app.py

# app.py
import os
import io
import sqlite3
from datetime import datetime
import fitz  # PyMuPDF
import numpy as np
from PIL import Image
import gradio as gr
import faiss
import pytesseract
from sentence_transformers import SentenceTransformer
import sympy as sp

# Optional: huggingface inference
from huggingface_hub import InferenceApi

# ------------- CONFIG -------------
APP_NAME = "Jajabor – SEBA Assamese Class 10 Tutor (Spaces)"
BASE_DIR = os.path.abspath(os.path.dirname(__file__))
PDF_DIR = os.path.join(BASE_DIR, "pdfs", "class10")
DB_PATH = os.path.join(BASE_DIR, "jajabor_users.db")

# Embedding model - compact for Spaces. Swap if you run on stronger infra.
EMBEDDING_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"

# LLM model to call via Inference API (optional)
# WARNING: not all large models will run under a free plan; see docs.
LLM_MODEL_NAME = "Qwen/Qwen2.5-3B-Instruct"  # can change to a hosted model
USE_HF_INFERENCE = True  # set False if you plan to load a local small model

CHUNK_SIZE = 600
CHUNK_OVERLAP = 120
TOP_K = 5

HUGGINGFACE_API_TOKEN = os.environ.get("HF_API_TOKEN", None)
if USE_HF_INFERENCE:
    if not HUGGINGFACE_API_TOKEN:
        print("Warning: HF API token not found in env (HF_API_TOKEN). LLM calls will fail.")
    else:
        inference = InferenceApi(repo_id=LLM_MODEL_NAME, token=HUGGINGFACE_API_TOKEN)

# ------------- DB helpers -------------
def init_db(db_path=DB_PATH):
    os.makedirs(os.path.dirname(db_path), exist_ok=True)
    conn = sqlite3.connect(db_path)
    cur = conn.cursor()
    cur.execute(
        """
        CREATE TABLE IF NOT EXISTS users (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            username TEXT UNIQUE,
            created_at TEXT
        )
        """
    )
    cur.execute(
        """
        CREATE TABLE IF NOT EXISTS interactions (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            user_id INTEGER,
            timestamp TEXT,
            query TEXT,
            answer TEXT,
            is_math INTEGER,
            FOREIGN KEY(user_id) REFERENCES users(id)
        )
        """
    )
    conn.commit()
    conn.close()

def get_or_create_user(username: str):
    username = username.strip()
    if not username:
        return None
    conn = sqlite3.connect(DB_PATH)
    cur = conn.cursor()
    cur.execute("SELECT id FROM users WHERE username=?", (username,))
    row = cur.fetchone()
    if row:
        user_id = row[0]
    else:
        cur.execute(
            "INSERT INTO users (username, created_at) VALUES (?, ?)",
            (username, datetime.utcnow().isoformat()),
        )
        conn.commit()
        user_id = cur.lastrowid
    conn.close()
    return user_id

def log_interaction(user_id, query, answer, is_math: bool):
    conn = sqlite3.connect(DB_PATH)
    cur = conn.cursor()
    cur.execute(
        """
        INSERT INTO interactions (user_id, timestamp, query, answer, is_math)
        VALUES (?, ?, ?, ?, ?)
        """,
        (user_id, datetime.utcnow().isoformat(), query, answer, 1 if is_math else 0),
    )
    conn.commit()
    conn.close()

def get_user_stats(user_id):
    conn = sqlite3.connect(DB_PATH)
    cur = conn.cursor()
    cur.execute(
        "SELECT COUNT(*), SUM(is_math) FROM interactions WHERE user_id=?", (user_id,)
    )
    row = cur.fetchone()
    conn.close()
    total = row[0] or 0
    math_count = row[1] or 0
    return total, math_count

init_db()

