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import os
import streamlit as st
import requests
from transformers import pipeline
import openai
from langchain import LLMChain, PromptTemplate
from langchain import HuggingFaceHub
# Suppressing all warnings
import warnings
warnings.filterwarnings("ignore")
api_token = os.getenv('H_TOKEN')
# 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
model = "tiiuae/falcon-7b-instruct"
llm = HuggingFaceHub(
huggingfacehub_api_token = api_token,
repo_id = model,
verbose = False,
model_kwargs = {"temperature":0.2, "max_new_tokens": 4000})
def generate_story(scenario, llm):
template= """You are a story teller.
You get a scenario as an input text, and generates a short story out of it.
Context: {scenario}
Story:
"""
prompt = PromptTemplate(template=template, input_variables=["scenario"])
#Let's create our LLM chain now
chain = LLMChain(prompt=prompt, llm=llm)
story = chain.predict(scenario=scenario)
start_index = story.find("Story:") + len("Story:")
# Extract the text after "Story:"
story = story[start_index:].strip()
return story
# 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 {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"])
# Parameters for LLM model (in the sidebar)
st.sidebar.markdown("# LLM Inference Configuration Parameters")
top_k = st.sidebar.number_input("Top-K", min_value=1, max_value=100, value=5)
top_p = st.sidebar.number_input("Top-P", min_value=0.0, max_value=1.0, value=0.8)
temperature = st.sidebar.number_input("Temperature", min_value=0.1, max_value=2.0, value=1.5)
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 = generate_story(scenario, llm) # Generates a story based on the image text, LLM params
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() |