"""
AI Engine – uses Groq (free tier) for LLM calls + gTTS for audio.

Setup on Hugging Face Spaces:
  Settings → Repository secrets → Add:
    Name : GROQ_API_KEY
    Value: your key from https://console.groq.com  (free, no credit card)

Free Groq model used: llama-3.3-70b-versatile
"""

import os
import json
import re
from groq import Groq


# ── Client ───────────────────────────────────────────────────────────────────
def get_client() -> Groq:
    api_key = os.environ.get("GROQ_API_KEY", "")
    if not api_key:
        raise ValueError(
            "GROQ_API_KEY secret not set.\n"
            "Go to: Space Settings → Repository secrets → add GROQ_API_KEY\n"
            "Get a free key at: https://console.groq.com"
        )
    return Groq(api_key=api_key)


def _chat(prompt: str, max_tokens: int = 1024) -> str:
    """Single-turn chat with llama-3.3-70b on Groq."""
    client = get_client()
    resp = client.chat.completions.create(
        model="llama-3.3-70b-versatile",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=max_tokens,
        temperature=0.7,
    )
    return resp.choices[0].message.content


# ── Experiment Explanation ────────────────────────────────────────────────────
def generate_experiment_explanation(exp: dict) -> str:
    prompt = f"""You are an enthusiastic science teacher for Class 10 students (age ~15).

Experiment: {exp['title']}
Materials: {exp['materials']}
Steps: {exp['steps']}
Outcome: {exp['outcome']}

Write a clear, engaging explanation (250–350 words) covering:
1. **What is happening scientifically** – the core concept
2. **Why it works** – the chemistry/biology/physics behind it
3. **Real-world connection** – where students see this in daily life
4. **Key formula or equation** if applicable (use simple notation)

Use friendly, enthusiastic language suitable for a 15-year-old."""
    return _chat(prompt, max_tokens=700)


# ── Quiz Generator ────────────────────────────────────────────────────────────
def generate_quiz_questions(exp: dict) -> list:
    prompt = f"""You are a science quiz creator for Class 10 students.

Experiment: {exp['title']}
Materials: {exp['materials']}
Steps: {exp['steps']}
Outcome: {exp['outcome']}

Generate exactly 5 multiple-choice questions testing understanding of this experiment.

Return ONLY a valid JSON array — no markdown fences, no extra text — in this exact format:
[
  {{
    "question": "Question text here?",
    "options": ["A) Option 1", "B) Option 2", "C) Option 3", "D) Option 4"],
    "answer": "A) Option 1",
    "explanation": "Brief reason why this answer is correct."
  }}
]

Cover: observation, concept, reasoning, safety, and real-world application."""

    raw = _chat(prompt, max_tokens=1400)
    raw = re.sub(r"```json|```", "", raw).strip()

    # Find the JSON array inside the response
    match = re.search(r"\[.*\]", raw, re.DOTALL)
    if match:
        raw = match.group(0)

    try:
        questions = json.loads(raw)
        if isinstance(questions, list) and questions:
            return questions
    except json.JSONDecodeError:
        pass

    # Fallback single question
    return [{
        "question": f"What is the main observation in '{exp['title']}'?",
        "options": [
            f"A) {exp['outcome'][:70]}",
            "B) No reaction takes place",
            "C) The mixture turns blue",
            "D) Heat is always absorbed",
        ],
        "answer": f"A) {exp['outcome'][:70]}",
        "explanation": exp["outcome"],
    }]


# ── Video Script Generator ────────────────────────────────────────────────────
def generate_video_script(exp: dict) -> str:
    prompt = f"""You are a scriptwriter creating a 2-minute science video for Class 10 students.

Experiment: {exp['title']}
Materials: {exp['materials']}
Procedure: {exp['steps']}
Safety: {exp['safety']}
Outcome: {exp['outcome']}

Write a complete VIDEO SCRIPT with these six scenes:

🎬 SCENE 1 – HOOK (0:00–0:15)
[Camera/action]
Narrator: "..."

🎬 SCENE 2 – MATERIALS (0:15–0:30)
[Visual: each material shown]
Narrator: "..."

🎬 SCENE 3 – SAFETY BRIEFING (0:30–0:40)
[Safety icons / lab coat]
Narrator: "..."

🎬 SCENE 4 – STEP-BY-STEP PROCEDURE (0:40–1:20)
[Camera angle + action per step]
Narrator: "..."

🎬 SCENE 5 – OBSERVATION & RESULT (1:20–1:45)
[Close-up of the change/outcome]
Narrator: "..."

🎬 SCENE 6 – SCIENCE EXPLANATION (1:45–2:00)
[Animation or diagram]
Narrator: "..."

Include [ANIMATION: ...], [CLOSE-UP: ...], and [TEXT ON SCREEN: ...] cues.
Narrator text must be enthusiastic and clear for 15-year-old students."""
    return _chat(prompt, max_tokens=1200)


# ── Text-to-Speech ────────────────────────────────────────────────────────────
def text_to_speech_explanation(exp: dict, script: str = None):
    """
    Converts narrator lines from the script to MP3 using gTTS (free, no API key).
    Returns the path to the MP3 file, or None on failure.
    """
    try:
        from gtts import gTTS
        import tempfile

        if script:
            lines = []
            for line in script.split("\n"):
                line = line.strip()
                if line.lower().startswith("narrator:"):
                    text = line[9:].strip().strip('"').strip("'")
                    if text:
                        lines.append(text)
            audio_text = " ".join(lines) if lines else script
        else:
            audio_text = (
                f"Welcome to the AI Science Lab. Today we explore: {exp['title']}. "
                f"Materials needed: {exp['materials']}. "
                f"Procedure: {exp['steps']}. "
                f"Safety: {exp['safety']}. "
                f"Expected outcome: {exp['outcome']}."
            )

        audio_text = audio_text[:3000]  # gTTS limit
        tts = gTTS(text=audio_text, lang="en", slow=False)
        tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
        tts.save(tmp.name)
        return tmp.name

    except ImportError:
        return None
    except Exception as e:
        print(f"TTS error: {e}")
        return None