app.py

import streamlit as st
import faiss
import numpy as np
import pickle
import json
import torch
from sentence_transformers import SentenceTransformer
from transformers import pipeline, RagTokenizer, RagRetriever, RagSequenceForGeneration

# ========================
# File Names & Model Names
# ========================
INDEX_FILE = "faiss_index.index"
CHUNKS_FILE = "chunks.pkl"
CURATED_QA_FILE = "curated_qa_pairs.json"

EMBEDDING_MODEL_NAME = "all-MiniLM-L6-v2"
QA_MODEL_NAME = "deepset/roberta-large-squad2"

# ========================
# Loading Functions (cached)
# ========================
@st.cache_resource
def load_index_and_chunks():
    try:
        index = faiss.read_index(INDEX_FILE)
        with open(CHUNKS_FILE, "rb") as f:
            chunks = pickle.load(f)
        return index, chunks
    except Exception as e:
        st.error(f"Error loading FAISS index and chunks: {e}")
        return None, None

@st.cache_resource
def load_embedding_model():
    return SentenceTransformer(EMBEDDING_MODEL_NAME)

@st.cache_resource
def load_qa_pipeline():
    return pipeline("question-answering", model=QA_MODEL_NAME, tokenizer=QA_MODEL_NAME)

@st.cache_resource
def load_curated_qa_pairs():
    try:
        with open(CURATED_QA_FILE, "r", encoding="utf-8") as f:
            return json.load(f)
    except Exception as e:
        st.error(f"Error loading curated Q/A pairs: {e}")
        return []

# ========================================
# Chatbot Interface & Conversation Handling
# ========================================
def display_conversation():
    """Displays conversation history in a structured chat format."""
    for role, message in st.session_state.conversation_history:
        with st.chat_message(role):
            st.write(message)

def add_to_conversation(role, message):
    """Adds a message to conversation history."""
    st.session_state.conversation_history.append((role, message))

# Initialize conversation history
if "conversation_history" not in st.session_state:
    st.session_state.conversation_history = []

# ========================================
# Main Streamlit Chat UI
# ========================================
st.title("Takalama - AI Chat")

# Load models & data
index, chunks = load_index_and_chunks()
embed_model = load_embedding_model()
qa_pipeline = load_qa_pipeline()
curated_qa_pairs = load_curated_qa_pairs()

display_conversation()

# User Input
user_query = st.chat_input("Ask a question about the document...")
if user_query:
    add_to_conversation("user", user_query)
    
    # Check for curated Q/A pair
    answer = None
    for pair in curated_qa_pairs:
        if user_query.lower() in pair["question"].lower():
            answer = pair["answer"]
            break
    
    if not answer:
        # Retrieve relevant context
        query_embedding = embed_model.encode([user_query]).astype("float32")
        distances, indices = index.search(query_embedding, 3)
        pdf_context = "\n".join(chunks[idx] for idx in indices[0])
        
        # Generate an answer using the QA pipeline
        response = qa_pipeline(question=user_query, context=pdf_context)
        answer = response.get("answer", "I couldn't find an answer to that.")
    
    add_to_conversation("assistant", answer)
    st.rerun()