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| import streamlit as st | |
| import re | |
| import string | |
| import torch | |
| from transformers import pipeline | |
| from datasets import load_dataset | |
| def img2text(url): | |
| image_to_text_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large", use_fast=True) | |
| text = image_to_text_model(url)[0]["generated_text"] | |
| return text | |
| def text2story(text): | |
| story_generator = pipeline("text-generation", model="Qwen/Qwen2.5-1.5B", device_map="auto", return_full_text=False) | |
| prompt = f"Give me a story under 100 words based upon: {text}." | |
| generated = story_generator(prompt, max_new_tokens=140, do_sample=True) | |
| story_text = generated[0]['generated_text'] | |
| # Remove leading punctuation if present | |
| story_text = re.sub(r'^\s*[.,!?;:]+\s*', '', story_text) | |
| story_text = story_text.lstrip() | |
| # Capitalize the first alphabetic character if it's lowercase | |
| for i, char in enumerate(story_text): | |
| if char.isalpha(): | |
| if char.islower(): | |
| story_text = story_text[:i] + char.upper() + story_text[i+1:] | |
| break | |
| # Split into sentences | |
| sentences = re.split(r'(?<=[.!?])\s+', story_text.strip()) | |
| # Initialize variables | |
| current_word_count = 0 | |
| final_sentences = [] | |
| # Iterate through each sentence and accumulate until the word count is within 100 | |
| for sentence in sentences: | |
| words = sentence.split() | |
| word_count = len(words) | |
| if current_word_count + word_count > 100: | |
| break | |
| final_sentences.append(sentence) | |
| current_word_count += word_count | |
| # Join the final sentences to form the story | |
| final_story = ' '.join(final_sentences) | |
| # Ensure it ends with a punctuation mark | |
| if not final_story.endswith(('.', '!', '?')): | |
| final_story += '.' | |
| return final_story | |
| def text2audio(story_text): | |
| audio_generator = pipeline("text-to-speech", "microsoft/speecht5_tts") | |
| embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") | |
| speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) | |
| audio_output = audio_generator(story_text, forward_params={"speaker_embeddings": speaker_embedding}) | |
| return audio_output | |
| def main(): | |
| st.set_page_config(page_title="Once Upon A Time - Storytelling Application", page_icon="📖") | |
| st.header("Create a story of yours with an image!🧙") | |
| uploaded_file = st.file_uploader("Upload an image for creating your story!") | |
| if uploaded_file is not None: | |
| bytes_data = uploaded_file.getvalue() | |
| with open(uploaded_file.name, "wb") as file: | |
| file.write(bytes_data) | |
| st.image(uploaded_file, caption="Uploaded Image", use_container_width=True) | |
| st.text('Entering the scene...🏰') | |
| scenario = img2text(uploaded_file.name) | |
| st.write(scenario) | |
| st.text('Your story is going to begin...🦄') | |
| story = text2story(scenario) | |
| st.write(story) | |
| st.text('Your story is going to be told...🎧') | |
| audio_data = text2audio(story) | |
| st.audio(audio_data['audio'], format="audio/wav", start_time=0, sample_rate=audio_data['sampling_rate']) | |
| if __name__ == "__main__": | |
| main() |