import subprocess
import os
import threading
import numpy as np
import librosa
import gradio as gr
from functools import lru_cache
from transformers import pipeline
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import torch
from huggingface_hub import login

# Install required dependencies
def install_missing_packages():
    required_packages = {
        "librosa": None,
        "diffusers": ">=0.14.0",
        "gradio": ">=3.35.2",
        "huggingface_hub": None,
        "accelerate": ">=0.20.1",
        "transformers": ">=4.31.0",
        "torch": ">=1.11.0"
    }
    for package, version in required_packages.items():
        try:
            __import__(package)
        except ImportError:
            package_name = f"{package}{version}" if version else package
            subprocess.check_call(["pip", "install", package_name])

install_missing_packages()

# Hugging Face token authentication
hf_token = os.getenv("HF_TOKEN")
if hf_token:
    login(hf_token)
else:
    raise ValueError("HF_TOKEN environment variable not set.")

# Load the speech-to-text model
speech_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")

# Load Stable Diffusion model
text_to_image = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", 
    torch_dtype=torch.float16
)
device = "cuda" if torch.cuda.is_available() else "cpu"
text_to_image.to(device)
text_to_image.enable_attention_slicing()  # Optimizes memory usage
text_to_image.safety_checker = None  # Disables safety checker
text_to_image.scheduler = DPMSolverMultistepScheduler.from_config(text_to_image.scheduler.config)

# Preprocess audio file into NumPy array
def preprocess_audio(audio_path):
    try:
        audio, sr = librosa.load(audio_path, sr=16000)  # Resample to 16kHz
        return np.array(audio, dtype=np.float32)
    except Exception as e:
        return f"Error in preprocessing audio: {str(e)}"

# Transcribe audio to text
@lru_cache(maxsize=10)
def transcribe_audio(audio_path):
    try:
        audio_array = preprocess_audio(audio_path)
        if isinstance(audio_array, str):  # Error message from preprocessing
            return audio_array
        result = speech_to_text(audio_array)
        return result["text"]
    except Exception as e:
        return f"Error in transcription: {str(e)}"

# Generate image from text
@lru_cache(maxsize=10)
def generate_image_from_text(text):
    try:
        image = text_to_image(text, height=512, width=512).images[0]
        return image
    except Exception as e:
        return f"Error in image generation: {str(e)}"

# Process audio input (speech-to-image)
def speech_to_image(audio_path):
    transcription = transcribe_audio(audio_path)
    if "Error" in transcription:
        return None, f"Transcription failed: {transcription}"
    
    image = generate_image_from_text(transcription)
    if isinstance(image, str) and "Error" in image:
        return None, f"Image generation failed: {image}"
    
    return image

# Process text input (text-to-image)
def text_to_image_interface(input_text):
    try:
        image = generate_image_from_text(input_text)
        return image
    except Exception as e:
        return f"Error: {str(e)}"

# Gradio interface
speech_to_image_interface = gr.Interface(
    fn=speech_to_image,
    inputs=gr.Audio(type="filepath", label="Upload audio file (WAV/MP3)"),
    outputs=gr.Image(label="Generated Image"),
    title="Speech-to-Image Generator",
    description="Upload an audio file to generate an image based on the transcribed speech."
)

text_to_image_interface = gr.Interface(
    fn=text_to_image_interface,
    inputs=gr.Textbox(label="Enter Text", placeholder="Describe an image..."),
    outputs=gr.Image(label="Generated Image"),
    title="Text-to-Image Generator",
    description="Enter text to generate an image based on the description."
)

# Combine interfaces into a single Gradio app
app = gr.TabbedInterface(
    interface_list=[speech_to_image_interface, text_to_image_interface],
    tab_names=["Speech-to-Image", "Text-to-Image"]
)

# Launch Gradio interface
app.launch(debug=True, share=True)