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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"<BOS> {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="π")
st.header("Create a story of yours with an image!π§")
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...π°')
scenario = img2text(uploaded_file.name)
st.write(scenario)
st.text('Your story is going to begin...π¦')
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']) |