| import gradio as gr | |
| import os, sys | |
| from prompting import promptingutils | |
| from imageprocessing import imageprocessingtools | |
| from openai import OpenAI | |
| from prompting.promptingutils import DEFAULT_N_SAMPLES, DEFAULT_OBJECT_THRESHOLD, DEFAULT_RANDOM_STATE | |
| AVAILABLE_LLMS = [ | |
| "vicuna-7b", | |
| "llama-7b-chat", | |
| "mistral-7b-instruct", | |
| "vicuna-13b", | |
| ] | |
| DEFAULT_TEMPERATURE = 0 | |
| LLAMA_API_TOKEN = os.environ["LLAMA_API_TOKEN"] | |
| client = OpenAI( | |
| api_key = LLAMA_API_TOKEN, | |
| base_url = "https://api.llama-api.com" | |
| ) | |
| def caption_artwork( | |
| image_filepath: os.PathLike, | |
| llm :str, | |
| temperature = DEFAULT_TEMPERATURE, | |
| items_threshold = DEFAULT_OBJECT_THRESHOLD, | |
| random_state = DEFAULT_RANDOM_STATE, | |
| n_samples_per_emotion = DEFAULT_N_SAMPLES | |
| )-> tuple: | |
| all_information = imageprocessingtools.extract_all_information_from_image(image_filepath) | |
| emotion = all_information["emotion"] | |
| colors_list = all_information["colors_list"] | |
| objects_and_probs = all_information["objects_and_probs"] | |
| objects_list = promptingutils.filter_items(objects_and_probs, items_threshold=items_threshold) | |
| user_prompt = promptingutils.get_user_prompt( | |
| colors_list=colors_list, | |
| objects_list=objects_list, | |
| emotion=emotion, | |
| n_samples_per_emotion=n_samples_per_emotion, | |
| random_state=random_state, | |
| object_threshold=items_threshold | |
| ) | |
| response = client.chat.completions.create( | |
| model = llm, | |
| messages = [ | |
| {"role": "system" , "content": "Assistant is a large language model trained by OpenAI."}, | |
| {"role": "user" , "content": user_prompt} | |
| ], | |
| temperature = temperature | |
| ) | |
| commentary_str = response.choices[0].message.content | |
| colors_str = ", ".join(colors_list) | |
| objects_str = ", ".join(objects_list) | |
| emotion_str = emotion | |
| return (emotion_str, colors_str, objects_str, commentary_str) | |
| with gr.Blocks() as demo: | |
| with gr.Column(): | |
| gr.HTML(""" | |
| <h2 style="text-align: center;"> | |
| LLMs talk about art! | |
| </h2> | |
| <p style="text-align: center;"></p> | |
| """) | |
| gr_image = gr.Image( | |
| label= "An artwork: ", | |
| value="./1665_Girl_with_a_Pearl_Earring.jpg" | |
| ) | |
| gr_model = gr.Dropdown( | |
| label= "A Large Language Model", | |
| choices = AVAILABLE_LLMS, | |
| value = AVAILABLE_LLMS[0] | |
| ) | |
| gr_emotion = gr.Textbox( | |
| label = "Evoked emotion: ", | |
| ) | |
| gr_colors = gr.Textbox( | |
| label = "Main colors: ", | |
| ) | |
| gr_objects = gr.Textbox( | |
| label = "Main objects present: ", | |
| ) | |
| gr_commentary = gr.Textbox( | |
| label = "Commentary on the artwork:", | |
| ) | |
| btn = gr.Button(value="Submit your image!") | |
| btn.click( | |
| caption_artwork, | |
| inputs=[gr_image, gr_model], | |
| outputs=[gr_emotion, gr_colors, gr_objects, gr_commentary] | |
| ) | |
| """ | |
| def greet(name): | |
| return "Hello " + name + "!!" | |
| iface = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| iface.launch() | |
| """ | |
| if __name__ == "__main__": | |
| demo.launch(allowed_paths = [os.path.dirname(__file__)]) |