import os
import zipfile
import sqlite3
import tempfile
import csv
from dotenv import load_dotenv
from groq import Groq
import httpx
import time
from pathlib import Path
import re
import pandas as pd
import PyPDF2

from tag_constants import KEYWORD_MAP


load_dotenv()

# Paths to your assets
ASSETS_DIR = Path(__file__).parent.parent / "assets"
DB_PATH = Path(__file__).parent.parent / "resources.db"
FLASHCARDS_DIR = ASSETS_DIR / "flashcards"
PYQ_CSV = ASSETS_DIR / "pyq" / "questions-data-new.csv"
VIDEO_DIR = ASSETS_DIR / "video"
import sys
# Add the project root to sys.path so we can import the app module
sys.path.append(str(Path(__file__).parent.parent))
from app.groq_client import groq_chat


def setup_db():
    """Initializes the SQLite Database for micro-resources."""
    conn = sqlite3.connect(DB_PATH)
    cursor = conn.cursor()
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS micro_resources (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            topic TEXT,
            type TEXT, 
            content TEXT,
            duration_min INTEGER,
            cognitive_load INTEGER
        )
    """)
    conn.commit()
    return conn

def classify_flashcard(content):
    content_lower = content.lower()

    for keyword, topic in KEYWORD_MAP.items():
        if keyword in content_lower:
            return topic
    
    return "General CS" # Fallback if no keywords match

def process_anki_apkg(conn, apkg_path, batch_size=20):
    
    print(f"Processing Flashcard Deck: {apkg_path.name} in batches of {batch_size}...")
    cursor = conn.cursor()
    
    existing_flashcards = {row[0].split('\n**Back:**')[0].replace('**Front:** ', '').strip() 
        for row in cursor.execute("SELECT content FROM micro_resources WHERE type = 'flashcard'")}
    
    with tempfile.TemporaryDirectory() as tmp_dir:
        # 1. Unzip the .apkg file
        with zipfile.ZipFile(apkg_path, 'r') as zip_ref:
            zip_ref.extractall(tmp_dir)
            
        # 2. Find the SQLite DB (usually collection.anki2 or collection.anki21)
        db_file = os.path.join(tmp_dir, 'collection.anki2')
        if not os.path.exists(db_file):
            db_file = os.path.join(tmp_dir, 'collection.anki21')
            
        if not os.path.exists(db_file):
            print(f"  [!] No valid collection.anki2 DB found in {apkg_path.name}")
            return
            
        # 3. Connect to the Anki DB and extract notes
        anki_conn = sqlite3.connect(db_file)
        anki_cursor = anki_conn.cursor()
        
        try:
            # flds contains front and back separated by \x1f
            anki_cursor.execute("SELECT tags, flds FROM notes")
            rows = anki_cursor.fetchall()
            
            count = 0
            for tags, flds in rows:
                fields = flds.split('\x1f')
                if len(fields) >= 2:
                    front = fields[0].replace('\n', '<br>')
                    back_raw = fields[1].replace('\n', '<br>')
                    
                    # Phase 11: Add detailed explanation and research link to flashcard
                    augment_prompt = "You are an expert tutor. Enhance this flashcard answer with a detailed explanation and a deep-dive research link (e.g. Wikipedia or documentation). Output ONLY the enhanced answer in HTML format using <br> for newlines."
                    augment_user = f"Front: {front}\nBack: {back_raw}"
                    try:
                        back = groq_chat(system_prompt=augment_prompt, user_message=augment_user, model="openai/gpt-oss-20b")
                    except Exception as e:
                        back = f"{back_raw}<br><br><b>Explanation:</b> (Auto-generation failed - {str(e)})"

                    content = f"**Front:** {front}\n**Back:** {back}"
                    
                    if front.strip() in existing_flashcards:
                        continue  # Skip already processed flashcards
                    existing_flashcards.add(front.strip())
                    
                    detected_topic = classify_flashcard(content)
                    
                    cursor.execute("""
                        INSERT INTO micro_resources (topic, type, content, duration_min, cognitive_load)
                        VALUES (?, ?, ?, ?, ?)
                    """, (detected_topic.strip(), 'flashcard', content, 2, 2))
                    
                    count += 1
                    
                    if count % batch_size == 0:
                        conn.commit()
                        print(f" [+] Saved batch of {batch_size}. Total flashcards processed: {count}")
                        
