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# Imports
import os
import streamlit as st
import requests
from transformers import pipeline
import openai
# Suppressing all warnings
import warnings
warnings.filterwarnings("ignore")
# Image-to-text
def img2txt(url):
print("Initializing captioning model...")
captioning_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
print("Generating text from the image...")
text = captioning_model(url, max_new_tokens=20)[0]["generated_text"]
print(text)
return text
# Text-to-story
def txt2story(img_text):
print("Initializing client...")
client = openai.OpenAI(
api_key=os.environ["TOGETHER_API_KEY"],
base_url='https://api.together.xyz',
)
messages = [
{"role": "system", "content": '''As an experienced short story writer, write story title and then create a meaningful story influenced by provided words.
Ensure stories conclude positively within 100 words. Remember the story must end within 100 words''', "temperature": 1.8},
{"role": "user", "content": f"Here is input set of words: {img_text}", "temperature": 1.5},
]
print("Story...")
chat_completion = client.chat.completions.create(
messages=messages,
model="togethercomputer/llama-2-70b-chat")
print(chat_completion.choices[0].message.content)
return chat_completion.choices[0].message.content
# Text-to-speech
def txt2speech(text):
print("Initializing text-to-speech conversion...")
API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
headers = {"Authorization": f"Bearer {os.environ['HUGGINGFACEHUB_API_TOKEN']}"}
payloads = {'inputs': text}
response = requests.post(API_URL, headers=headers, json=payloads)
with open('audio_story.mp3', 'wb') as file:
file.write(response.content)
# Streamlit web app main function
def main():
st.set_page_config(page_title="🎨 Image-to-Audio Story 🎧", page_icon="🖼️")
st.title("Turn the Image into Audio Story")
# Allows users to upload an image file
uploaded_file = st.file_uploader("# 📷 Upload an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Reads and saves uploaded image file
bytes_data = uploaded_file.read()
with open("uploaded_image.jpg", "wb") as file:
file.write(bytes_data)
st.image(uploaded_file, caption='🖼️ Uploaded Image', use_column_width=True)
# Initiates AI processing and story generation
with st.spinner("## 🤖 AI is at Work! "):
scenario = img2txt("uploaded_image.jpg") # Extracts text from the image
story = txt2story(scenario) # Generates a story based on the image text
txt2speech(story) # Converts the story to audio
st.markdown("---")
st.markdown("## 📜 Image Caption")
st.write(scenario)
st.markdown("---")
st.markdown("## 📖 Story")
st.write(story)
st.markdown("---")
st.markdown("## 🎧 Audio Story")
st.audio("audio_story.mp3")
if __name__ == '__main__':
main()
# Credits
st.markdown("### Credits")
st.caption('''
Made with ❤️ by @Aditya-Neural-Net-Ninja\n
Utilizes Image-to-text, Text Generation, Text-to-speech Transformer Models\n
Gratitude to Streamlit, 🤗 Spaces for Deployment & Hosting
''') |