# Use a pipeline as a high-level helper
from transformers import pipeline
import gradio as gr
from PIL import Image, ImageDraw
import scipy.io.wavfile as wavfile

# from phonemizer.backend.espeak.wrapper import EspeakWrapper
# _ESPEAK_LIBRARY = '/opt/homebrew/Cellar/espeak/1.48.04_1/lib/libespeak.1.1.48.dylib'  #use the Path to the library.
# EspeakWrapper.set_library(_ESPEAK_LIBRARY)

object_detector = pipeline("object-detection", model="facebook/detr-resnet-50")

# object_detector_model_path = "../Models/models--facebook--detr-resnet-50/snapshots/1d5f47bd3bdd2c4bbfa585418ffe6da5028b4c0b"

# object_detector = pipeline("object-detection", model=object_detector_model_path)

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

# tts_model_path = "../Models/models--kakao-enterprise--vits-ljs/snapshots/3bcb8321394f671bd948ebf0d086d694dda95464"

# narrator = pipeline("text-to-speech", model=tts_model_path)

# Define the function to generate audio from text 
def generate_audio(text):
    # Generate the narrated text
    narrated_text = narrator(text)

    # Save the audio to WAV file
    wavfile.write("finetuned_output.wav", rate=narrated_text["sampling_rate"],
                  data=narrated_text["audio"][0])

    # Return the path to the saved output WAV file
    return "finetuned_output.wav"

def read_objects(detection_objects):
    # Initialize counters for each object label
    object_counts = {}

    # Count the occurrences of each label
    for detection in detection_objects:
        label = detection['label']
        if label in object_counts:
            object_counts[label] += 1
        else:
            object_counts[label] = 1

    # Generate the response string
    response = "This picture contains"
    labels = list(object_counts.keys())
    for i, label in enumerate(labels):
        response += f" {object_counts[label]} {label}"
        if object_counts[label] > 1:
            response += "s"
        if i < len(labels) - 2:
            response += ","
        elif i == len(labels) - 2:
            response += " and"

    response += "."

    return response

def draw_bounding_boxes(image, object_detections):
    """
    Draws bounding boxes around detected objects on a PIL image.
    
    Args:
        image (PIL.Image): The input image.
        object_detections (list): A list of dictionaries, where each dictionary represents a detected object. 
                                 Each dictionary should have the following keys:
                                 - 'score': the confidence score of the detection
                                 - 'label': the label of the detected object
                                 - 'box': a dictionary with keys 'xmin', 'ymin', 'xmax', 'ymax' 
                                   representing the bounding box coordinates.
    
    Returns:
        PIL.Image: The input image with bounding boxes drawn around the detected objects.
    """
    draw = ImageDraw.Draw(image)
    for detection in object_detections:
        box = detection['box']
        label = detection['label']
        score = detection['score']
        
        # Draw the bounding box
        draw.rectangle((box['xmin'], box['ymin'], box['xmax'], box['ymax']), outline=(255, 0, 0), width=2)
        
        # Draw the label and score
        text = f"{label} ({score:.2f})"
        draw.text((box['xmin'], box['ymin'] - 20), text, fill=(255, 0, 0))
    
    return image

def detect_object(image):
    # raw_image = Image.open(image)
    output = object_detector(image)
    processed_image = draw_bounding_boxes(image, output)
    natural_text = read_objects(output)
    processed_audio = generate_audio(natural_text)
    return processed_image, processed_audio

gr.close_all()

demo = gr.Interface(fn=detect_object,
                    inputs=[gr.Image(label="Select Image",type="pil")],
                    outputs=[gr.Image(label="Processed Image", type="pil"), gr.Audio(label="Generated Audio")],
                    title="@IT AI Enthusiast (https://www.youtube.com/@itaienthusiast/) - Project 7: Object Detector with Audio",
                    description="THIS APPLICATION WILL BE USED TO HIGHLIGHT OBJECTS AND GIVES AUDIO DESCRIPTION FOR THE PROVIDED INPUT IMAGE.")
demo.launch()