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| # Only the two imports you requested | |
| import streamlit as st | |
| from transformers import pipeline | |
| from PIL import Image | |
| # Simple image-to-text function | |
| def img2text(image): | |
| image_to_text = pipeline("image-to-text", model="sooh-j/blip-image-captioning-base") | |
| text = image_to_text(image)[0]["generated_text"] | |
| return text | |
| # Simple text-to-story function | |
| def text2story(text): | |
| generator = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0") | |
| prompt = f"Write a short children's story based on this: {text}. The story should have a clear beginning, middle, and end. Keep it under 150 words. Once upon a time, " | |
| # Generate a longer text to ensure we get a complete story | |
| story_result = generator( | |
| prompt, | |
| max_length=300, | |
| num_return_sequences=1, | |
| temperature=0.7, | |
| do_sample=True | |
| ) | |
| story_text = story_result[0]['generated_text'] | |
| story_text = story_text.replace(prompt, "Once upon a time, ") | |
| # Find natural ending points (end of sentences) | |
| periods = [i for i, char in enumerate(story_text) if char == '.'] | |
| question_marks = [i for i, char in enumerate(story_text) if char == '?'] | |
| exclamation_marks = [i for i, char in enumerate(story_text) if char == '!'] | |
| # Combine all ending punctuation and sort | |
| all_endings = sorted(periods + question_marks + exclamation_marks) | |
| # If we have any sentence endings | |
| if all_endings: | |
| # Get the index where the story should reasonably end (after at least 100 characters) | |
| min_story_length = 100 | |
| suitable_endings = [i for i in all_endings if i >= min_story_length] | |
| if suitable_endings: | |
| # Find an ending that completes a thought (not just the first sentence) | |
| if len(suitable_endings) > 2: | |
| # Use the third sentence ending or later for a more complete story | |
| return story_text[:suitable_endings[2]+1] | |
| else: | |
| # If we don't have many sentences, use the last one we found | |
| return story_text[:suitable_endings[-1]+1] | |
| # If no good ending is found, return as is | |
| return story_text | |
| # Simple text-to-audio function | |
| def text2audio(story_text): | |
| synthesizer = pipeline("text-to-speech", model="HelpingAI/HelpingAI-TTS-v1") | |
| speech = synthesizer(story_text) | |
| return speech | |
| # Basic Streamlit interface | |
| st.title("Image to Audio Story") | |
| uploaded_file = st.file_uploader("Upload an image") | |
| if uploaded_file is not None: | |
| # Display image | |
| st.image(uploaded_file, caption="Uploaded Image") | |
| # Convert to PIL Image | |
| image = Image.open(uploaded_file) | |
| # Image to Text | |
| st.write("Generating caption...") | |
| caption = img2text(image) | |
| st.write(f"Caption: {caption}") | |
| # Text to Story | |
| st.write("Creating story...") | |
| story = text2story(caption) | |
| st.write(f"Story: {story}") | |
| # Text to Audio | |
| st.write("Generating audio...") | |
| speech_output = text2audio(story) | |
| # Play audio | |
| try: | |
| if 'audio' in speech_output and 'sampling_rate' in speech_output: | |
| st.audio(speech_output['audio'], sample_rate=speech_output['sampling_rate']) | |
| elif 'audio_array' in speech_output and 'sampling_rate' in speech_output: | |
| st.audio(speech_output['audio_array'], sample_rate=speech_output['sampling_rate']) | |
| else: | |
| st.write("Audio generated but could not be played.") | |
| except Exception as e: | |
| st.error(f"Error playing audio: {e}") |