import gradio as gr from llm_inference import LLMInferenceNode import random title = """

Random Prompt Generator

[X gokaygokay] [Github gokayfem]

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

""" # Global variable to store selected prompt type selected_prompt_type = "Long" # Default value 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_type = gr.Dropdown( choices=["Long", "Short", "Medium", "OnlyObjects", "NoFigure", "Landscape", "Fantasy"], label="Prompt Type", value="Long", interactive=True ) # Function to update the selected prompt type def update_prompt_type(value): global selected_prompt_type selected_prompt_type = value print(f"Updated prompt type: {selected_prompt_type}") return value # Connect the update_prompt_type function to the prompt_type dropdown prompt_type.change(update_prompt_type, inputs=[prompt_type], outputs=[prompt_type]) 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="Groq" ) api_key = gr.Textbox(label="API Key", type="password", visible=False) model = gr.Dropdown(label="Model", choices=["llama-3.1-70b-versatile", "mixtral-8x7b-32768", "gemma2-9b-it"], value="llama-3.1-70b-versatile") with gr.Row(): # **Single Button for Generating Prompt and Text** generate_button = gr.Button("Generate Random Prompt with LLM") with gr.Row(): text_output = gr.Textbox(label="LLM Generated Text", lines=10, show_copy_button=True) # 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", "gemma2-9b-it" ], "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): global selected_prompt_type # Declare as global try: # Step 1: Generate Prompt dynamic_seed = random.randint(0, 1000000) 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: # Inform the system to create a random prompt based on the selected prompt_type 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, # Added the missing 'poster' argument prompt_type=selected_prompt_type, custom_base_prompt=custom_base_prompt, provider=provider, api_key=api_key, model=model_selected ) print(f"Generated Text: {result}") # Reset selected_prompt_type if necessary selected_prompt_type = "Long" 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], outputs=[text_output], api_name="generate_random_prompt_with_llm" ) return demo if __name__ == "__main__": demo = create_interface() demo.launch(share=True)