# ------------- PDF loading + RAG -------------
def extract_text_from_pdf(pdf_path: str) -> str:
    doc = fitz.open(pdf_path)
    pages = []
    for page in doc:
        txt = page.get_text("text")
        if txt:
            pages.append(txt)
    return "\n".join(pages)

def load_all_pdfs(pdf_dir: str):
    texts = []
    metas = []
    if not os.path.isdir(pdf_dir):
        print("PDF_DIR not found:", pdf_dir)
        return texts, metas
    for fname in os.listdir(pdf_dir):
        if fname.lower().endswith(".pdf"):
            path = os.path.join(pdf_dir, fname)
            print("Reading:", path)
            text = extract_text_from_pdf(path)
            texts.append(text)
            metas.append({"source": fname})
    return texts, metas

def split_text(text: str, chunk_size=600, overlap=120):
    chunks = []
    start = 0
    while start < len(text):
        end = start + chunk_size
        chunk = text[start:end]
        if chunk.strip():
            chunks.append(chunk)
        start = max(end - overlap, end)  # avoid infinite loop
    return chunks

print("Loading embedding model:", EMBEDDING_MODEL_NAME)
embedding_model = SentenceTransformer(EMBEDDING_MODEL_NAME)

print("Loading PDFs from", PDF_DIR)
all_texts, all_metas = load_all_pdfs(PDF_DIR)
print("Number of PDFs:", len(all_texts))

corpus_chunks = []
corpus_metas = []
for text, meta in zip(all_texts, all_metas):
    chs = split_text(text, CHUNK_SIZE, CHUNK_OVERLAP)
    corpus_chunks.extend(chs)
    corpus_metas.extend([meta] * len(chs))

print("Total chunks:", len(corpus_chunks))
if len(corpus_chunks) > 0:
    print("Encoding chunks...")
    embs = embedding_model.encode(corpus_chunks, batch_size=32, show_progress_bar=False).astype("float32")
    dim = embs.shape[1]
    index = faiss.IndexFlatL2(dim)
    index.add(embs)
    print("FAISS index ready; dim:", dim)
else:
    index = None
    print("No corpus chunks - upload PDFs to the `pdfs/class10` folder in the repo.")

def rag_search(query: str, k: int = TOP_K):
    if index is None:
        return []
    q_vec = embedding_model.encode([query]).astype("float32")
    D, I = index.search(q_vec, k)
    results = []
    for dist, idx in zip(D[0], I[0]):
        if idx == -1:
            continue
        results.append(
            {
                "score": float(dist),
                "text": corpus_chunks[idx],
                "meta": corpus_metas[idx],
            }
        )
    return results

# ------------- LLM helpers -------------
SYSTEM_PROMPT = """
You are "Jajabor", an expert SEBA Assamese tutor for Class 10.
Always prefer to answer in Assamese. If the student clearly asks for English, you may reply in English.

Rules:
- Use ONLY the given textbook context.
- If you are not sure, say: "এই প্ৰশ্নটো পাঠ্যপুথিৰ অংশত স্পষ্টকৈ নাই, সেয়েহে মই নিশ্চিত নহয়।"
- বোঝাপৰা সহজ ভাষাত ব্যাখ্যা কৰা, উদাহৰণ দিয়ক।
- If it is a maths question, explain step-by-step clearly.
"""

def build_rag_prompt(context_blocks, question, chat_history):
    ctx = ""
    for i, block in enumerate(context_blocks, start=1):
        src = block["meta"].get("source", "textbook")
        ctx += f"\n[Context {i} – {src}]\n{block['text']}\n"

    hist = ""
    for role, msg in chat_history:
        hist += f"{role}: {msg}\n"

    prompt = f"""{SYSTEM_PROMPT}

পূৰ্বৰ বাৰ্তাসমূহ:
{hist}

সদস্যৰ প্ৰশ্ন:
{question}

সম্পৰ্কিত পাঠ্যপুথিৰ অংশ:
{ctx}

এতিয়া একেদম সহায়ক আৰু বুজিবলৈ সহজ উত্তৰ দিয়া।
"""
    return prompt

def call_llm_via_hf(prompt: str, max_tokens=512):
    if not HUGGINGFACE_API_TOKEN:
        return "LLM not available: HF API token (env HF_API_TOKEN) is required to call the Inference API."
    try:
        # huggingface InferenceApi text-generation returns text (model-specific format)
        out = inference(inputs=prompt, params={"max_new_tokens": max_tokens, "temperature": 0.3})
        # inference result may be a dict or string; try to extract
        if isinstance(out, dict) and "generated_text" in out:
            return out["generated_text"]
        if isinstance(out, list) and len(out) > 0 and "generated_text" in out[0]:
            return out[0]["generated_text"]
        if isinstance(out, str):
            return out
        return str(out)
    except Exception as e:
        return f"LLM call failed: {e}"

