from dotenv import find_dotenv, load_dotenv # get the API keys
from transformers import pipeline # download huggingface model to our machine
from langchain_core.prompts import PromptTemplate
from langchain_community.chat_models import ChatOpenAI
from langchain.chains import LLMChain
import requests
import os
import streamlit as st

load_dotenv(find_dotenv())
HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")

# img2text

def img2text(url):
    image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")

    text = image_to_text(url)[0]["generated_text"]
    
    print(text)
    return text
    

# llm
def generate_story(scenario):
    # template to generate a story
    template = """
    You are a story teller;
    You can generate a short story based on a single narrative, the story should be no more than 20 words;
    
    CONTEXT: {scenario}
    STORY:
    """
    prompt = PromptTemplate(template=template, input_variables=["scenario"])
    
    # llm chain
    story_llm = LLMChain(llm=ChatOpenAI(
        model_name="gpt-3.5-turbo", temperature=1), prompt=prompt, verbose=True)
    
    story = story_llm.predict(scenario=scenario)

    
    print(story)
    return story 


# text to speech
def text2speech(message):
    API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
    headers = {"Authorization": f"Bearer {HUGGINGFACEHUB_API_TOKEN}"}
    payloads = {"inputs": message}
    response = requests.post(API_URL, headers=headers, json=payloads)

    with open("audio.wav", 'wb') as file: # for me .wav worked instead of .flac
        file.write(response.content)

# scenario = img2text("photo.jpg") 
# story = generate_story(scenario) 
# text2speech(story)





# main function for UI layer
def main():
    st.set_page_config(page_title="Image 2 Audio Story", page_icon="🩵")
    
    st.header("Turn image into a short audio story")
    uploaded_file = st.file_uploader("Choose an image...", type="jpg")
    
    if uploaded_file is not None:
        print(uploaded_file)
        bytes_data = uploaded_file.getvalue()
        with open(uploaded_file.name, "wb") as file:
            file.write(bytes_data)
        st.image(uploaded_file, caption="Uploaded Image.",
                 use_container_width=True)
        
        scenario = img2text(uploaded_file.name)
        story = generate_story(scenario)
        text2speech(story)
        
        with st.expander("scenario"):
            st.write(scenario)
        with st.expander("story"):
            st.write(story)
        
        st.audio("audio.wav")
        
if __name__ == '__main__':    
    main()