                        time.sleep(2)
                        
            conn.commit()
            print(f"  [+] Finished! Extracted & Tagged {count} NEW flashcards.")
                    
        except Exception as e:
            print(f"  [!] Error reading Anki DB: {e}")
        finally:
            anki_conn.close()
            
    conn.commit()

def generate_solution_with_groq(question_text):
    """Uses Groq API to solve PYQ offline and extract a quick tip."""
    system_prompt = "You are an expert Computer Science professor. Solve this past-year exam question concisely. Then, provide a 1-sentence 'Quick Tip'."
    user_message = f"Question: {question_text}"
    
    try:
        return groq_chat(system_prompt=system_prompt, user_message=user_message, model="openai/gpt-oss-20b")
    except Exception as e:
        print(f"  [!] Groq API Error: {e}")
        return None

def process_pyq_csv(conn, csv_path, max_rows=10):
    """
    Reads the PYQ CSV. 
    WARNING: We limit to `max_rows` by default so you don't exhaust your Groq API limits.
    """
    print(f"Processing PYQs from {csv_path.name} (Limit: {max_rows} rows)...")
    cursor = conn.cursor()
    
    # Fetch already processed questions to allow resuming
    cursor.execute("SELECT content FROM micro_resources WHERE type='pyq_solution'")
    existing_content = [row[0] for row in cursor.fetchall()]
    
    count = 0
    with open(csv_path, 'r', encoding='utf-8') as f:
        reader = csv.DictReader(f)
        for row in reader:
            if count >= max_rows:
                break
                
            topic = row.get('topic', 'General')
            question = row.get('question', '')
            
            # Check if this question was already processed (exists in any content string)
            already_processed = any(question in content for content in existing_content)
            if already_processed:
                print(f"  -> Skipping (Already processed): {question[:50]}...")
                continue

            
            print(f"  -> Generating solution for: {question[:50]}...")
            solution = generate_solution_with_groq(question)
            
            if solution:
                content = f"**Question:** {question}\n\n**Solution & Tip:**\n{solution}"
                cursor.execute("""
                    INSERT INTO micro_resources (topic, type, content, duration_min, cognitive_load)
                    VALUES (?, ?, ?, ?, ?)
                """, (topic, 'pyq_solution', content, 5, 4))
                count += 1
                time.sleep(1) # Prevent hitting rate limits
                
    conn.commit()
    print(f"  [+] Generated {count} PYQ solutions.")

def time_to_minutes(time_str):
    """Converts SRT timestamp 'HH:MM:SS,mmm' to total minutes."""
    # Example: 00:05:30,000 -> 5.5
    parts = re.split(r'[:,]', time_str)
    if len(parts) >= 3:
        h, m, s = int(parts[0]), int(parts[1]), int(parts[2])
        return (h * 60) + m + (s / 60.0)
    return 0

def process_video_srt(conn, srt_dir, chunk_duration_min=5):
    """Parses .srt files into N-minute chunks and stores them."""
    if not srt_dir.exists():
        print("  [!] Video directory not found.")
        return

    cursor = conn.cursor()
    total_chunks = 0

    for srt_file in srt_dir.glob("*.srt"):
        print(f"Processing Video Subtitles: {srt_file.name}...")
        
        # Use filename as topic temporarily
        topic = srt_file.stem.replace('_', ' ').title()
        
        with open(srt_file, 'r', encoding='utf-8') as f:
            content = f.read()

