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| import gradio as gr | |
| from transformers import pipeline, WhisperProcessor, WhisperForConditionalGeneration | |
| from diffusers import StableDiffusionPipeline | |
| import torch | |
| # Step 1: Prompt-to-Prompt Generation using BART (or any LLM except GPT or DeepSeek) | |
| prompt_generator = pipeline("text2text-generation", model="facebook/bart-large-cnn") | |
| def generate_prompt(description: str) -> str: | |
| # Generate a detailed prompt based on a short description | |
| prompt = prompt_generator(f"Expand this description into a detailed prompt for an image: {description}", max_length=150)[0]['generated_text'] | |
| return prompt | |
| # Step 2: Prompt-to-Image Generation using Stable Diffusion v1.5 (with CPU support) | |
| stable_diffusion = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base") | |
| stable_diffusion.to("cpu") # Use CPU instead of GPU | |
| def generate_image(prompt: str): | |
| # Generate an image from the prompt using Stable Diffusion | |
| image = stable_diffusion(prompt).images[0] | |
| return image | |
| # Step 5: Voice Input Integration using Whisper for Speech-to-Text | |
| processor = WhisperProcessor.from_pretrained("openai/whisper-large") | |
| model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large") | |
| def transcribe_audio(audio): | |
| # Convert audio to text using Whisper | |
| audio_input = processor(audio, return_tensors="pt").input_features | |
| predicted_ids = model.generate(audio_input) | |
| transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) | |
| return transcription | |
| # Step 3: Gradio Interface with Multiple Controllers (Textbox, Slider, Checkbox, Audio) | |
| def process_input(description: str, creativity: float, include_background: bool): | |
| # Generate a detailed prompt | |
| prompt = generate_prompt(description) | |
| # Optionally modify prompt based on checkbox (for background inclusion) | |
| if include_background: | |
| prompt += " with a detailed, vibrant background." | |
| # Generate image based on the prompt | |
| image = generate_image(prompt) | |
| return prompt, image | |
| def process_audio_input(audio): | |
| # Convert audio to text | |
| description = transcribe_audio(audio) | |
| # Generate a prompt and image based on transcribed text | |
| prompt = generate_prompt(description) | |
| image = generate_image(prompt) | |
| return prompt, image | |
| # Define Gradio interface | |
| text_input = gr.Textbox(label="Enter Description", placeholder="E.g., A magical treehouse in the sky") | |
| creativity_slider = gr.Slider(minimum=0, maximum=1, step=0.1, label="Creativity (0 to 1)", value=0.7) | |
| background_checkbox = gr.Checkbox(label="Include Background", value=True) | |
| audio_input = gr.Audio(type="numpy", label="Speak your Description") | |
| # Create interface with both text and audio inputs | |
| interface = gr.Interface( | |
| fn=process_input, | |
| inputs=[ | |
| text_input, | |
| creativity_slider, | |
| background_checkbox | |
| ], | |
| outputs=[ | |
| gr.Textbox(label="Generated Prompt"), | |
| gr.Image(label="Generated Image") | |
| ], | |
| title="Magical Image Generator", | |
| description="Enter a short description or speak it to generate a magical image! Adjust creativity and background options.", | |
| theme="huggingface" | |
| ) | |
| # Add audio input for voice interaction | |
| interface_with_audio = gr.Interface( | |
| fn=process_audio_input, | |
| inputs=[audio_input], | |
| outputs=[gr.Textbox(label="Generated Prompt"), gr.Image(label="Generated Image")], | |
| title="Magical Image Generator with Voice Input", | |
| description="Speak a short description and generate a magical image!" | |
| ) | |
| # Launch the interface with multiple tabs for text and voice input | |
| gr.TabbedInterface([interface, interface_with_audio]).launch() |