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import os
import time
from typing import Any
import requests
import streamlit as st
from dotenv import find_dotenv, load_dotenv
from langchain.chains import LLMChain
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from transformers import pipeline
from utils.custom import css_code
load_dotenv(find_dotenv())
HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
def progress_bar(amount_of_time: int) -> Any:
"""
A very simple progress bar the increases over time,
then disappears when it reached completion
:param amount_of_time: time taken
:return: None
"""
progress_text = "Please wait, Generative models hard at work"
my_bar = st.progress(0, text=progress_text)
for percent_complete in range(amount_of_time):
time.sleep(0.04)
my_bar.progress(percent_complete + 1, text=progress_text)
time.sleep(1)
my_bar.empty()
def generate_text_from_image(url: str) -> str:
"""
A function that uses the blip model to generate text from an image.
:param url: image location
:return: text: generated text from the image
"""
image_to_text: Any = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
generated_text: str = image_to_text(url)[0]["generated_text"]
print(f"IMAGE INPUT: {url}")
print(f"GENERATED TEXT OUTPUT: {generated_text}")
return generated_text
def generate_story_from_text(scenario: str) -> str:
"""
A function using a prompt template and GPT to generate a short story. LangChain is also
used for chaining purposes
:param scenario: generated text from the image
:return: generated story from the text
"""
prompt_template: str = f"""
You are a talented story teller who can create a story from a simple narrative./
Create a story using the following scenario; the story should have be maximum 50 words long;
CONTEXT: {scenario}
STORY:
"""
prompt: PromptTemplate = PromptTemplate(template=prompt_template, input_variables=["scenario"])
llm: Any = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.9)
story_llm: Any = LLMChain(llm=llm, prompt=prompt, verbose=True)
generated_story: str = story_llm.predict(scenario=scenario)
print(f"TEXT INPUT: {scenario}")
print(f"GENERATED STORY OUTPUT: {generated_story}")
return generated_story
def generate_speech_from_text(message: str) -> Any:
"""
A function using the ESPnet text to speech model from HuggingFace
:param message: short story generated by the GPT model
:return: generated audio from the short story
"""
API_URL: str = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
headers: dict[str, str] = {"Authorization": f"Bearer {HUGGINGFACE_API_TOKEN}"}
payloads: dict[str, str] = {
"inputs": message
}
response: Any = requests.post(API_URL, headers=headers, json=payloads)
with open("generated_audio.flac", "wb") as file:
file.write(response.content)
def main() -> None:
"""
Main function
:return: None
"""
st.set_page_config(page_title= "IMAGE TO STORY CONVERTER", page_icon= "🖼️")
st.markdown(css_code, unsafe_allow_html=True)
with st.sidebar:
st.image("img/gkj.jpg")
st.write("---")
st.write("AI App created by @ Gurpreet Kaur")
st.header("Image-to-Story Converter")
uploaded_file: Any = st.file_uploader("Please choose a file to upload", type="jpg")
if uploaded_file is not None:
print(uploaded_file)
bytes_data: Any = 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)
progress_bar(100)
scenario: str = generate_text_from_image(uploaded_file.name)
story: str = generate_story_from_text(scenario)
generate_speech_from_text(story)
with st.expander("Generated Image scenario"):
st.write(scenario)
with st.expander("Generated short story"):
st.write(story)
st.audio("generated_audio.flac")
if __name__ == "__main__":
main() |