        # Simple Regex to match SRT blocks
        blocks = re.split(r'\n\n+', content.strip())
        
        current_chunk_text = []
        current_chunk_start = None
        current_chunk_end = None
        
        for block in blocks:
            lines = block.split('\n')
            if len(lines) >= 3:
                # Line 2 has timestamps: 00:00:00,000 --> 00:00:05,000
                timestamps = lines[1].split(' --> ')
                if len(timestamps) == 2:
                    start_min = time_to_minutes(timestamps[0])
                    end_min = time_to_minutes(timestamps[1])
                    text = ' '.join(lines[2:])
                    
                    if current_chunk_start is None:
                        current_chunk_start = start_min
                    
                    current_chunk_text.append(text)
                    current_chunk_end = end_min
                    
                    # If this chunk has reached our duration limit, save it!
                    if (current_chunk_end - current_chunk_start) >= chunk_duration_min:
                        chunk_content = f"**Video:** {topic} ({int(current_chunk_start)}m - {int(current_chunk_end)}m)\n\n" + " ".join(current_chunk_text)
                        
                        cursor.execute("""
                            INSERT INTO micro_resources (topic, type, content, duration_min, cognitive_load)
                            VALUES (?, ?, ?, ?, ?)
                        """, (topic, 'video_chunk', chunk_content, chunk_duration_min, 3))
                        
                        total_chunks += 1
                        # Reset for next chunk
                        current_chunk_text = []
                        current_chunk_start = None

        # Save any leftover text as the final chunk
        if current_chunk_text and current_chunk_start is not None and current_chunk_end is not None:
            actual_duration = max(1, int(current_chunk_end - current_chunk_start))
            chunk_content = f"**Video:** {topic} ({int(current_chunk_start)}m - {int(current_chunk_end)}m)\n\n" + " ".join(current_chunk_text)
            cursor.execute("""
                INSERT INTO micro_resources (topic, type, content, duration_min, cognitive_load)
                VALUES (?, ?, ?, ?, ?)
            """, (topic, 'video_chunk', chunk_content, actual_duration, 3))
            total_chunks += 1
            
    conn.commit()
    print(f"  [+] Extracted {total_chunks} video chunks.")

def process_programming_csv(conn, csv_path):
    #read CSV usign pandas or csv.DictReader
    print(f"Processing Programming CSV: {csv_path.name}...")
    cursor = conn.cursor()
    
    df = pd.read_csv(csv_path)
    count = 0
    
    #topic can be "Programming Language" or "Topic" column
    for index, row in df.iterrows():
        question = str(row.get('Question', ''))
        language = str(row.get('Programming Language', ''))
        topic = str(row.get('Topic', ''))
        solution = str(row.get('AI-Generated Solution', ''))
        explanation = str(row.get('Explanation', ''))
        
        if pd.isna(question) or question == 'nan' : continue
    
        #Combine the question, solution and explanation into a Markdown string and save it as a pyq_solution type.
        db_topic = f"{language} {topic}".strip() if language != 'nan' else topic
        if not db_topic or db_topic == 'nan':
            db_topic = "General Programming"
        
        content = f"**Question:**\n{question}\n\n**Solution:**\n{solution}\n\n**Explanation:**\n{explanation}"
        
        cursor.execute("""
            INSERT INTO micro_resources (topic, type, content, duration_min, cognitive_load)
            VALUES (?, ?, ?, ?, ?)
        """, (db_topic, 'pyq_solution', content, 5, 4))
        count += 1
        
    conn.commit()
    print(f"  [+] Processed {count} programming resources.")
    
def process_software_csv(conn, csv_path):
    print(f"Processing Software Question from {csv_path.name}...")
    cursor = conn.cursor()
    
    #Add encoding='latin1' to the file reader to bypass the unicode error.
    df = pd.read_csv(csv_path, encoding='latin1')
    count = 0
    
    #Map its question/answer columns to our micro_resources schema.
    for index, row in df.iterrows():
        question = str(row.get('Question', ''))
        answer = str(row.get('Answer', ''))
        topic = str(row.get('Category', ''))
        
        if pd.isna(question) or question == 'nan' : continue
        
        if not topic or topic == 'nan':
            topic = "General Software"
        
        content = f"**Question:**\n{question}\n\n**Answer:**\n{answer}"
        
        cursor.execute("""
            INSERT INTO micro_resources (topic, type, content, duration_min, cognitive_load)
            VALUES (?, ?, ?, ?, ?)
        """, (topic, 'flashcard', content, 2, 2))
        count += 1
        
    conn.commit()
    print(f"  [+] Processed {count} software resources.")

