loaders import DirectoryLoader
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from faiss import IndexFlatL2  # Assuming using L2 distance for simplicity
-
-# Initialize Ray
-ray.init()
-
-# Set up basic configuration for logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-# Load documents with logging
-logging.info("Loading documents...")
-loader = DirectoryLoader('data', glob="./*.txt")
-documents = loader.load()
-
-# Extract text from documents and split into manageable texts with logging
-logging.info("Extracting and splitting texts from documents...")
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=200)
-texts = []
-for document in documents:
-    if hasattr(document, 'get_text'):
-        text_content = document.get_text()  # Adjust according to actual method
-    else:
-        text_content = ""  # Default to empty string if no text method is available
-
-    texts.extend(text_splitter.split_text(text_content))
-
-# Define embedding function
-def embedding_function(text):
-    embeddings_model = HuggingFaceEmbeddings(model_name="law-ai/InLegalBERT")
-    return embeddings_model.embed_query(text)
-
-# Create FAISS index for embeddings
-index = IndexFlatL2(768)  # Dimension of embeddings, adjust as needed
-
-# Assuming docstore as a simple dictionary to store document texts
-docstore = {i: text for i, text in enumerate(texts)}
-index_to_docstore_id = {i: i for i in range(len(texts))}
-
-# Initialize FAISS
-faiss_db = FAISS(embedding_function, index, docstore, index_to_docstore_id)
-
-# Process and store embeddings
-logging.info("Storing embeddings in FAISS...")
-for i, text in enumerate(texts):
-    embedding = embedding_function(text)
-    faiss_db.add_documents([embedding])
-
-# Exporting the vector embeddings database with logging
-logging.info("Exporting the vector embeddings database...")
-faiss_db.save_local("ipc_embed_db")
-
-# Log a message to indicate the completion of the process
-logging.info("Process completed successfully.")
-
-# Shutdown Ray after the process
-ray.shutdown()

AI_legal/app.py

@@ -1,127 +0,0 @@
-import time
-import os
-import streamlit as st
-from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain.prompts import PromptTemplate
-from langchain.memory import ConversationBufferWindowMemory
-from langchain.chains import ConversationalRetrievalChain
-from langchain_together import Together
-
-from footer import footer
-
-# Set the Streamlit page configuration and theme
-st.set_page_config(page_title="BharatLAW", layout="centered")
-
-# Display the logo image
-col1, col2, col3 = st.columns([1, 30, 1])
-with col2:
-    st.image("https://github.com/Nike-one/BharatLAW/blob/master/images/banner.png?raw=true", use_column_width=True)
-
-def hide_hamburger_menu():
-    st.markdown("""
-        <style>
-            #MainMenu {visibility: hidden;}
-            footer {visibility: hidden;}
-        </style>
-        """, unsafe_allow_html=True)
-
-hide_hamburger_menu()
-
-# Initialize session state for messages and memory
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-if "memory" not in st.session_state:
-    st.session_state.memory = ConversationBufferWindowMemory(k=2, memory_key="chat_history", return_messages=True)
-
-@st.cache_resource
-def load_embeddings():
-    """Load and cache the embeddings model."""
-    return HuggingFaceEmbeddings(model_name="law-ai/InLegalBERT")
-
-embeddings = load_embeddings()
-db = FAISS.load_local("ipc_embed_db", embeddings, allow_dangerous_deserialization=True)
-db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3})
-
-prompt_template = """
-<s>[INST]
-As a legal chatbot specializing in the Indian Penal Code, you are tasked with providing highly accurate and contextually appropriate responses. Ensure your answers meet these criteria:
-- Respond in a bullet-point format to clearly delineate distinct aspects of the legal query.
-- Each point should accurately reflect the breadth of the legal provision in question, avoiding over-specificity unless directly relevant to the user's query.
-- Clarify the general applicability of the legal rules or sections mentioned, highlighting any common misconceptions or frequently misunderstood aspects.
-- Limit responses to essential information that directly addresses the user's question, providing concise yet comprehensive explanations.
-- Avoid assuming specific contexts or details not provided in the query, focusing on delivering universally applicable legal interpretations unless otherwise specified.
-- Conclude with a brief summary that captures the essence of the legal discussion and corrects any common misinterpretations related to the topic.
-
-CONTEXT: {context}
-CHAT HISTORY: {chat_history}
-QUESTION: {question}
-ANSWER:
-- [Detail the first key aspect of the law, ensuring it reflects general application]
-- [Provide a concise explanation of how the law is typically interpreted or applied]
-- [Correct a common misconception or clarify a frequently misunderstood aspect]
-- [Detail any exceptions to the general rule, if applicable]
-- [Include any additional relevant information that directly relates to the user's query]
-</s>[INST]
-"""
-
-
-
-prompt = PromptTemplate(template=prompt_template,
-                        input_variables=['context', 'question', 'chat_history'])
-
-api_key = os.getenv('TOGETHER_API_KEY')
-llm = Together(model="mistralai/Mixtral-8x22B-Instruct-v0.1", temperature=0.5, max_tokens=1024, together_api_key=api_key)
-
-qa = ConversationalRetrievalChain.from_llm(llm=llm, memory=st.session_state.memory, retriever=db_retriever, combine_docs_chain_kwargs={'prompt': prompt})
-
-def extract_answer(full_response):
-    """Extracts the answer from the LLM's full response by removing the instructional text."""
-    answer_start = full_response.find("Response:")
-    if answer_start != -1:
-        answer_start += len("Response:")
-        answer_end = len(full_response)
-        return full_response[answer_start:answer_end].strip()
-    return full_response
-
-def reset_conversation():
-    st.session_state.messages = []
-    st.session_state.memory.clear()
-
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.write(message["content"])
-
-
-input_prompt = st.chat_input("Say something...")
-if input_prompt:
-    with st.chat_message("user"):
-        st.markdown(f"**You:** {input_prompt}")
-
-    st.session_state.messages.append({"role": "user", "content": input_prompt})
-    with st.chat_message("assistant"):
-        with st.spinner("Thinking 💡..."):
-            result = qa.invoke(input=input_prompt)
-            message_placeholder = st.empty()
-            answer = extract_answer(result["answer"])
-
-            # Initialize the response message
-            full_response = "⚠️ **_Gentle reminder: We generally ensure precise information, but do double-check._** \n\n\n"
-            for chunk in answer:
-                # Simulate typing by appending chunks of the response over time
-                full_response += chunk
-                time.sleep(0.02)  # Adjust the sleep time to control the "typing" speed
-                message_placeholder.markdown(full_response + " |", unsafe_allow_html=True)
-
-        st.session_state.messages.append({"role": "assistant", "content": answer})
-
-        if st.button('🗑️ Reset All Chat', on_click=reset_conversation):
-            st.experimental_rerun()
-
-
-
-# Define the CSS to style the footer
-footer()
-
-

