File size: 3,920 Bytes
e15f81c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
# 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 generated successfully.")
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',
)
print("Constructing prompt for story generation...")
content_prompt = f'''Based on the image description "{img_text}", conclude the story.
Resolve the conflict or summarize the outcome of the situation. Ensure the story MUST
have a definitive ending. The end.'''
print("Preparing message sequences for interaction...")
messages = [
{"role": "system", "content": "Once upon a time..."},
{"role": "user", "content": img_text, "temperature": 1},
{"role": "system",
"content": content_prompt,
"temperature": 0.7},
]
print("Generating story completion using the AI model...")
chat_completion = client.chat.completions.create(
messages=messages,
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
max_tokens=200)
print("Story generated successfully.")
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}
print("Sending request for speech synthesis...")
response = requests.post(API_URL, headers=headers, json=payloads)
print("Saving synthesized speech to audio file...")
with open('audio_story.mp3', 'wb') as file:
file.write(response.content)
print("Text-to-speech conversion completed.")
# 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
''') |