app.py

# app.py
"""
Jajabor – SEBA Assamese Class 10 Tutor (Gradio app)
Full single-file app:
- Loads PDFs from ./pdfs/class10
- Builds FAISS index using sentence-transformers
- Optional Hugging Face Inference API for LLM (set HF_API_TOKEN env var)
- Login + sqlite interactions logging
- OCR from images (pytesseract) with robust handling of gr.Image(type="filepath")
"""

import os
import io
import sqlite3
from datetime import datetime
import traceback

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 HF inference
from huggingface_hub import InferenceApi

# -------------------- CONFIG --------------------
APP_NAME = "Jajabor – SEBA Assamese Class 10 Tutor"

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)
EMBEDDING_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"

# LLM: model to call via HF Inference API. Change if you have another hosted model.
LLM_MODEL_NAME = "Qwen/Qwen2.5-3B-Instruct"
USE_HF_INFERENCE = True  # set False if you don't want to call HF Inference

CHUNK_SIZE = 600
CHUNK_OVERLAP = 120
TOP_K = 5

HUGGINGFACE_API_TOKEN = os.environ.get("HF_API_TOKEN", None)
if USE_HF_INFERENCE and HUGGINGFACE_API_TOKEN is None:
    print("Warning: HF_API_TOKEN not set. LLM calls will fail until the token is provided in env.")

inference = None
if USE_HF_INFERENCE and HUGGINGFACE_API_TOKEN:
    try:
        inference = InferenceApi(repo_id=LLM_MODEL_NAME, token=HUGGINGFACE_API_TOKEN)
    except Exception as e:
        print("Failed to initialize HF Inference API client:", e)
        inference = None

# -------------------- 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:
    try:
        doc = fitz.open(pdf_path)
    except Exception:
        return ""
    pages = []
    for page in doc:
        try:
            txt = page.get_text("text")
            if txt:
                pages.append(txt)
        except Exception:
            continue
    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 sorted(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):
    if not text:
        return []
    chunks = []
    start = 0
    L = len(text)
    # Keep stepping forward by chunk_size - overlap
    step = max(chunk_size - overlap, 1)
    while start < L:
        end = min(start + chunk_size, L)
        chunk = text[start:end]
        if chunk.strip():
            chunks.append(chunk)
        start += step
    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))
index = None
if len(corpus_chunks) > 0:
    print("Encoding chunks (this may take some seconds)...")
    try:
        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)
    except Exception as e:
        print("Failed to encode/add to index:", e)
        index = None
else:
    print("No corpus chunks found: upload PDFs to ./pdfs/class10")

def rag_search(query: str, k: int = TOP_K):
    if index is None:
        return []
    try:
        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
    except Exception:
        return []

# -------------------- 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 inference is None:
        return "LLM not available: HF Inference client not configured (set HF_API_TOKEN and ensure model name is accessible)."
    try:
        # Some inference endpoints accept dict return, some strings. Handle flexibly.
        out = inference(inputs=prompt, params={"max_new_tokens": max_tokens, "temperature": 0.3})
        # Handle common return types
        if isinstance(out, dict) and "generated_text" in out:
            return out["generated_text"]
        if isinstance(out, list) and len(out) > 0:
            if isinstance(out[0], dict) and "generated_text" in out[0]:
                return out[0]["generated_text"]
            # sometimes list of strings
            if isinstance(out[0], str):
                return out[0]
        if isinstance(out, str):
            return out
        return str(out)
    except Exception as e:
        traceback.print_exc()
        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 ""
    try:
        img = img.convert("RGB")
    except Exception:
        pass
    try:
        # try Assamese + English; fallback if languages not installed
        text = pytesseract.image_to_string(img, lang="asm+eng")
    except Exception:
        try:
            text = pytesseract.image_to_string(img)
        except Exception:
            text = ""
    return text.strip()

def is_likely_math(text: str) -> bool:
    if not text:
        return False
    math_chars = set("0123456789+-*/=^()%")
    if any(ch in text for ch in math_chars):
        return True
    kws = ["গণিত", "সমীকৰণ", "উদাহৰণ", "প্ৰশ্ন", "বীজগণিত", "solve", "equation"]
    if any(k in text for k in kws):
        return True
    return False