AI_legal/footer.py

@@ -1,68 +0,0 @@
-import streamlit as st
-from htbuilder import HtmlElement, div, a, p, img, styles
-from htbuilder.units import percent, px
-
-
-def image(src_as_string, **style):
-    return img(src=src_as_string, style=styles(**style))
-
-
-def link(link, text, **style):
-    return a(_href=link, _target="_blank", style=styles(**style))(text)
-
-
-def layout(*args):
-
-    style = """
-    <style>
-      # MainMenu {visibility: hidden;}
-      footer {visibility: hidden;}
-     .stApp { bottom: 40px; }
-     .st-emotion-cache-139wi93 {
-    width: 100%;
-    padding: 1rem 1rem 15px;
-    max-width: 46rem;
-    }
-    </style>
-    """
-
-    style_div = styles(
-        position="fixed",
-        left=0,
-        bottom=0,
-        margin=px(0, 0, 0, 0),
-        width=percent(100),
-        color="white",
-        text_align="center",
-        height="auto",
-        opacity=1
-    )
-
-    body = p()
-    foot = div(
-        style=style_div
-    )(
-        body
-    )
-
-    st.markdown(style, unsafe_allow_html=True)
-
-    for arg in args:
-        if isinstance(arg, str):
-            body(arg)
-
-        elif isinstance(arg, HtmlElement):
-            body(arg)
-
-    st.markdown(str(foot), unsafe_allow_html=True)
-
-
-def footer():
-    myargs = [
-        "Made with ❤️ by Nikhil, Mihir, Nilay",
-    ]
-    layout(*myargs)
-
-
-if __name__ == "__main__":
-    footer()

AI_legal/requirements.txt

@@ -1,13 +0,0 @@
-langchain==0.1.15
-pypdf
-transformers==4.39.3
-sentence-transformers
-accelerate
-faiss-cpu
-streamlit==1.33.0
-langchain-fireworks
-einops
-langchain-together
-ray==2.10.0
-unstructured
-htbuilder