from transformers import pipeline
#from transformers.utils import logging
from langchain import PromptTemplate, LLMChain
import streamlit as st

#logging.set_verbosity_error()

#API KEYS
openai_api_key = st.secrets['OPENAI_API_KEY']

url = st.text_area('enter a url to process')
def img2text(url):
    image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
    text = image_to_text(url)[0]["generated_text"]
    return text

def generate_poem(scenario, openai_api_key):

    template = """
    You are a writer and your favorite poet is Emily Dickinson.
    You can generate a short poem based on a simple phrase or sentence.     
    
    CONTEXT: {scenario}
    POEM: 
    """
    prompt = PromptTemplate(template = template, input_variables = ['scenario'])
    
    # Call LLM Chain with OpenAI's API in LangChain
    
    poem_LLM = LLMChain(llm = ChatOpenAI(
                        openai_api_key = openai_api_key,
                        model_name = "gpt-3.5-turbo",
                        temperature = 0.9,
                        prompt = prompt,
                        verbose = True
                        ))

    poem = poem_LLM.predict(scenario = scenario)
    print(poem)
    return poem
    
if url:

    scenario = img2text(url)
    poem_output = generate_poem(scenario, openai_api_key)
    st.write(poem_output)