app1.py

import os
import re
import html
import json
import pickle
from urllib.parse import quote

import numpy as np
import gradio as gr
from rank_bm25 import BM25Okapi
from sentence_transformers import SentenceTransformer
from openai import OpenAI

BUILD_DIR = "brainchat_build"
CHUNKS_PATH = os.path.join(BUILD_DIR, "chunks.pkl")
TOKENS_PATH = os.path.join(BUILD_DIR, "tokenized_chunks.pkl")
EMBED_PATH = os.path.join(BUILD_DIR, "embeddings.npy")
CONFIG_PATH = os.path.join(BUILD_DIR, "config.json")

EMBED_MODEL = None
BM25 = None
CHUNKS = None
EMBEDDINGS = None
OAI = None


def tokenize(text: str):
    return re.findall(r"\w+", text.lower(), flags=re.UNICODE)


def ensure_loaded():
    global EMBED_MODEL, BM25, CHUNKS, EMBEDDINGS, OAI

    if CHUNKS is None:
        if not os.path.exists(CHUNKS_PATH):
            raise FileNotFoundError("Missing brainchat_build/chunks.pkl")
        if not os.path.exists(TOKENS_PATH):
            raise FileNotFoundError("Missing brainchat_build/tokenized_chunks.pkl")
        if not os.path.exists(EMBED_PATH):
            raise FileNotFoundError("Missing brainchat_build/embeddings.npy")
        if not os.path.exists(CONFIG_PATH):
            raise FileNotFoundError("Missing brainchat_build/config.json")

        with open(CHUNKS_PATH, "rb") as f:
            CHUNKS = pickle.load(f)

        with open(TOKENS_PATH, "rb") as f:
            tokenized_chunks = pickle.load(f)

        EMBEDDINGS = np.load(EMBED_PATH)

        with open(CONFIG_PATH, "r", encoding="utf-8") as f:
            cfg = json.load(f)

        BM25 = BM25Okapi(tokenized_chunks)
        EMBED_MODEL = SentenceTransformer(cfg["embedding_model"])

    if OAI is None:
        api_key = os.getenv("OPENAI_API_KEY")
        if not api_key:
            raise ValueError("OPENAI_API_KEY is missing in Hugging Face Space Secrets.")
        OAI = OpenAI(api_key=api_key)


def search_hybrid(query: str, shortlist_k: int = 30, final_k: int = 5):
    ensure_loaded()

    query_tokens = tokenize(query)
    bm25_scores = BM25.get_scores(query_tokens)

    shortlist_idx = np.argsort(bm25_scores)[::-1][:shortlist_k]
    shortlist_embeddings = EMBEDDINGS[shortlist_idx]

    qvec = EMBED_MODEL.encode([query], normalize_embeddings=True).astype("float32")[0]
    dense_scores = shortlist_embeddings @ qvec

    rerank_order = np.argsort(dense_scores)[::-1][:final_k]
    final_idx = shortlist_idx[rerank_order]

    return [CHUNKS[int(i)] for i in final_idx]


def build_context(records):
    blocks = []
    for i, r in enumerate(records, start=1):
        blocks.append(
            f"""[Source {i}]
Book: {r['book']}
Section: {r['section_title']}
Pages: {r['page_start']}-{r['page_end']}
Text:
{r['text']}"""
        )
    return "\n\n".join(blocks)


def make_sources(records):
    seen = set()
    lines = []
    for r in records:
        key = (r["book"], r["section_title"], r["page_start"], r["page_end"])
        if key in seen:
            continue
        seen.add(key)
        lines.append(f"- {r['book']} | {r['section_title']} | pp. {r['page_start']}-{r['page_end']}")
    return "\n".join(lines)


def answer_question(message: str, history, show_sources: bool):
    if not message or not message.strip():
        return "Please type a question."

    try:
        records = search_hybrid(message, shortlist_k=30, final_k=5)
        context = build_context(records)

        system_prompt = """You are BrainChat, a neurology and neuroanatomy tutor.

Rules:
- Answer only from the provided context.
- If the answer is not supported by the context, say exactly:
  Not found in the course material.
- Keep the answer clear and concise unless the user asks for more detail.
- If the question is in Spanish, answer in Spanish.
- If the question is in English, answer in English.
"""

        user_prompt = f"""Context:
{context}

Question:
{message}
"""

        resp = OAI.chat.completions.create(
            model="gpt-4o-mini",
            temperature=0.2,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt},
            ],
        )

        answer = resp.choices[0].message.content.strip()

        if show_sources:
            answer += "\n\n---\nSources used:\n" + make_sources(records)

        return answer

    except Exception as e:
        return f"Error: {str(e)}"


def detect_logo_url():
    candidates = [
        "Brain chat-09.png",
        "brainchat_logo.png",
        "Brain Chat Imagen.svg",
    ]
    for name in candidates:
        if os.path.exists(name):
            return f"/gradio_api/file={quote(name)}"
    return None


def top_html():
    logo_url = detect_logo_url()
    if logo_url:
        logo = f'<img src="{logo_url}" style="width:110px;height:110px;object-fit:contain;border-radius:50%;">'
    else:
        logo = '<div style="width:110px;height:110px;border-radius:50%;background:#efe85a;display:flex;align-items:center;justify-content:center;font-weight:bold;">BRAIN<br>CHAT</div>'

    return f"""
    <div style="
        max-width:430px;
        margin:18px auto 0 auto;
        border:16px solid black;
        border-radius:42px;
        background:linear-gradient(180deg,#e8c7d4 0%,#a55ca2 48%,#2b0c46 100%);
        padding:72px 18px 18px 18px;
        box-sizing:border-box;
        position:relative;">
        <div style="position:absolute;top:0;left:50%;transform:translateX(-50%);width:170px;height:30px;background:black;border-bottom-left-radius:20px;border-bottom-right-radius:20px;"></div>
        <div style="display:flex;justify-content:center;margin-bottom:18px;">{logo}</div>
        <div style="text-align:center;color:white;font-size:28px;font-weight:700;margin-bottom:8px;">BrainChat</div>
        <div style="text-align:center;color:white;opacity:0.9;margin-bottom:10px;">Ask questions from all your uploaded neurology books</div>
    </div>
    """


CUSTOM_CSS = """
body, .gradio-container {
    background:#dcdcdc !important;
}
footer {display:none !important;}
"""


with gr.Blocks(css=CUSTOM_CSS) as demo:
    gr.HTML(top_html())

    show_sources = gr.Checkbox(value=True, label="Show sources")

    gr.ChatInterface(
        fn=answer_question,
        additional_inputs=[show_sources],
        title=None,
        description=None,
        textbox=gr.Textbox(placeholder="Ask a question...", lines=1),
    )

if __name__ == "__main__":
    demo.launch()