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| import gradio as gr | |
| import os | |
| import requests | |
| SYSTEM_PROMPT = "As an LLM, your job is to generate detailed prompts that start with generate the image, for image generation models based on user input. Be descriptive and specific, but also make sure your prompts are clear and concise." | |
| TITLE = "Gera Prompt" | |
| EXAMPLE_INPUT = "uma onça pintada na floresta." | |
| import gradio as gr | |
| import os | |
| import requests | |
| zephyr_7b_beta = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta/" | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"} | |
| def build_input_prompt(message, chatbot, system_prompt): | |
| """ | |
| Constructs the input prompt string from the chatbot interactions and the current message. | |
| """ | |
| input_prompt = "<|system|>\n" + system_prompt + "</s>\n<|user|>\n" | |
| for interaction in chatbot: | |
| input_prompt = input_prompt + str(interaction[0]) + "</s>\n<|assistant|>\n" + str(interaction[1]) + "\n</s>\n<|user|>\n" | |
| input_prompt = input_prompt + str(message) + "</s>\n<|assistant|>" | |
| return input_prompt | |
| def post_request_beta(payload): | |
| """ | |
| Sends a POST request to the predefined Zephyr-7b-Beta URL and returns the JSON response. | |
| """ | |
| response = requests.post(zephyr_7b_beta, headers=HEADERS, json=payload) | |
| response.raise_for_status() # Will raise an HTTPError if the HTTP request returned an unsuccessful status code | |
| return response.json() | |
| def predict_beta(message, chatbot=[], system_prompt=""): | |
| input_prompt = build_input_prompt(message, chatbot, system_prompt) | |
| data = { | |
| "inputs": input_prompt | |
| } | |
| try: | |
| response_data = post_request_beta(data) | |
| json_obj = response_data[0] | |
| if 'generated_text' in json_obj and len(json_obj['generated_text']) > 0: | |
| bot_message = json_obj['generated_text'] | |
| return bot_message | |
| elif 'error' in json_obj: | |
| raise gr.Error(json_obj['error'] + ' Please refresh and try again with smaller input prompt') | |
| else: | |
| warning_msg = f"Unexpected response: {json_obj}" | |
| raise gr.Error(warning_msg) | |
| except requests.HTTPError as e: | |
| error_msg = f"Request failed with status code {e.response.status_code}" | |
| raise gr.Error(error_msg) | |
| except json.JSONDecodeError as e: | |
| error_msg = f"Failed to decode response as JSON: {str(e)}" | |
| raise gr.Error(error_msg) | |
| def test_preview_chatbot(message, history): | |
| response = predict_beta(message, history, SYSTEM_PROMPT) | |
| text_start = response.rfind("<|assistant|>", ) + len("<|assistant|>") | |
| response = response[text_start:] | |
| return response | |
| welcome_preview_message = f""" | |
| **{TITLE}**! Gere prompts detalhados para modelos de geração de imagens com base em entradas simples e curtas do usuário. | |
| """ | |
| chatbot_preview = gr.Chatbot(layout="panel", value=[(None, welcome_preview_message)]) | |
| textbox_preview = gr.Textbox(scale=7, container=False, value=EXAMPLE_INPUT) | |
| demo = gr.ChatInterface(test_preview_chatbot, chatbot=chatbot_preview, textbox=textbox_preview) | |
| demo.queue().launch(show_api=False) |