import gradio as gr from llm_inference import LLMInferenceNode import random from PIL import Image import io title = """

Random Prompt Generator

[X gokaygokay] [Github gokayfem] [Flux Realtime KingNish]

Generate random prompts using powerful LLMs from Hugging Face, Groq, and SambaNova.

""" def create_interface(): llm_node = LLMInferenceNode() with gr.Blocks(theme='bethecloud/storj_theme') as demo: gr.HTML(title) with gr.Row(): with gr.Column(scale=2): custom = gr.Textbox(label="Custom Input Prompt (optional)", lines=3) prompt_types = ["Random", "Long", "Short", "Medium", "OnlyObjects", "NoFigure", "Landscape", "Fantasy"] prompt_type = gr.Dropdown( choices=prompt_types, label="Prompt Type", value="Random", interactive=True ) # Add a State component to store the selected prompt type prompt_type_state = gr.State("Random") # Update the function to use State and handle Random option def update_prompt_type(value, state): if value == "Random": new_value = random.choice([t for t in prompt_types if t != "Random"]) print(f"Random prompt type selected: {new_value}") return value, new_value print(f"Updated prompt type: {value}") return value, value # Connect the update_prompt_type function to the prompt_type dropdown prompt_type.change(update_prompt_type, inputs=[prompt_type, prompt_type_state], outputs=[prompt_type, prompt_type_state]) with gr.Column(scale=2): with gr.Accordion("LLM Prompt Generation", open=False): long_talk = gr.Checkbox(label="Long Talk", value=True) compress = gr.Checkbox(label="Compress", value=True) compression_level = gr.Dropdown( choices=["soft", "medium", "hard"], label="Compression Level", value="hard" ) custom_base_prompt = gr.Textbox(label="Custom Base Prompt", lines=5) # LLM Provider Selection llm_provider = gr.Dropdown( choices=["Hugging Face", "Groq", "SambaNova"], label="LLM Provider", value="Hugging Face" ) api_key = gr.Textbox(label="API Key", type="password", visible=False) model = gr.Dropdown(label="Model", choices=["Qwen/Qwen2.5-72B-Instruct","meta-llama/Meta-Llama-3.1-70B-Instruct","mistralai/Mixtral-8x7B-Instruct-v0.1","mistralai/Mistral-7B-Instruct-v0.3"], value="Qwen/Qwen2.5-72B-Instruct") with gr.Row(): # **Single Button for Generating Prompt and Text** generate_button = gr.Button("Generate Prompt") with gr.Row(): text_output = gr.Textbox(label="LLM Generated Text", lines=10, show_copy_button=True) image_output = gr.Image(label="Generated Image", type="filepath") # Updated Models based on provider def update_model_choices(provider): provider_models = { "Hugging Face": [ "Qwen/Qwen2.5-72B-Instruct", "meta-llama/Meta-Llama-3.1-70B-Instruct", "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.3" ], "Groq": [ "llama-3.1-70b-versatile", "mixtral-8x7b-32768", "llama-3.2-90b-text-preview" ], "SambaNova": [ "Meta-Llama-3.1-70B-Instruct", "Meta-Llama-3.1-405B-Instruct", "Meta-Llama-3.1-8B-Instruct" ], } models = provider_models.get(provider, []) return gr.Dropdown(choices=models, value=models[0] if models else "") def update_api_key_visibility(provider): return gr.update(visible=False) # No API key required for selected providers llm_provider.change( update_model_choices, inputs=[llm_provider], outputs=[model] ) llm_provider.change( update_api_key_visibility, inputs=[llm_provider], outputs=[api_key] ) # **Unified Function to Generate Prompt and Text** def generate_random_prompt_with_llm(custom_input, prompt_type, long_talk, compress, compression_level, custom_base_prompt, provider, api_key, model_selected, prompt_type_state): try: # Step 1: Generate Prompt dynamic_seed = random.randint(0, 1000000) # Update prompt_type if it's "Random" if prompt_type == "Random": prompt_type = random.choice([t for t in prompt_types if t != "Random"]) print(f"Random prompt type selected: {prompt_type}") if custom_input and custom_input.strip(): prompt = llm_node.generate_prompt(dynamic_seed, prompt_type, custom_input) print(f"Using Custom Input Prompt.") else: prompt = llm_node.generate_prompt(dynamic_seed, prompt_type, f"Create a random prompt based on the '{prompt_type}' type.") print(f"No Custom Input Prompt provided. Generated prompt based on prompt_type: {prompt_type}") print(f"Generated Prompt: {prompt}") # Step 2: Generate Text with LLM poster = False # Set a default value or modify as needed result = llm_node.generate( input_text=prompt, long_talk=long_talk, compress=compress, compression_level=compression_level, poster=poster, prompt_type=prompt_type, # Use the updated prompt_type here custom_base_prompt=custom_base_prompt, provider=provider, api_key=api_key, model=model_selected ) print(f"Generated Text: {result}") return result except Exception as e: print(f"An error occurred: {e}") return f"Error occurred while processing the request: {str(e)}" # **Connect the Unified Function to the Single Button** generate_button.click( generate_random_prompt_with_llm, inputs=[custom, prompt_type, long_talk, compress, compression_level, custom_base_prompt, llm_provider, api_key, model, prompt_type_state], outputs=[text_output], api_name="generate_random_prompt_with_llm" ) # Add image generation button generate_image_button = gr.Button("Generate Image") # Function to generate image def generate_image(text): try: seed = random.randint(0, 1000000) image_path = llm_node.generate_image(text, seed=seed) print(f"Image generated: {image_path}") return image_path except Exception as e: print(f"An error occurred while generating the image: {e}") return None # Connect the image generation button generate_image_button.click( generate_image, inputs=[text_output], outputs=[image_output] ) return demo if __name__ == "__main__": demo = create_interface() demo.launch(share=True)