import streamlit as st from transformers import pipeline 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="pranavpsv/gpt2-genre-story-generator", device_map="auto", return_full_text=False) prompt = f" {text}" generated = story_generator(prompt, max_new_tokens=140, do_sample=True, temperature=0.7) story_text = generated[0]['generated_text'] # 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", model="ylacombe/vits_vctk_scottish_female") audio_output = audio_generator(story_text) return audio_output 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: print(uploaded_file) 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) audio_data = text2audio(story) st.audio(audio_data['audio'], format="audio/wav", start_time=0, sample_rate = audio_data['sampling_rate'])