def process_unstructured_folder(conn, pdf_path):
    #Read the PDFs/TXTs and split the text into small 500-word chunks.
    print(f"Processing Unstructured Folder: {pdf_path.name}...")
    cursor = conn.cursor()
    count = 0
    
    text_content = ""
    try:
        with open(pdf_path, 'rb') as f:
            reader = PyPDF2.PdfReader(f)
            for page in reader.pages:
                text_content += page.extract_text() + "\n"
    except Exception as e:
        print(f"  [!] Error reading PDF {pdf_path}: {e}")
        return
    
    words = text_content.split()
    chunk_size = 500

    for i in range(0, len(words), chunk_size):
        chunk_words = words[i:i + chunk_size]
        chunk_text = ' '.join(chunk_words)
        if not chunk_text.strip(): continue
        
        print(f"    -> Generating flashcards from chunk {count+1}...")
        system_prompt = "You are an expert Computer Science professor. Create 3-5 concise flashcards from this content. Format as markdown with **Question:** and **Answer:** pairs."
            
        try:
            time.sleep(3) # To avoid hitting rate limits
            response = groq_chat(system_prompt=system_prompt, 
                                user_message=chunk_text,
                                model="openai/gpt-oss-20b")
            if response:
                cursor.execute("""
                    INSERT INTO micro_resources (topic, type, content, duration_min, cognitive_load)
                    VALUES (?, ?, ?, ?, ?)
                """, ("Data Science", "flashcard", response, 2, 2))
        except Exception as e:
            print(f"  [!] Error generating flashcards: {e}")

        print(f"    -> Generating PYQ from chunk {count+1}...")
        pyq_prompt = "You are an expert Computer Science professor. Create 1 complex, exam-style past-year question and a detailed solution based on this content. Format as markdown with exactly '**Question:**' and '**Solution & Tip:**'."
            
        try:
            time.sleep(3)
            pyq_response = groq_chat(system_prompt=pyq_prompt, 
                                user_message=chunk_text,
                                model="openai/gpt-oss-20b")
            if pyq_response:
                cursor.execute("""
                    INSERT INTO micro_resources (topic, type, content, duration_min, cognitive_load)
                    VALUES (?, ?, ?, ?, ?)
                """, ("Data Science", "pyq_solution", pyq_response, 5, 4))
        except Exception as e:
            print(f"  [!] Error generating PYQ: {e}")

        count +=1
        conn.commit()
        
    print(f"  [+] Processed {count} unstructured resources.")

    
    #Save under data science topic
if __name__ == "__main__":
    print("Starting Offline Asset Processing...")
    conn = setup_db()
    
    # 1. Process Flashcards (.apkg files)
    if FLASHCARDS_DIR.exists():
        for apkg_file in FLASHCARDS_DIR.glob("*.apkg"):
            process_anki_apkg(conn, apkg_file)
    else:
        print(" [!] Flashcards directory not found.")
        
    # 2. Process PYQ CSV
    if PYQ_CSV.exists():
        process_pyq_csv(conn, PYQ_CSV, max_rows=10) # Set limit higher when ready!
    else:
        print(" [!] PYQ CSV not found.")
        
    # 3. Process Video Subtitles (.srt files)
    process_video_srt(conn, VIDEO_DIR, chunk_duration_min=5)
    
    pyq_dir = ASSETS_DIR / "pyq"
    
    if (pyq_dir / "programming_questions_solutions.csv").exists():
        process_programming_csv(conn, pyq_dir / "programming_questions_solutions.csv")
    else:
        print("  [!] Programming CSV not found.")
        
    if (pyq_dir / "software_questions.csv").exists():
        process_software_csv(conn, pyq_dir / "software_questions.csv")
    else:
        print("  [!] Software CSV not found.")
    ds_dir = pyq_dir / "data_science"
    if (ds_dir / "questions1.pdf").exists():
        process_unstructured_folder(conn, ds_dir / "questions1.pdf")
    else:
        print("  [!] Data Science PDF 1 not found.")
        
    if (ds_dir / "questions2.pdf").exists():
        process_unstructured_folder(conn, ds_dir / "questions2.pdf")
    else:
        print("  [!] Data Science PDF 2 not found.")
        
    conn.close()