def llm_answer_with_rag(question: str, chat_history):
    retrieved = rag_search(question, TOP_K)
    prompt = build_rag_prompt(retrieved, question, chat_history)
    if USE_HF_INFERENCE:
        return call_llm_via_hf(prompt)
    else:
        return "LLM not configured (USE_HF_INFERENCE=False)."

# ------------- OCR + math helpers -------------
def ocr_from_image(img: Image.Image):
    if img is None:
        return ""
    img = img.convert("RGB")
    try:
        text = pytesseract.image_to_string(img, lang="asm+eng")
    except Exception:
        text = pytesseract.image_to_string(img)
    return text.strip()

def is_likely_math(text: str) -> bool:
    math_chars = set("0123456789+-*/=^()%")
    if any(ch in text for ch in math_chars):
        return True
    kws = ["গণিত", "সমীকৰণ", "উদাহৰণ", "প্ৰশ্ন", "বীজগণিত"]
    return any(k in text for k in kws)

def solve_math_expression(expr: str):
    try:
        expr = expr.replace("^", "**")
        if "=" in expr:
            left, right = expr.split("=", 1)
            left_s = sp.sympify(left)
            right_s = sp.sympify(right)
            eq = sp.Eq(left_s, right_s)
            sol = sp.solve(eq)
            steps = []
            steps.append("প্ৰথমে সমীকৰণ লওঁ:")
            steps.append(f"{sp.pretty(eq)}")
            steps.append("Sympy ৰ সহায়ত সমাধান পোৱা যায়:")
            steps.append(str(sol))
            explanation = "ধাপ-ধাপে সমাধান (সংক্ষেপে):\n" + "\n".join(f"- {s}" for s in steps)
            explanation += f"\n\nসেয়েহে সমাধান: {sol}"
        else:
            expr_s = sp.sympify(expr)
            simp = sp.simplify(expr_s)
            explanation = (
                "প্ৰদত্ত গণিতীয় অভিব্যক্তি:\n"
                f"{expr}\n\nসরলীকৰণ কৰাৰ পিছত পোৱা যায়:\n{simp}"
            )
        return explanation
    except Exception:
        return (
            "মই সঠিকভাৱে গণিতীয় অভিব্যক্তি চিনাক্ত কৰিব নোৱাৰিলোঁ। "
            "দয়া কৰি সমীকৰণটো অলপ বেছি স্পষ্টকৈ লিখা: উদাহৰণ – 2x + 3 = 7"
        )

def speech_to_text(audio):
    return ""

def text_to_speech(text: str):
    return None

# ------------- Chat logic -------------
def login_user(username, user_state):
    username = (username or "").strip()
    if not username:
        return user_state, "⚠️ অনুগ্ৰহ কৰি প্ৰথমে লগিনৰ বাবে এটা নাম লিখক।"
    user_id = get_or_create_user(username)
    user_state = {"username": username, "user_id": user_id}
    total, math_count = get_user_stats(user_id)
    stats = (
        f"👤 ব্যৱহাৰকাৰী: **{username}**\n\n"
        f"📊 মোট প্ৰশ্ন: **{total}**\n"
        f"🧮 গণিত প্ৰশ্ন: **{math_count}**"
    )
    return user_state, stats

def chat_logic(
    username,
    text_input,
    image_input,
    audio_input,
    chat_history,
    user_state,
):
    if not user_state or not user_state.get("user_id"):
        sys_msg = "⚠️ প্ৰথমে ওপৰত আপোনাৰ নাম লিখি **Login / লগিন** টিপক।"
        chat_history = chat_history + [[text_input or "", sys_msg]]
        return chat_history, user_state, None

    user_id = user_state["user_id"]

