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import os
from pathlib import Path
import streamlit as st
from transformers import pipeline
from dotenv import load_dotenv
from langchain import PromptTemplate, LLMChain, OpenAI
import requests
if Path(".env").is_file():
load_dotenv(".env")
st.set_page_config(layout="wide")
HF_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
def img2Text(url):
image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
text = image_to_text(url)[0]["generated_text"]
st.subheader("Caption :")
st.subheader(text)
return text
#llm
def generate_story(scenario):
template = """
You are a story teller;
You can generate a short story based on a simple narrative, the story should be no momre than 20 words;
CONTEXT: {scenario}
STORY:
"""
prompt = PromptTemplate(template=template,input_variables=["scenario"])
story_llm = LLMChain(llm=OpenAI(
model_name="gpt-3.5-turbo", temperature=1), prompt=prompt, verbose=True)
story = story_llm.predict(scenario=scenario)
st.subheader("Story :")
st.subheader(story)
return story
scenario = img2Text("photo.jpg")
story = generate_story(scenario)
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