app.py

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 = "Image Prompter"
EXAMPLE_INPUT = "A Reflective cat between stars."

# Path to your local image file
enticing_image_path = "C:/Users/alain/Downloads/enticing_image.jpg"

style = """
    div {
        text-align: center;
        background-color: #f4f4f4;
        padding: 20px;
        border-radius: 10px;
        position: relative;
    }
    
    h1 {
        color: #333;
    }

    img.enticing {
        width: 100px;
        height: 100px;
        border-radius: 50%;
        position: absolute;
        top: 0;
        right: 0;
    }

    img.main {
        width: 300px;
        height: 300px;
        border-radius: 50%;
    }

    p {
        font-size: 18px;
        color: #555;
    }
"""

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):
    input_prompt = "\n" + system_prompt + "</s>\n\n"
    for interaction in chatbot:
        input_prompt = input_prompt + str(interaction[0]) + "</s>\n\n" + str(interaction[1]) + "\n</s>\n\n"

    input_prompt = input_prompt + str(message) + "</s>\n"
    return input_prompt

def post_request_beta(payload):
    response = requests.post(zephyr_7b_beta, headers=HEADERS, json=payload)
    response.raise_for_status()
    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("", ) + len("")
    response = response[text_start:]
    return response

welcome_preview_message = f"""
Expand your imagination and broaden your horizons with LLM. Welcome to **{TITLE}**!:\nThis is a chatbot that can generate detailed prompts for image generation models based on simple and short user input.\nSay something like: 

"{EXAMPLE_INPUT}"
"""

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, title=None, style=style)

demo.launch(share=True)