app.py

import os
import subprocess
import sys

# Function to install missing dependencies
def install_dependencies():
    try:
        # Check if langchain is installed
        import langchain
    except ImportError:
        # Install langchain if not found
        print("Installing langchain...")
        subprocess.check_call([sys.executable, "-m", "pip", "install", "langchain"])
    try:
        # Check if other dependencies are installed
        import streamlit
        import fastapi
        import requests
        import datasets
        import pinecone
        import sentence_transformers
        import dotenv
        import PIL
    except ImportError as e:
        # Install missing dependencies
        print(f"Installing missing dependency: {e.name}")
        subprocess.check_call([sys.executable, "-m", "pip", "install", e.name])

# Install dependencies before proceeding
install_dependencies()

import os
import requests
import streamlit as st
from fastapi import FastAPI, HTTPException
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatAnthropic
from langchain.vectorstores import Pinecone
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.memory import ConversationBufferMemory
from datasets import load_dataset
from dotenv import load_dotenv
from pinecone import Pinecone
from PIL import Image
from langchain_community.vectorstores import Pinecone
from pinecone import Pinecone as PineconeClient
from anthropic import Anthropic

# Load environment variables
load_dotenv()

# Initialize FastAPI
app = FastAPI()

# API Keys
import os
from dotenv import load_dotenv

PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
PINECONE_ENV = os.getenv("PINECONE_ENV")
INDEX_NAME = "agenticrag"

if not PINECONE_API_KEY:
    raise ValueError("Pinecone API Key is missing. Please set it in environment variables.")

#Initialize Hugging Face Embeddings
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

vector_store = Pinecone.from_existing_index(index_name=INDEX_NAME, embedding=embeddings)


# Load LLM & Memory

class ChatAnthropicWithTokenCount(ChatAnthropic):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.anthropic_client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))

    def count_tokens(self, text: str) -> int:
        return self.anthropic_client.count_tokens(text)

llm = ChatAnthropicWithTokenCount(
    model="claude-2",
    temperature=0,
    ANTHROPIC_API_KEY=os.getenv("ANTHROPIC_API_KEY")
)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)


# Build RAG Chain
qa_chain = ConversationalRetrievalChain.from_llm(
    llm=llm,
    retriever=vector_store.as_retriever(),
    memory=memory,
    return_source_documents=True
)

@app.post("/query/")
async def query_agent(query: str):
    try:
        response = qa_chain.run(query)
        return {"response": response}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/")
def read_root():
    return {"message": "Welcome to the Agentic RAG Legal Assistant!"}

# Load dataset
dataset = load_dataset("c4lliope/us-congress")
chunks = [str(text) for text in dataset['train']['text']]
embedding_vectors = embeddings.embed_documents(chunks)
pinecone_data = [(str(i), embedding_vectors[i], {"text": chunks[i]}) for i in range(len(chunks))]
vector_store.upsert(vectors=pinecone_data)

# Streamlit UI
st.set_page_config(page_title="LegalAI Assistant", layout="wide")

bg_image = "https://source.unsplash.com/1600x900/?law,court"
sidebar_image = "https://source.unsplash.com/400x600/?law,justice"

st.markdown(
    f"""
    <style>
    .stApp {{
        background: url({bg_image}) no-repeat center center fixed;
        background-size: cover;
    }}
    .sidebar .sidebar-content {{
        background: url({sidebar_image}) no-repeat center center;
        background-size: cover;
    }}
    </style>
    """,
    unsafe_allow_html=True,
)

st.sidebar.title("⚖️ Legal AI Assistant")
st.sidebar.markdown("Your AI-powered legal research assistant.")

st.markdown("# 🏛️ Agentic RAG Legal Assistant")
st.markdown("### Your AI-powered assistant for legal research and case analysis.")

if "chat_history" not in st.session_state:
    st.session_state.chat_history = []

user_query = st.text_input("🔍 Enter your legal question:", "")
API_URL = "http://127.0.0.1:8000/query/"

if st.button("Ask AI") and user_query:
    with st.spinner("Fetching response..."):
        try:
            response = requests.post(API_URL, json={"query": user_query})
            response_json = response.json()
            ai_response = response_json.get("response", "Error: No response received.")
        except Exception as e:
            ai_response = f"Error: {e}"
    
    st.session_state.chat_history.append((user_query, ai_response))

st.markdown("---")
st.markdown("### 📜 Chat History")
for user_q, ai_r in st.session_state.chat_history:
    st.markdown(f"**🧑‍⚖️ You:** {user_q}")
    st.markdown(f"**🤖 AI:** {ai_r}")
    st.markdown("---")

st.markdown("---")
st.markdown("🚀 Powered by Anthropic Claude, Pinecone, and LangChain.")