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| import streamlit as st | |
| from PIL import Image | |
| import os | |
| import tempfile | |
| import subprocess | |
| import sys | |
| # Check for required dependencies and install if missing | |
| def check_and_install_dependencies(): | |
| required_packages = { | |
| "transformers": "transformers", | |
| "sentencepiece": "sentencepiece", | |
| "gtts": "gTTS" | |
| } | |
| missing_packages = [] | |
| for package, pip_name in required_packages.items(): | |
| try: | |
| __import__(package) | |
| except ImportError: | |
| missing_packages.append((package, pip_name)) | |
| if missing_packages: | |
| st.warning("Missing required dependencies. Please install them before continuing.") | |
| for package, pip_name in missing_packages: | |
| st.code(f"pip install {pip_name}", language="bash") | |
| if st.button("Install Dependencies Automatically"): | |
| with st.spinner("Installing dependencies..."): | |
| for package, pip_name in missing_packages: | |
| try: | |
| subprocess.check_call([sys.executable, "-m", "pip", "install", pip_name]) | |
| st.success(f"Successfully installed {pip_name}") | |
| except Exception as e: | |
| st.error(f"Failed to install {pip_name}: {str(e)}") | |
| st.info("Please restart the application after installing dependencies.") | |
| return False | |
| return True | |
| # function part | |
| # img2text | |
| def img2text(image_path): | |
| try: | |
| # Import here to ensure dependencies are checked first | |
| from transformers import pipeline | |
| # Load the image-to-text model | |
| image_to_text_model = pipeline("image-to-text", model="naver-clova-ix/donut-base") | |
| # Open the image file | |
| image = Image.open(image_path) | |
| # Extract text from the image | |
| result = image_to_text_model(image) | |
| # Get the generated text | |
| text = result[0]["generated_text"] if result else "No text detected" | |
| return text | |
| except Exception as e: | |
| st.error(f"Error processing image: {str(e)}") | |
| return f"Error: {str(e)}" | |
| # text2story | |
| def text2story(text): | |
| # For now, just return the extracted text as the story | |
| story_text = f"Here's a story based on the text: {text}" | |
| return story_text | |
| # text2audio using Google Text-to-Speech | |
| def text2audio(story_text): | |
| try: | |
| from gtts import gTTS | |
| # Create a temporary file | |
| temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix='.wav') | |
| temp_audio_path = temp_audio.name | |
| temp_audio.close() | |
| # Initialize gTTS and generate audio | |
| tts = gTTS(text=story_text, lang='en', slow=False) | |
| # Save to the temporary file | |
| tts.save(temp_audio_path) | |
| return temp_audio_path | |
| except Exception as e: | |
| st.error(f"Error generating audio: {str(e)}") | |
| return None | |
| # main part | |
| st.set_page_config(page_title="Your Image to Audio Story", | |
| page_icon="🦜") | |
| st.header("Turn Your Image to Audio Story") | |
| st.subheader("Using Donut model for text extraction") | |
| # Check dependencies before proceeding | |
| dependencies_ok = check_and_install_dependencies() | |
| if dependencies_ok: | |
| uploaded_file = st.file_uploader("Select an Image...", type=['png', 'jpg', 'jpeg', 'gif', 'bmp', 'webp']) | |
| if uploaded_file is not None: | |
| # Save the uploaded file temporarily | |
| bytes_data = uploaded_file.getvalue() | |
| image_temp_path = os.path.join(tempfile.gettempdir(), uploaded_file.name) | |
| with open(image_temp_path, "wb") as file: | |
| file.write(bytes_data) | |
| # Display the uploaded image | |
| st.image(uploaded_file, caption="Uploaded Image", | |
| use_column_width=True) | |
| # Stage 1: Image to Text | |
| with st.spinner('Processing img2text...'): | |
| extracted_text = img2text(image_temp_path) | |
| st.subheader("Extracted Text:") |