app3.py

import os
import re
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")
LOGO_FILE = "Brain chat-09.png"

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:
        for path in [CHUNKS_PATH, TOKENS_PATH, EMBED_PATH, CONFIG_PATH]:
            if not os.path.exists(path):
                raise FileNotFoundError(f"Missing file: {path}")

        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 choose_quiz_count(user_text: str, selector: str) -> int:
    if selector in {"3", "5", "7"}:
        return int(selector)

    t = user_text.lower()
    if any(k in t for k in ["mock test", "final exam", "exam practice", "full test"]):
        return 7
    if any(k in t for k in ["detailed", "revision", "comprehensive", "study"]):
        return 5
    return 3


def language_instruction(language_mode: str) -> str:
    if language_mode == "English":
        return "Answer only in English."
    if language_mode == "Spanish":
        return "Answer only in Spanish."
    if language_mode == "Bilingual":
        return "Answer first in English, then provide a Spanish version under the heading 'Español:'."
    return (
        "If the user's message is in Spanish, answer in Spanish. "
        "If the user's message is in English, answer in English."
    )


def build_tutor_prompt(mode: str, language_mode: str, question: str, context: str) -> str:
    mode_map = {
        "Explain": "Explain clearly like a friendly tutor using simple language.",
        "Detailed": "Give a fuller and more detailed explanation.",
        "Short Notes": "Answer in concise revision-note format using bullets.",
        "Flashcards": "Create 6 flashcards in Q/A format.",
        "Case-Based": "Create a short clinical scenario and explain it clearly."
    }

    return f"""
You are BrainChat, an interactive neurology and neuroanatomy tutor.

Rules:
- Use only the provided context from the books.
- If the answer is not supported by the context, say exactly:
  Not found in the course material.
- Be accurate and student-friendly.
- {language_instruction(language_mode)}

Teaching style:
{mode_map[mode]}

Context:
{context}

Question:
{question}
""".strip()


def build_quiz_generation_prompt(language_mode: str, topic: str, context: str, n_questions: int) -> str:
    return f"""
You are BrainChat, an interactive tutor.

Rules:
- Use only the provided context.
- Create exactly {n_questions} quiz questions.
- Questions should be short and clear.
- Also create a short answer key.
- Return valid JSON only.
- {language_instruction(language_mode)}

Required JSON format:
{{
  "title": "short quiz title",
  "questions": [
    {{"q": "question 1", "answer_key": "expected short answer"}},
    {{"q": "question 2", "answer_key": "expected short answer"}}
  ]
}}

Context:
{context}

Topic:
{topic}
""".strip()


def chat_text(prompt: str) -> str:
    resp = OAI.chat.completions.create(
        model="gpt-4o-mini",
        temperature=0.2,
        messages=[
            {"role": "system", "content": "You are a helpful educational assistant."},
            {"role": "user", "content": prompt},
        ],
    )
    return resp.choices[0].message.content.strip()


def chat_json(prompt: str) -> dict:
    resp = OAI.chat.completions.create(
        model="gpt-4o-mini",
        temperature=0.2,
        response_format={"type": "json_object"},
        messages=[
            {"role": "system", "content": "Return only valid JSON."},
            {"role": "user", "content": prompt},
        ],
    )
    return json.loads(resp.choices[0].message.content)


def detect_logo_url():
    if os.path.exists(LOGO_FILE):
        return f"/gradio_api/file={quote(LOGO_FILE)}"
    return None


def render_header():
    logo_url = detect_logo_url()
    if logo_url:
        logo_html = f'<img src="{logo_url}" alt="BrainChat Logo" style="width:120px;height:120px;object-fit:contain;display:block;margin:0 auto;">'
    else:
        logo_html = '<div style="width:120px;height:120px;border-radius:50%;background:#efe85a;display:flex;align-items:center;justify-content:center;font-weight:700;text-align:center;margin:0 auto;">BRAIN<br>CHAT</div>'

    return f"""
    <div class="hero-card">
        <div class="hero-inner">
            <div class="hero-logo">{logo_html}</div>
            <div class="hero-title">BrainChat</div>
            <div class="hero-subtitle">
                Interactive neurology and neuroanatomy tutor based on your uploaded books
            </div>
        </div>
    </div>
    """


def answer_question(message, history, mode, language_mode, quiz_count_mode, show_sources):
    if not message or not message.strip():
        return "Please type a topic or question."

    ensure_loaded()
    user_text = message.strip()

    records = search_hybrid(user_text, shortlist_k=30, final_k=5)
    context = build_context(records)

    if mode == "Quiz Me":
        n_questions = choose_quiz_count(user_text, quiz_count_mode)
        prompt = build_quiz_generation_prompt(language_mode, user_text, context, n_questions)
        quiz_data = chat_json(prompt)

        lines = []
        lines.append(f"**{quiz_data.get('title', 'Quiz')}**")
        lines.append("")
        lines.append(f"**Total questions: {len(quiz_data['questions'])}**")
        lines.append("")

        for i, q in enumerate(quiz_data["questions"], start=1):
            lines.append(f"**Q{i}.** {q['q']}")

        lines.append("")
        lines.append("Reply with your answers in one message, for example:")
        lines.append("1. ...")
        lines.append("2. ...")
        lines.append("")
        lines.append("This version generates quiz questions only. Evaluation can be added next.")

        if show_sources:
            lines.append("\n---\n**Topic sources used to create the quiz:**")
            lines.append(make_sources(records))

        return "\n".join(lines)

    prompt = build_tutor_prompt(mode, language_mode, user_text, context)
    answer = chat_text(prompt)

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

    return answer


CSS = """
body, .gradio-container {
    background: #dcdcdc !important;
    font-family: Arial, Helvetica, sans-serif !important;
}
footer { display: none !important; }
.hero-card {
    max-width: 860px;
    margin: 18px auto 14px auto;
    border-radius: 28px;
    background: linear-gradient(180deg, #e8c7d4 0%, #a55ca2 48%, #2b0c46 100%);
    padding: 22px 22px 18px 22px;
}
.hero-inner { text-align: center; }
.hero-title {
    color: white;
    font-size: 34px;
    font-weight: 800;
    margin-top: 6px;
}
.hero-subtitle {
    color: white;
    opacity: 0.92;
    font-size: 16px;
    margin-top: 6px;
}
"""


with gr.Blocks(css=CSS) as demo:
    gr.HTML(render_header())

    with gr.Row():
        mode = gr.Dropdown(
            choices=["Explain", "Detailed", "Short Notes", "Quiz Me", "Flashcards", "Case-Based"],
            value="Explain",
            label="Tutor Mode"
        )
        language_mode = gr.Dropdown(
            choices=["Auto", "English", "Spanish", "Bilingual"],
            value="Auto",
            label="Answer Language"
        )

    with gr.Row():
        quiz_count_mode = gr.Dropdown(
            choices=["Auto", "3", "5", "7"],
            value="Auto",
            label="Quiz Questions"
        )
        show_sources = gr.Checkbox(value=True, label="Show Sources")

    gr.Markdown("""
**How to use**
- Choose a **Tutor Mode**
- Then type a topic or question
- For **Quiz Me**, type a topic such as: `cranial nerves`
- For **Flashcards**, type a topic such as: `hippocampus`
""")

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

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