    final_query_parts = []
    voice_text = speech_to_text(audio_input)
    if voice_text:
        final_query_parts.append(voice_text)

    ocr_text = ""
    if image_input is not None:
        try:
            img = Image.open(io.BytesIO(image_input.read()))
        except Exception:
            img = image_input
        ocr_text = ocr_from_image(img)
        if ocr_text:
            final_query_parts.append(ocr_text)

    if text_input:
        final_query_parts.append(text_input)

    if not final_query_parts:
        sys_msg = "⚠️ অনুগ্ৰহ কৰি প্ৰশ্ন লিখক, কিম্বা ছবি আপলোড কৰক।"
        chat_history = chat_history + [["", sys_msg]]
        return chat_history, user_state, None

    full_query = "\n".join(final_query_parts)
    conv = []
    for u, b in chat_history:
        if u:
            conv.append(("Student", u))
        if b:
            conv.append(("Tutor", b))

    is_math = is_likely_math(full_query)
    if is_math:
        math_answer = solve_math_expression(full_query)
        combined_question = (
            full_query
            + "\n\nগণিত প্ৰোগ্ৰামে এই ফলাফল দিছে:\n"
            + math_answer
            + "\n\nঅনুগ্ৰহ কৰি শ্রেণী ১০ ৰ শিক্ষাৰ্থীৰ বাবে সহজ ভাষাত ব্যাখ্যা কৰক।"
        )
        final_answer = llm_answer_with_rag(combined_question, conv)
    else:
        final_answer = llm_answer_with_rag(full_query, conv)

    log_interaction(user_id, full_query, final_answer, is_math)
    audio_out = text_to_speech(final_answer)
    display_question = text_input or voice_text or ocr_text or "(empty)"
    chat_history = chat_history + [[display_question, final_answer]]
    return chat_history, user_state, audio_out

# ------------- Gradio UI -------------
with gr.Blocks(title=APP_NAME) as demo:
    gr.Markdown(
        """
        # 🧭 জাজাবৰ – SEBA অসমীয়া ক্লাছ ১০ AI Tutor (Spaces)

        - Upload your SEBA Class 10 PDFs to `pdfs/class10` in this Space repo
        - Text + Image (OCR) input
        - Math step-by-step solutions
        - User login + progress
        """
    )

    user_state = gr.State({})

    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### 👤 লগিন")
            username_inp = gr.Textbox(label="নাম / ইউজাৰ আইডি", placeholder="উদাহৰণ: abu10")
            login_btn = gr.Button("✅ Login / লগিন")
            stats_md = gr.Markdown("এতিয়ালৈকে লগিন হোৱা নাই।", elem_classes="stats-box")
        with gr.Column(scale=3):
            chat = gr.Chatbot(label="জাজাবৰ সৈতে কথোপকথন", height=500)
            text_inp = gr.Textbox(label="আপোনাৰ প্ৰশ্ন লিখক", lines=2)
            with gr.Row():
                image_inp = gr.Image(label="📷 প্ৰশ্নৰ ছবি (Optional)", type="file")
                audio_inp = gr.Audio(label="🎙️ কণ্ঠস্বৰ প্ৰশ্ন (Stub)", type="numpy")
            with gr.Row():
                ask_btn = gr.Button("🤖 জাজাবৰক সোধক")
                audio_out = gr.Audio(label="🔊 উত্তৰৰ অডিঅ’ (TTS – future)", interactive=False)

    login_btn.click(login_user, inputs=[username_inp, user_state], outputs=[user_state, stats_md])

    def wrapped_chat(text, image, audio, history, user_state_inner, username_inner):
        if user_state_inner and username_inner and not user_state_inner.get("username"):
            user_state_inner["username"] = username_inner
        return chat_logic(username_inner, text, image, audio, history, user_state_inner)

    ask_btn.click(
        wrapped_chat,
        inputs=[text_inp, image_inp, audio_inp, chat, user_state, username_inp],
        outputs=[chat, user_state, audio_out],
    )
    text_inp.submit(
        wrapped_chat,
        inputs=[text_inp, image_inp, audio_inp, chat, user_state, username_inp],
        outputs=[chat, user_state, audio_out],
    )

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