import gradio as gr
import random
from datetime import datetime
import tempfile
import os
import edge_tts
import asyncio
import warnings
import pytz
import re
import json
import pandas as pd
from pathlib import Path
from gradio_client import Client

warnings.filterwarnings('ignore')

# Initialize story starters
STORY_STARTERS = [
    ['Adventure', 'In a hidden temple deep in the Amazon...'],
    ['Mystery', 'The detective found an unusual note...'],
    ['Romance', 'Two strangers meet on a rainy evening...'],
    ['Sci-Fi', 'The space station received an unexpected signal...'],
    ['Fantasy', 'A magical portal appeared in the garden...']
]

# Initialize client outside of interface definition
arxiv_client = None

def init_client():
    global arxiv_client
    if arxiv_client is None:
        arxiv_client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
    return arxiv_client

def save_story(story, audio_path):
    """Save story and audio to gallery"""
    try:
        # Create gallery directory if it doesn't exist
        gallery_dir = Path("gallery")
        gallery_dir.mkdir(exist_ok=True)
        
        # Generate timestamp for unique filename
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        
        # Save story text
        story_path = gallery_dir / f"story_{timestamp}.txt"
        with open(story_path, "w") as f:
            f.write(story)
        
        # Copy audio file to gallery
        if audio_path:
            new_audio_path = gallery_dir / f"audio_{timestamp}.mp3"
            os.system(f"cp {audio_path} {str(new_audio_path)}")
            
        return str(story_path), str(new_audio_path)
    except Exception as e:
        print(f"Error saving to gallery: {str(e)}")
        return None, None

def load_gallery():
    """Load all stories and audio from gallery"""
    try:
        gallery_dir = Path("gallery")
        if not gallery_dir.exists():
            return []
        
        files = []
        for story_file in gallery_dir.glob("story_*.txt"):
            timestamp = story_file.stem.split('_')[1]
            audio_file = gallery_dir / f"audio_{timestamp}.mp3"
            
            with open(story_file) as f:
                story_text = f.read()
            
            # Format as list instead of dict for Gradio Dataframe
            files.append([
                timestamp,
                story_text[:100] + "...",
                str(story_file),
                str(audio_file) if audio_file.exists() else None
            ])
        
        return sorted(files, key=lambda x: x[0], reverse=True)
    except Exception as e:
        print(f"Error loading gallery: {str(e)}")
        return []

def generate_story(prompt, model_choice):
    """Generate story using specified model"""
    try:
        client = init_client()
        if client is None:
            return "Error: Story generation service is not available."
        
        result = client.predict(
            prompt=prompt,
            llm_model_picked=model_choice,
            stream_outputs=True,
            api_name="/ask_llm"
        )
        return result
    except Exception as e:
        return f"Error generating story: {str(e)}"

async def generate_speech(text, voice="en-US-AriaNeural"):
    """Generate speech from text"""
    try:
        communicate = edge_tts.Communicate(text, voice)
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
            tmp_path = tmp_file.name
            await communicate.save(tmp_path)
        return tmp_path
    except Exception as e:
        print(f"Error in text2speech: {str(e)}")
        return None

def process_story_and_audio(prompt, model_choice):
    """Process story, generate audio, and save to gallery"""
    try:
        # Generate story
        story = generate_story(prompt, model_choice)
        if isinstance(story, str) and story.startswith("Error"):
            return story, None, None
        
        # Generate audio
        audio_path = asyncio.run(generate_speech(story))
        
        # Save to gallery
        story_path, saved_audio_path = save_story(story, audio_path)
        
        return story, audio_path, load_gallery()
    except Exception as e:
        return f"Error: {str(e)}", None, None

def play_gallery_audio(evt: gr.SelectData, gallery_data):
    """Play audio from gallery selection"""
    try:
        selected_row = gallery_data[evt.index[0]]
        audio_path = selected_row[3]  # Audio path is the fourth element
        if audio_path and os.path.exists(audio_path):
            return audio_path
        return None
    except Exception as e:
        print(f"Error playing gallery audio: {str(e)}")
        return None

# Create the Gradio interface
with gr.Blocks(title="AI Story Generator") as demo:
    gr.Markdown("""
    # 🎭 AI Story Generator & Narrator
    Generate creative stories, listen to them, and build your gallery!
    """)
    
    with gr.Row():
        with gr.Column(scale=3):
            with gr.Row():
                prompt_input = gr.Textbox(
                    label="Story Concept",
                    placeholder="Enter your story idea...",
                    lines=3
                )
            
            with gr.Row():
                model_choice = gr.Dropdown(
                    label="Model",
                    choices=[
                        "mistralai/Mixtral-8x7B-Instruct-v0.1",
                        "mistralai/Mistral-7B-Instruct-v0.2"
                    ],
                    value="mistralai/Mixtral-8x7B-Instruct-v0.1"
                )
                generate_btn = gr.Button("Generate Story")
            
            with gr.Row():
                story_output = gr.Textbox(
                    label="Generated Story",
                    lines=10,
                    interactive=False
                )
            
            with gr.Row():
                audio_output = gr.Audio(
                    label="Story Narration",
                    type="filepath"
                )
        
        # Sidebar with Story Starters and Gallery
        with gr.Column(scale=1):
            gr.Markdown("### 📚 Story Starters")
            story_starters = gr.Dataframe(
                value=STORY_STARTERS,
                headers=["Category", "Starter"],
                interactive=False
            )
            
            gr.Markdown("### 🎬 Gallery")
            gallery = gr.Dataframe(
                value=load_gallery(),
                headers=["Timestamp", "Preview", "Story Path", "Audio Path"],
                interactive=False
            )
    
    # Event handlers
    def update_prompt(evt: gr.SelectData):
        return STORY_STARTERS[evt.index[0]][1]
    
    story_starters.select(update_prompt, None, prompt_input)
    
    generate_btn.click(
        fn=process_story_and_audio,
        inputs=[prompt_input, model_choice],
        outputs=[story_output, audio_output, gallery]
    )
    
    gallery.select(
        fn=play_gallery_audio,
        inputs=[gallery],
        outputs=[audio_output]
    )

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