import gradio as gr
import os, sys
from prompting import promptingutils
from imageprocessing import imageprocessingtools 
from openai import OpenAI
from prompting.promptingutils import DEFAULT_N_SAMPLES, DEFAULT_OBJECT_THRESHOLD, DEFAULT_RANDOM_STATE


AVAILABLE_LLMS = [
    "mistral-7b-instruct",
    "vicuna-7b",
    "llama-7b-chat",
    "vicuna-13b",
]

DEFAULT_TEMPERATURE = 0
LLAMA_API_TOKEN = os.environ["LLAMA_API_TOKEN"]

client = OpenAI(
    api_key = LLAMA_API_TOKEN,
    base_url = "https://api.llama-api.com"
)


def caption_artwork(
        image_filepath: os.PathLike,
        llm :str, 
        temperature = DEFAULT_TEMPERATURE,
        items_threshold = DEFAULT_OBJECT_THRESHOLD,
        random_state = DEFAULT_RANDOM_STATE,
        n_samples_per_emotion = DEFAULT_N_SAMPLES
)-> tuple:
    
    all_information = imageprocessingtools.extract_all_information_from_image(image_filepath)
    
    emotion = all_information["emotion"]
    colors_list = all_information["colors_list"]
    objects_and_probs = all_information["objects_and_probs"]
    objects_list = promptingutils.filter_items(objects_and_probs, items_threshold=items_threshold)

    user_prompt = promptingutils.get_user_prompt(
        colors_list=colors_list, 
        objects_list=objects_list,
        emotion=emotion,
        n_samples_per_emotion=n_samples_per_emotion,
        random_state=random_state,
        object_threshold=items_threshold
        
        )
    
    response = client.chat.completions.create(
        model = llm,
        messages = [
            {"role": "system"   , "content": "Assistant is a large language model trained by OpenAI."},
            {"role": "user"     , "content": user_prompt}
        ],
        temperature = temperature
    )

    commentary_str = response.choices[0].message.content
    colors_str = ", ".join(colors_list)
    objects_str = ", ".join(objects_list)
    emotion_str = emotion

    return (emotion_str, colors_str, objects_str, commentary_str)

with gr.Blocks() as demo:

    with gr.Column():
        gr.HTML("""
                <h2 style="text-align: center;">
                LLMs talk about art!
                </h2>
                <p style="text-align: center;"></p>
            """)
        print("Checking for image")
        test_image_name = "1665_Girl_with_a_Pearl_Earring.jpg"
        print(os.path.isfile(test_image_name))
        print(os.path.isfile(os.path.join(os.getcwd(), test_image_name)))
        print(os.path.isfile(os.path.join(os.path.dirname(__file__), test_image_name)))
        gr_image = gr.Image(
            label= "An artwork: ",
            type = "filepath",
            #value= "https://d3vjn2zm46gms2.cloudfront.net/blogs/2015/05/27010344/Whistler27s-Mother_small.jpg"
            #value= os.path.join("imageprocessing", test_image_name) 
            #"https://upload.wikimedia.org/wikipedia/commons/0/0f/1665_Girl_with_a_Pearl_Earring.jpg" 
                #os.path.join(
                #os.getcwd(),
                #os.path.dirname(__file__),
                #"1665_Girl_with_a_Pearl_Earring.jpg"
            #)
        )

        gr_model = gr.Dropdown(
            label= "A Large Language Model",
            choices = AVAILABLE_LLMS,
            value = AVAILABLE_LLMS[0]
        )

        btn = gr.Button(value="Submit your image!")

        gr_emotion = gr.Textbox(
            label = "Evoked emotion: ",
        )
        gr_colors = gr.Textbox(
            label = "Main colors: ",
        )
        gr_objects = gr.Textbox(
            label = "Main objects present: ",
        )

        gr_commentary = gr.Textbox(
            label = "Commentary on the artwork:",
        )

        btn.click(
            caption_artwork, 
            inputs=[gr_image, gr_model], 
            outputs=[gr_emotion, gr_colors, gr_objects, gr_commentary]
            )

"""
def greet(name):
    return "Hello " + name + "!!"

iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()
"""
if __name__ == "__main__":
    demo.launch(allowed_paths = [os.path.dirname(__file__)])