app.py

import os
import requests
from dotenv import find_dotenv, load_dotenv
from transformers import pipeline
import streamlit as st
import tensorflow

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

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("Result:", text)
    return text


def generateStory(scenario):
    template = """
You are a skilled storyteller. Your task is to create a short, engaging story based on the provided context. The story should be imaginative and concise, not exceeding 30 words.
Context: {scenario}
Example:
Context: They are having a conversation at a table with a cup of coffee.
Story: "And then she whispered a secret that changed everything," he said over coffee, eyes gleaming.
Story:
"""

    prompt = template.format(scenario=scenario)
    story_generator = pipeline("text-generation", model="gpt2")

    story = story_generator(prompt, max_new_tokens=40, num_return_sequences=1)[0]['generated_text']

    print("Story:", story)
    return story

def text2speech(message):
    API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
    headers = {"Authorization": f"Bearer {HUGGINGFACE_KEY}"}
    payloads ={
         "inputs": message
    }
    response = requests.post(API_URL, headers=headers, json=payloads)
    with open("audio.flac", "wb") as file:
        file.write(response.content)

#scenario = img2text("conversation.jpg")
#story = generateStory(scenario)
#text2speech(story)

def main():
    st.set_page_config(
        page_title="image", page_icon="🤖"
    )
    st.header("Turn your image to an 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 file", 
            use_column_width= True
            )
        
        scenario = img2text(uploaded_file.name)
        story = generateStory(scenario)
        text2speech = story

        with st.expander("Scenario: "):
            st.write(scenario)

        with st.expander("Story"):
            st.write(story)
        
        st.audio("audio.flac")

main()