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| import gradio as gr | |
| from anthropic import Anthropic | |
| from openai import OpenAI | |
| import openai | |
| import json | |
| import uuid | |
| import os | |
| # List of available Claude models | |
| claude_models = ["claude-3-opus-20240229", "claude-3-sonnet-20240229", "claude-3-haiku-20240307"] | |
| # List of available OpenAI models | |
| openai_models = ["gpt-4", "gpt-4-32k", "gpt-3.5-turbo", "gpt-4-0125-preview", "gpt-4-turbo-preview", "gpt-4-1106-preview", "gpt-4-0613"] | |
| def generate_response(api_key, model, user_prompt): | |
| if api_key.startswith("sk-ant-"): | |
| # Set the Anthropic API key | |
| client = Anthropic(api_key=api_key) | |
| system_prompt_path = __file__.replace("app.py", "json.txt") | |
| elif api_key.startswith("sk-"): | |
| # Set the OpenAI API key | |
| client = OpenAI(api_key=api_key) | |
| system_prompt_path = __file__.replace("app.py", "json.txt") | |
| else: | |
| return "Invalid API key prefix. Please use 'sk-ant-' for Anthropic or 'sk-' for OpenAI.", "", None | |
| # Read the system prompt from a text file | |
| with open(system_prompt_path, "r") as file: | |
| system_prompt = file.read() | |
| if api_key.startswith("sk-ant-"): | |
| # Generate a response using the selected Anthropic model | |
| response = client.messages.create( | |
| system=system_prompt, | |
| messages=[{"role": "user", "content": user_prompt}], | |
| model=model, | |
| max_tokens=4096 | |
| ) | |
| response_text = response.content[0].text | |
| else: | |
| # Generate a response using the selected OpenAI model | |
| response = client.chat.completions.create( | |
| model=model, | |
| messages=[ | |
| {"role": "system", "content": system_prompt}, | |
| {"role": "user", "content": user_prompt} | |
| ], | |
| max_tokens=4096 | |
| ) | |
| response_text = response.choices[0].message.content | |
| json_string, json_json = extract_json(response_text) | |
| json_file = json_string if json_string else None | |
| create_unique_id = str(uuid.uuid4()) | |
| json_folder = __file__.replace("app.py", f"outputs/") | |
| if not os.path.exists(json_folder): | |
| os.makedirs(json_folder) | |
| path = None | |
| if json_string: | |
| with open(f"{json_folder}{json_json['name']}_{create_unique_id}.json", "w") as file: | |
| file.write(json_file) | |
| path = f"{json_folder}{json_json['name']}_{create_unique_id}.json" | |
| else: | |
| json_string = "No JSON data was found, or the JSON data was incomplete." | |
| return response_text, json_string or "", path | |
| def extract_json(generated_output): | |
| try: | |
| generated_output = generated_output.replace("```json", "").replace("```", "").strip() | |
| # Find the JSON string in the generated output | |
| json_start = generated_output.find("{") | |
| json_end = generated_output.rfind("}") + 1 | |
| json_string = generated_output[json_start:json_end] | |
| print(json_string) | |
| # Parse the JSON string | |
| json_data = json.loads(json_string) | |
| json_data['name'] = json_data['char_name'] | |
| json_data['personality'] = json_data['char_persona'] | |
| json_data['scenario'] = json_data['world_scenario'] | |
| json_data['first_mes'] = json_data['char_greeting'] | |
| # Check if all the required keys are present | |
| required_keys = ["char_name", "char_persona", "world_scenario", "char_greeting", "example_dialogue", "description"] | |
| if all(key in json_data for key in required_keys): | |
| return json.dumps(json_data), json_data | |
| else: | |
| return None, None | |
| except Exception as e: | |
| print(e) | |
| return None, None | |
| def generate_second_response(api_key, model, generated_output): | |
| if api_key.startswith("sk-ant-"): | |
| # Set the Anthropic API key | |
| client = Anthropic(api_key=api_key) | |
| system_prompt_path = __file__.replace("app.py", "diffusion.txt") | |
| elif api_key.startswith("sk-"): | |
| # Set the OpenAI API key | |
| client = OpenAI(api_key=api_key) | |
| system_prompt_path = __file__.replace("app.py", "diffusion.txt") | |
| else: | |
| return "Invalid API key prefix. Please use 'sk-ant-' for Anthropic or 'sk-' for OpenAI." | |
| # Read the system prompt from a text file | |
| with open(system_prompt_path, "r") as file: | |
| system_prompt = file.read() | |
| if api_key.startswith("sk-ant-"): | |
| # Generate a second response using the selected Anthropic model and the previously generated output | |
| response = client.messages.create( | |
| system=system_prompt, | |
| messages=[{"role": "user", "content": generated_output}], | |
| model=model, | |
| max_tokens=4096 | |
| ) | |
| response_text = response.content[0].text | |
| else: | |
| # Generate a response using the selected OpenAI model | |
| response = client.chat.completions.create( | |
| model=model, | |
| messages=[ | |
| {"role": "system", "content": system_prompt}, | |
| {"role": "user", "content": generated_output} | |
| ], | |
| max_tokens=4096 | |
| ) | |
| response_text = response.choices[0].message.content | |
| return response_text | |
| # Set up the Gradio interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# SillyTavern Character Generator") | |
| #Text explaining that you can use the API key from the Anthropic API or the OpenAI API | |
| gr.Markdown("You can use the API key from the Anthropic API or the OpenAI API. The API key should start with 'sk-ant-' for Anthropic or 'sk-' for OpenAI.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| api_key = gr.Textbox(label="API Key", type="password", placeholder="sk-ant-api03-... or sk-...") | |
| model_dropdown = gr.Dropdown(choices=[], label="Select a model") | |
| user_prompt = gr.Textbox(label="User Prompt", value="Make me a card for a panther made of translucent pastel colored goo. Its color never changes once it exists but each 'copy' has a different color. The creature comes out of a small jar, seemingly defying physics with its size. It is the size of a real panther, and as strong as one too. By default its female but is able to change gender. It can even split into multiple copies of itself if needed with no change in its own size or mass. Its outside is normally lightly squishy but solid, but on command it can become viscous like non-newtonian fluids. Be descriptive when describing this character, and make sure to describe all of its features in char_persona just like you do in description. Make sure to describe commonly used features in detail (visual, smell, taste, touch, etc).") | |
| generate_button = gr.Button("Generate JSON") | |
| with gr.Column(): | |
| generated_output = gr.Textbox(label="Generated Output") | |
| json_output = gr.Textbox(label="JSON Output") | |
| json_download = gr.File(label="Download JSON") | |
| with gr.Row(): | |
| with gr.Column(): | |
| generate_button_2 = gr.Button("Generate SDXL Prompt") | |
| with gr.Column(): | |
| generated_output_2 = gr.Textbox(label="Generated SDXL Prompt") | |
| def update_models(api_key): | |
| if api_key.startswith("sk-ant-"): | |
| return gr.Dropdown(choices=claude_models) | |
| elif api_key.startswith("sk-"): | |
| return gr.Dropdown(choices=openai_models) | |
| else: | |
| return gr.Dropdown(choices=[]) | |
| api_key.change(update_models, inputs=api_key, outputs=model_dropdown) | |
| generate_button.click(generate_response, inputs=[api_key, model_dropdown, user_prompt], outputs=[generated_output, json_output, json_download]) | |
| generate_button_2.click(generate_second_response, inputs=[api_key, model_dropdown, generated_output], outputs=generated_output_2) | |
| demo.launch() |