import torch
from transformers import pipeline
from PIL import Image
from scipy.io import wavfile
import gradio as gr
import numpy as np
import os
import requests
# Specify the device (CPU or GPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the image-to-text pipeline
caption_image = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large", device=device)
# Load the image-to-text pipeline with the vit-gpt2 model
#caption_pipeline = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning", device=device)

# Load the text-to-speech pipeline
narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs", device=device)


# URLs of the images
image_urls = [
    "https://github.com/Walid-Ahmed/ML_Datasets/blob/master/image1.jpeg?raw=true",
    "https://github.com/Walid-Ahmed/ML_Datasets/blob/master/image2.jpeg?raw=true",
    "https://github.com/Walid-Ahmed/ML_Datasets/blob/master/image3.jpeg?raw=true"
]

# Directory to save images
save_dir = "example_images"
os.makedirs(save_dir, exist_ok=True)

# Function to download images
def download_image(url, filename):
    response = requests.get(url)
    if response.status_code == 200:
        with open(filename, "wb") as f:
            f.write(response.content)
        return filename
    else:
        print(f"Failed to download: {url}")
        return None

# Download images
example_images = []
for idx, url in enumerate(image_urls):
    img_path = os.path.join(save_dir, f"image{idx+1}.jpeg")
    if not os.path.exists(img_path):  # Avoid redownloading if already exists
        download_image(url, img_path)
    example_images.append(img_path)

def process_image(image):
    # Generate the caption
    caption = caption_image(image)[0]['generated_text']

    # Generate speech from the caption
    speech = narrator(caption)

    # Convert the audio to PCM format
    audio_data = np.array(speech["audio"][0] * 32767, dtype=np.int16)

    # Save the audio to a WAV file
    audio_path = "caption.wav"
    wavfile.write(audio_path, rate=speech["sampling_rate"], data=audio_data)

    return caption, audio_path

# Create Gradio interface
iface = gr.Interface(
    fn=process_image,
    inputs=gr.Image(type="pil"),
    outputs=[gr.Textbox(label="Generated Caption"), gr.Audio(label="Generated Audio", type="filepath")],
    examples=example_images
)

# Launch the interface
iface.launch()