import altair as alt import numpy as np import pandas as pd import streamlit as st from transformers import pipeline from PIL import Image # Creates a brief description for the pictures def generate_caption(image): with st.spinner("Analysing the Pictures for Key Message..."): # Loads the BLIP model to examine and describe the picture image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large") caption = image_to_text(image)[0]["generated_text"] return caption # Builds a story from the picture’s description def generate_story(caption): with st.spinner("Enhancing the Story for better Details..."): # Uses the text generation model to create a story based on the description pipe = pipeline("text-generation", model="TheBloke/phi-2-GGUF") story = pipe(caption)[0]['generated_text'] return story # Turns the story into audio def generate_audio(story): with st.spinner("Turning story into News audio..."): # Uses a speech model to turn description into audio pipe = pipeline("text-to-speech", model="hexgrad/Kokoro-82M") audio = pipe(story) return audio # Streamlit UI: Makes a simple interface to generate the audio # Displays the title st.title("Tool for the Reporter - Turning the News Photo into Audio") # Describes the app for users st.write("Please upload the News Photo within 200MB") # Allows picture uploads uploaded_file = st.file_uploader("Upload the Photo below", type=["png", "jpg", "jpeg"]) if uploaded_file is not None: # Shows the uploaded picture image = Image.open(uploaded_file) st.image(image, caption="Your Picture!", use_container_width=True) # Gets the picture’s description image_caption = generate_caption(image) st.subheader("Phot Description:") st.write(image_caption) # Generate the News descriptions story_telling = generate_story(image_caption) st.subheader("The News:") st.write(story_telling) # Generates audio audio = generate_audio(story_telling) if st.button("Hear the News"): st.audio(audio['audio'], format="audio/wav", start_time=0, sample_rate=audio['sampling_rate'])