import streamlit as st
import requests
import base64
from typing import Iterator
import os
from text_generation import Client
from deep_translator import GoogleTranslator

model_id = os.environ.get("CODE", None)

API_URL = "https://api-inference.huggingface.co/models/" + model_id
HF_TOKEN = os.environ.get("HF_TOKEN", None)

client = Client(
    API_URL,
    headers={"Authorization": f"Bearer {HF_TOKEN}"},
)
EOS_STRING = "</s>"
EOT_STRING = "<EOT>"

translator_to_en = GoogleTranslator(source='arabic', target='english')
translator_to_ar = GoogleTranslator(source='english', target='arabic')

def get_prompt(message: str, chat_history: list[tuple[str, str]],
               system_prompt: str) -> str:
    texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
    do_strip = False
    for user_input, response in chat_history:
        user_input = user_input.strip() if do_strip else user_input
        do_strip = True
        texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ')
    message = message.strip() if do_strip else message
    texts.append(f'{message} [/INST]')
    return ''.join(texts)


def run(message: str,
                   chat_history: list[tuple[str, str]],
                   system_prompt: str,
                   max_new_tokens: int = 1024,
                   temperature: float = 0.1,
                   top_p: float = 0.9,
                   top_k: int = 50) -> Iterator[str]:

    prompt = get_prompt(message, chat_history, system_prompt)

    generate_kwargs = dict(
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
    )

    stream = client.generate_stream(prompt, **generate_kwargs)
    output = ""
    
    for response in stream:
        if any([end_token in response.token.text for end_token in [EOS_STRING, EOT_STRING]]):
            translated_output = translator_to_ar.translate(output)
            yield translated_output
            output = ""
        else:
            output += response.token.text


def generate_image_caption(image_data):
    image_base64 = base64.b64encode(image_data).decode('utf-8')
    payload = {"data": ["data:image/jpeg;base64," + image_base64]}
    response = requests.post("https://ashrafb-salesforce-blip-image-captioning-base.hf.space/run/predict", json=payload)
    if response.status_code == 200:
        caption = response.json()["data"][0]
        return caption
    else:
        return "Error: Unable to generate caption"


def main():
    st.markdown('<p style="color:crimson;text-align:center;font-size:30px;">Aiconvert.online img2story</p>', unsafe_allow_html=True)
    hide_streamlit_style = """
            <style>
            #MainMenu {visibility: hidden;}
            footer {visibility: hidden;}
            </style>
            """
    st.markdown(hide_streamlit_style, unsafe_allow_html=True)
    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
    
    if uploaded_file is not None:
        image_data = uploaded_file.read()
        st.image(image_data, caption="Uploaded Image.", use_column_width=True)
    
    if st.button("Generate Story"):
        system_prompt = "write attractive story in 300 words about"
        
        if uploaded_file is not None:
            caption = generate_image_caption(image_data)
            
            if caption.startswith("Error"):
                st.error(caption)
                return

            with st.spinner("Generating story..."):  # Adding a spinner while generating the story
                ai_response = next(run(caption, [], system_prompt))
                
            # Display the generated story
            st.subheader("Generated Story:")
            st.write(ai_response, unsafe_allow_html=True)
        else:
            st.warning("Please upload an image.")
            return

if __name__ == "__main__":
    main()