from fastapi import FastAPI, UploadFile, Form, Request
from fastapi.responses import HTMLResponse
from fastapi.templating import Jinja2Templates

from ingestion import extract_pdf
from chunking import chunk_text
from retrieval_colbert import ColBERTRetriever
from reranker import rerank
from llm import generate_answer
from scraper import scrape_url

app = FastAPI()

templates = Jinja2Templates(directory="templates")

retriever = ColBERTRetriever()

# conversation memory
chat_memory = []


@app.get("/", response_class=HTMLResponse)
async def home(request: Request):
    return templates.TemplateResponse(
        "index.html",
        {"request": request}
    )


# -----------------------
# PDF Upload
# -----------------------

@app.post("/upload")
async def upload(file: UploadFile):

    text = extract_pdf(file.file)

    chunks = chunk_text(text, file.filename)

    if retriever.chunks:
        retriever.build_index(retriever.chunks + chunks)
    else:
        retriever.build_index(chunks)

    return {"status": "PDF indexed"}


# -----------------------
# Website Scraper
# -----------------------

@app.post("/scrape")
async def scrape(url: str = Form(...)):

    text = scrape_url(url)

    chunks = chunk_text(text, url)

    if retriever.chunks:
        retriever.build_index(retriever.chunks + chunks)
    else:
        retriever.build_index(chunks)

    return {"status": "Website indexed"}


# -----------------------
# Chat endpoint
# -----------------------

@app.post("/chat")
async def chat(message: str = Form(...)):

    global chat_memory

    retrieved = retriever.query(message, k=25)

    if not retrieved:
        return {"answer": "Please index a document first."}

    reranked = rerank(message, retrieved)

    top_chunks = reranked[:3]

    context = "\n\n".join(c["text"] for c in top_chunks)

    # build conversation history
    history = ""
    for m in chat_memory[-4:]:
        history += f"{m['role']}: {m['content']}\n"

    prompt_context = f"""
Conversation history:
{history}

Context:
{context}

User question:
{message}
"""

    answer = generate_answer(prompt_context, message)

    chat_memory.append({"role": "user", "content": message})
    chat_memory.append({"role": "assistant", "content": answer})

    return {
        "answer": answer,
        "chunks": top_chunks
    }