app.py

import gradio as gr
from llm_inference import LLMInferenceNode
import random
from PIL import Image
import io

title = """<h1 align="center">SD 3.5 Prompt Generator</h1>
<p><center>
<a href="https://x.com/gokayfem" target="_blank">[X gokaygokay]</a>
<a href="https://github.com/gokayfem" target="_blank">[Github gokayfem]</a>
<p align="center">Generate random prompts using powerful LLMs from Hugging Face and SambaNova.</p>
</center></p>
"""

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", "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)

        # 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"
                ],
                "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"
        )


    return demo

if __name__ == "__main__":
    demo = create_interface()
    demo.launch(share=True)