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\nসেয়ে 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 – 2*x + 3 = 7"
        )

def speech_to_text(audio):
    # stub for future ASR integration
    return ""

def text_to_speech(text: str):
    # stub for TTS integration
    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,
):
    # Ensure chat_history is a list
    if chat_history is None:
        chat_history = []

    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 = []

    # audio (stub)
    voice_text = speech_to_text(audio_input)
    if voice_text:
        final_query_parts.append(voice_text)

    # image handling (robust)
    ocr_text = ""
    if image_input is not None and image_input != "":
        img = None
        try:
            # If Gradio returns a file path (string)
            if isinstance(image_input, str):
                try:
                    img = Image.open(image_input)
                except Exception:
                    img = None
            else:
                # If it's a file-like object: has .read()
                read_method = getattr(image_input, "read", None)
                if callable(read_method):
                    try:
                        raw = image_input.read()
                        img = Image.open(io.BytesIO(raw))
                    except Exception:
                        img = None
                # If it's already a PIL Image
                if img is None and isinstance(image_input, Image.Image):
                    img = image_input
        except Exception:
            img = None

        if img is not None:
            try:
                ocr_text = ocr_from_image(img)
                if ocr_text:
                    final_query_parts.append(ocr_text)
            except Exception:
                pass

    # text input
    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)

    # If LLM returns the whole prompt + generation, try to remove the prompt (best-effort)
    if isinstance(final_answer, str) and final_answer.strip().startswith(SYSTEM_PROMPT.strip()):
        # best-effort: don't leak huge prompts to chat UI; keep as-is if detection fails
        # (Many HF inference responses do not include the prompt anyway)
        pass

    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, css=None) as demo:
    gr.Markdown(
        """
        # 🧭 জাজাবৰ – SEBA অসমীয়া ক্লাছ ১০ AI Tutor

        - Upload your SEBA Class 10 PDFs to `pdfs/class10` in this repo (or when running locally, ensure folder exists)
        - 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, student01 ..."
            )
            login_btn = gr.Button("✅ Login / লগিন")
            stats_md = gr.Markdown("এতিয়ালৈকে লগিন হোৱা নাই।", elem_classes="stats-box")

            gr.Markdown(
                """
                ### 💡 টিপছ
                - "ক্লাছ ১০ গণিত: উদাহৰণ ৩.১ প্ৰশ্ন ২" – এই ধৰণৰ প্ৰশ্ন ভাল
                - ফটো আপলোড কৰিলে টেক্স্টটো OCR কৰি পঢ়িব চেষ্টা কৰা হয়
                - সম্ভৱ হলে প্ৰশ্নটো অসমীয়াত সোধক 🙂
                """
            )

        with gr.Column(scale=3):
            chat = gr.Chatbot(label="জাজাবৰ সৈতে কথোপকথন", height=500)
            text_inp = gr.Textbox(
                label="আপোনাৰ প্ৰশ্ন লিখক",
                placeholder='উদাহৰণ: "ক্লাছ ১০ অসমীয়া: অনুচ্ছেদ পাঠ ১ ৰ মূল বিষয় কি?"',
                lines=2,
            )

            with gr.Row():
                # IMPORTANT: use type="filepath" so Gradio returns a local path string
                image_inp = gr.Image(label="📷 প্ৰশ্নৰ ছবি (Optional)", type="filepath")
                audio_inp = gr.Audio(label="🎙️ কণ্ঠস্বৰ প্ৰশ্ন (Stub — not used now)", type="numpy")

            with gr.Row():
                ask_btn = gr.Button("🤖 জাজাবৰক সোধক")
                audio_out = gr.Audio(label="🔊 উত্তৰৰ অডিঅ’ (TTS – future upgrade)", 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):
        # keep username in state if provided
        if user_state_inner is None:
            user_state_inner = {}
        if 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],
    )

# -------------------- Launch --------------------
if __name__ == "__main__":
    # For Spaces, demo.launch() is fine. Locally you can set server_name to "0.0.0.0"
    demo.launch()