from groq import Groq
from pydantic import BaseModel, ValidationError
from typing import List, Literal
import os
import tiktoken
import json
import re
from gtts import gTTS
import tempfile

groq_client = Groq(api_key=os.environ["GROQ_API_KEY"])
tokenizer = tiktoken.get_encoding("cl100k_base")

class DialogueItem(BaseModel):
    speaker: Literal["Maria", "Sarah"]
    text: str

class Dialogue(BaseModel):
    dialogue: List[DialogueItem]

def truncate_text(text, max_tokens=2048):
    tokens = tokenizer.encode(text)
    if len(tokens) > max_tokens:
        return tokenizer.decode(tokens[:max_tokens])
    return text

def generate_script(system_prompt: str, input_text: str, tone: str, target_length: str):
    input_text = truncate_text(input_text)
    word_limit = 300 if target_length == "Short (1-2 min)" else 750  # Assuming 150 words per minute
    
    prompt = f"""
    {system_prompt}
    TONE: {tone}
    TARGET LENGTH: {target_length} (approximately {word_limit} words)
    INPUT TEXT: {input_text}

    Generate a complete, well-structured podcast script that:
    1. Starts with a proper introduction
    2. Covers the main points from the input text
    3. Has a natural flow of conversation between Maria and Sarah
    4. Concludes with a summary and sign-off
    5. Fits within the {word_limit} word limit for the target length of {target_length}

    Ensure the script is not abruptly cut off and forms a complete conversation.
    """
    
    response = groq_client.chat.completions.create(
        messages=[
            {"role": "system", "content": prompt},
        ],
        model="llama-3.1-70b-versatile",
        max_tokens=2048,
        temperature=0.7
    )
    
    content = response.choices[0].message.content
    content = re.sub(r'```json\s*|\s*```', '', content)
    
    try:
        json_data = json.loads(content)
        dialogue = Dialogue.model_validate(json_data)
    except json.JSONDecodeError as json_error:
        match = re.search(r'\{.*\}', content, re.DOTALL)
        if match:
            try:
                json_data = json.loads(match.group())
                dialogue = Dialogue.model_validate(json_data)
            except (json.JSONDecodeError, ValidationError) as e:
                raise ValueError(f"Failed to parse dialogue JSON: {e}\nContent: {content}")
        else:
            raise ValueError(f"Failed to find valid JSON in the response: {content}")
    except ValidationError as e:
        raise ValueError(f"Failed to validate dialogue structure: {e}\nContent: {content}")
    
    return dialogue

def generate_audio(text: str, speaker: str) -> str:
    tts = gTTS(text=text, lang='en', tld='com')
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
        tts.save(temp_audio.name)
        return temp_audio.name