Update app.py

app.py

@@ -1,15 +1,73 @@
-import streamlit as st
+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
 
-# Hugging Face Deployment Compatibility
+# Load environment variables
+load_dotenv()
+
+# Initialize FastAPI
+app = FastAPI()
+
+# API Keys
+PINECONE_API_KEY = os.getenv("pcsk_7QKLEa_RCEnZawP7NgzW9FhfbjX8szFb66WYpcturDk5EHpvHHH97REiXKcgyqrhuuYH1d")
+PINECONE_ENV = os.getenv("us-east-1")
+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 & Pinecone
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+pc = Pinecone(api_key=PINECONE_API_KEY)
+vector_store = Pinecone.from_existing_index(index_name=INDEX_NAME, embedding=embeddings)
+
+# Load LLM & Memory
+llm = ChatAnthropic(model="claude-2", temperature=0)
+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="Agentic RAG Legal Assistant", layout="wide")
 
-# Load background and sidebar images
-bg_image = "https://source.unsplash.com/1600x900/?law,court"  # Background image
-sidebar_image = "https://source.unsplash.com/400x600/?law,justice"  # Sidebar image
+bg_image = "https://source.unsplash.com/1600x900/?law,court"
+sidebar_image = "https://source.unsplash.com/400x600/?law,justice"
 
-# Custom CSS for background styling
 st.markdown(
     f"""
     <style>
@@ -26,23 +84,17 @@ st.markdown(
     unsafe_allow_html=True,
 )
 
-# Sidebar Title
 st.sidebar.title("⚖️ Legal AI Assistant")
 st.sidebar.markdown("Your AI-powered legal research assistant.")
 
-# Main Heading
 st.markdown("# 🏛️ Agentic RAG Legal Assistant")
 st.markdown("### Your AI-powered assistant for legal research and case analysis.")
 
-# Initialize conversation history
 if "chat_history" not in st.session_state:
     st.session_state.chat_history = []
 
-# User input
 user_query = st.text_input("🔍 Enter your legal question:", "")
-
-# FastAPI backend URL
-API_URL = "http://127.0.0.1:8000/query/"  # Change this to your deployed FastAPI URL
+API_URL = "http://127.0.0.1:8000/query/"
 
 if st.button("Ask AI") and user_query:
     with st.spinner("Fetching response..."):
@@ -53,10 +105,8 @@ if st.button("Ask AI") and user_query:
         except Exception as e:
             ai_response = f"Error: {e}"
     
-    # Update chat history
     st.session_state.chat_history.append((user_query, ai_response))
 
-# Display chat history
 st.markdown("---")
 st.markdown("### 📜 Chat History")
 for user_q, ai_r in st.session_state.chat_history:
@@ -64,6 +114,5 @@ for user_q, ai_r in st.session_state.chat_history:
     st.markdown(f"**🤖 AI:** {ai_r}")
     st.markdown("---")
 
-# Footer
 st.markdown("---")
-st.markdown("🚀 Powered by OpenAI, Pinecone, and LangChain.")
+st.markdown("🚀 Powered by Anthropic Claude, Pinecone, and LangChain.")