src/streamlit_app.py

import os

os.environ.setdefault("HF_HOME", "/tmp/huggingface")
os.environ.setdefault("TRANSFORMERS_CACHE", "/tmp/huggingface")
os.environ.setdefault("HF_HUB_CACHE", "/tmp/huggingface")
os.environ.setdefault("SENTENCE_TRANSFORMERS_HOME", "/tmp/huggingface/st_models")

import streamlit as st
import openai
from collections import deque
from sentence_transformers import SentenceTransformer
from pinecone import Pinecone
import re

# Setup (exact hardcoded keys you provided)
client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
index = pc.Index("legal-ai")
model = SentenceTransformer('all-mpnet-base-v2')
chat_history = deque(maxlen=10)  # last 5 pairs = 10 messages
ll_model = 'gpt-4o-mini'

st.title("AI Legal Assistant ⚖️")

if "history" not in st.session_state:
    st.session_state.history = deque(maxlen=10)

def get_rewritten_query(user_query):
    hist = list(st.session_state.history)[-4:]
    hist_text = "\n".join(f"{m['role']}: {m['content']}" for m in hist)
    messages = [
        {"role": "system", "content":
         "You are a legal assistant that rewrites user queries into clear, context-aware queries for vector DB lookup. If its already clear then dont rewite"},
        {"role": "user", "content":
         f"History:\n{hist_text}\n\nNew query:\n{user_query}\n\n"
         "Rewrite if needed for clarity/search purposes. Otherwise, repeat exactly."}
    ]
    try:
        resp = client.chat.completions.create(
            model=ll_model,
            messages=messages,
            temperature=0.1,
            max_tokens=400
        )
        rewritten = resp.choices[0].message.content.strip()
    except Exception as e:
        st.error(f"Rewrite error: {e}")
        rewritten = user_query
    # st.session_state.history.append({"role": "assistant", "content": f"🔁 Rewritten query: {rewritten}"})
    return rewritten

def retrieve_documents(query, top_k=10):
    emb = model.encode(query).tolist()
    try:
        return index.query(vector=emb, top_k=top_k, include_metadata=True)['matches']
    except Exception as e:
        st.error(f"Retrieve error: {e}")
        return []


def clean_chunk_id(cid: str) -> str:
    """Beautify chunk_id by replacing underscores/dashes with spaces and capitalizing words."""
    # Remove any trailing '_chunk_xxx' stuff
    cid = re.sub(r'_chunk.*$', '', cid)
    # Replace _ and - with spaces
    cid = cid.replace("_", " ").replace("-", " ")
    # Capitalize each word
    cid = " ".join(word.capitalize() for word in cid.split())
    return cid



def generate_response(user_query, docs):
    # --- Collect context ---
    context = "\n\n---\n\n".join(d['metadata']['text'] for d in docs)

    # --- Build human-friendly sources + mapping ---
    source_links = {}
    for d in docs:
        meta = d['metadata']
        src = meta.get("source", "unknown").lower()
        cid = meta.get("chunk_id", "")
        text_preview = " ".join(meta.get("text", "").split()[:30])

        if src in ["constitution"]:
            display_name = f"Constitution ({clean_chunk_id(cid)})"

        elif src in ["fbr_ordinance", "ordinance", "tax_ordinance"]:
            display_name = f"Tax Ordinance ({clean_chunk_id(cid)})"

        elif src in ["case_law", "case", "tax_case"]:
            display_name = f"Case Law: {text_preview}..."

        else:
            display_name = f"{src.title()} ({clean_chunk_id(cid)})"

        source_links[display_name] = meta.get("text", "")

    # Deduplicate
    source_links = dict(sorted(source_links.items()))

    # --- System prompt ---
    messages = [
        {"role": "system", "content":
         "You are a helpful legal assistant. Use the provided context from documents to answer the user's question. "
         "At the end of your answer, write a single line starting with 'Source: ' followed by the sources used. "
         "Formatting rules:\n"
         "- For Constitution / Ordinances: show the clean chunk id, no underscores/dashes, capitalized words.\n"
         "- For Case law: ignore chunk id, instead show first ~30 words of the case text.\n"
         "- Do not use technical terms like 'chunk'. Present sources in a human-friendly way.\n"
         "If multiple are used, separate them with commas."}
    ]

    messages.extend(st.session_state.history)

    messages.append({"role": "user", "content": f"Context:\n{context}\n\n"
                   f"Sources:\n{', '.join(source_links.keys())}\n\n"
                   f"Question:\n{user_query}"})
    try:
        resp = client.chat.completions.create(
            model=ll_model,
            messages=messages,
            temperature=0.1,
            max_tokens=900
        )
        reply = resp.choices[0].message.content.strip()
    except Exception as e:
        st.error(f"Response error: {e}")
        reply = "Sorry, I encountered an error generating the answer."

    # Optional: force clean source line if LLM misses it
    if source_links:
        clean_sources = ", ".join(source_links.keys())
        if "Source:" not in reply:
            reply += f"\n\nSource: {clean_sources}"

    # Save reply into history
    st.session_state.history.append({"role": "assistant", "content": reply})

    # --- Render in Streamlit ---
    st.markdown(reply)

    # Add expandable sources
    if source_links:
        st.write("### Sources")
        for name, text in source_links.items():
            with st.expander(name):
                st.write(text)

    return reply




# Chat UI
with st.form("chat_input", clear_on_submit=True):
    user_input = st.text_input("You:", "")
    submit = st.form_submit_button("Send")

if submit and user_input:
    st.session_state.history.append({"role": "user", "content": user_input})
    rewritten = get_rewritten_query(user_input)
    docs = retrieve_documents(rewritten)
    assistant_reply = generate_response(rewritten, docs)

c = 0
# Display history
st.markdown("---")
for msg in reversed(st.session_state.history):
    c+=1
    if msg["role"] == "user":
        st.markdown(f"**You:** {msg['content']}")
    else:
        st.markdown(f"**Legal Assistant:** {msg['content']}")
    if c ^ 1: st.markdown("---")