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from dotenv import find_dotenv, load_dotenv
from transformers import pipeline
from langchain import PromptTemplate, LLMChain, OpenAI
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-base")
text = image_to_text(url)[0]["generated_text"]
print(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 more than 50 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)
print(story)
return story
#text to speech
def text2speech(message):
#API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
API_URL = "https://api-inference.huggingface.co/models/facebook/mms-tts-fra"
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:
file.write(response.content)
#scenario = img2text("mmd.png")
#story = generate_story(scenario)
#en_fr_translator = pipeline("translation_en_to_fr")
#story_fr = en_fr_translator(story)[0]["translation_text"]
#print(story_fr)
#text2speech(story_fr)
def main():
st.set_page_config(page_title="Img 2 audio story")
st.header("Turn img into 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_column_width=True)
scenario = img2text(uploaded_file.name)
story = generate_story(scenario)
en_fr_translator = pipeline("translation_en_to_fr")
story_fr = en_fr_translator(story)[0]["translation_text"]
text2speech(story_fr)
with st.expander("scenario"):
st.write(scenario)
with st.expander("story"):
st.write(story_fr)
st.audio("audio.wav")
if __name__ == '__main__':
main()
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