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| # import part | |
| import streamlit as st | |
| from transformers import pipeline | |
| import textwrap | |
| import numpy as np | |
| import soundfile as sf | |
| import tempfile | |
| import os | |
| from PIL import Image | |
| import string | |
| # Initialize pipelines with caching | |
| def load_pipelines(): | |
| captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large") | |
| storyer = pipeline("text-generation", model="gpt2") | |
| tts = pipeline("text-to-speech", model="facebook/mms-tts-eng") | |
| return captioner, storyer, tts | |
| captioner, storyer, tts = load_pipelines() | |
| # Function part | |
| # Function to generate content from an image | |
| def generate_content(image): | |
| pil_image = Image.open(image) | |
| # Generate caption | |
| caption = captioner(pil_image)[0]["generated_text"] | |
| st.write("**🌟 What's in the picture: 🌟**") | |
| st.write(caption) | |
| # Create prompt for story | |
| prompt = ( | |
| f"Write a funny, interesting children's story centered on this scene: {caption}\n" | |
| f"Story in third-person narrative, describing this scene exactly: {caption} " | |
| f"Mention the exact place, location, or venue within {caption}. " | |
| f"Avoid numbers, random letter combinations, and single-letter words.") | |
| # Generate raw story with optimized parameters | |
| raw = storyer( | |
| prompt, | |
| max_new_tokens=100, | |
| temperature=0.6, | |
| top_p=0.85, | |
| no_repeat_ngram_size=0, | |
| return_full_text=False | |
| )[0]["generated_text"].strip() | |
| # Combine cleaning and word trimming in one step | |
| # Use regex to keep only allowed characters and remove single-letter words | |
| allowed_pattern = re.compile(r'[a-zA-Z0-9.,!?"\'-]+\b(?<!\b\w\b)') | |
| clean_raw = ' '.join(word for word in re.findall(allowed_pattern, raw) if len(word) > 1) | |
| def generate_story(raw, caption, tts): | |
| # Split into words and trim to 100 words | |
| words = raw.split() | |
| story = " ".join(words[:100]) | |
| # Clean the story using clean_generated_story | |
| story = clean_generated_story(raw) | |
| # Display story in Streamlit | |
| st.write("**📖 Your funny story: 📖**") | |
| st.write(story) | |
| # Generate audio from cleaned story | |
| chunks = textwrap.wrap(story, width=200) | |
| audio = np.concatenate([tts(chunk)["audio"].squeeze() for chunk in chunks]) | |
| # Save audio to temporary file | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file: | |
| sf.write(temp_file.name, audio, tts.model.config.sampling_rate) | |
| temp_file_path = temp_file.name | |
| return caption, story, temp_file_path | |
| # Streamlit UI | |
| st.title("😎Story Maker") | |
| st.markdown("Upload a picture, I will generate a story for you") | |
| uploaded_image = st.file_uploader("Choose your picture", type=["jpg", "jpeg", "png"]) | |
| # Streamlit UI (modified image display section) | |
| if uploaded_image is None: | |
| st.image("https://example.com/placeholder_image.jpg", caption="Upload your picture here!", use_container_width=True) | |
| else: | |
| st.image(uploaded_image, caption="Your Picture ", use_container_width=True) | |
| if st.button("Generate a story"): | |
| if uploaded_image is not None: | |
| with st.spinner("Processing"): | |
| caption, story, audio_path = generate_content(uploaded_image) | |
| st.success(" Your story is ready!😊") | |
| st.audio(audio_path, format="audio/wav") | |
| os.remove(audio_path) | |
| else: | |
| st.warning("Please upload a picture first! ") |