app.py

import os
import time
import uuid
from typing import List, Tuple, Optional, Dict, Union

import google.generativeai as genai
import gradio as gr
from PIL import Image

print("google-generativeai:", genai.__version__)

GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")

TITLE = """ """
SUBTITLE = """ """
DUPLICATE = """"""

AVATAR_IMAGES = (
    None,
    "https://media.roboflow.com/spaces/gemini-icon.png"
)

IMAGE_CACHE_DIRECTORY = "/tmp"
IMAGE_WIDTH = 512
CHAT_HISTORY = List[Tuple[Optional[Union[Tuple[str], str]], Optional[str]]]


def preprocess_stop_sequences(stop_sequences: str) -> Optional[List[str]]:
    if not stop_sequences:
        return None
    return [sequence.strip() for sequence in stop_sequences.split(",")]


def preprocess_image(image: Image.Image) -> Optional[Image.Image]:
    image_height = int(image.height * IMAGE_WIDTH / image.width)
    return image.resize((IMAGE_WIDTH, image_height))


def cache_pil_image(image: Image.Image) -> str:
    image_filename = f"{uuid.uuid4()}.jpeg"
    os.makedirs(IMAGE_CACHE_DIRECTORY, exist_ok=True)
    image_path = os.path.join(IMAGE_CACHE_DIRECTORY, image_filename)
    image.save(image_path, "JPEG")
    return image_path


def preprocess_chat_history(
    history: CHAT_HISTORY
) -> List[Dict[str, Union[str, List[str]]]]:
    messages = []
    for user_message, model_message in history:
        if isinstance(user_message, tuple):
            pass
        elif user_message is not None:
            messages.append({'role': 'user', 'parts': [user_message]})
        if model_message is not None:
            messages.append({'role': 'model', 'parts': [model_message]})
    return messages


def upload(files: Optional[List[str]], chatbot: CHAT_HISTORY) -> CHAT_HISTORY:
    for file in files:
        image = Image.open(file).convert('RGB')
        image = preprocess_image(image)
        image_path = cache_pil_image(image)
        chatbot.append(((image_path,), None))
    return chatbot


def user(text_prompt: str, chatbot: CHAT_HISTORY):
    if text_prompt:
        # Pre-filled text to go with user input
        prefilled_text = "You are a specialized Prompt Generator focused on improving the original text while maintaining its essence. Keep the prompt length under 50 words never exceed this limit"
        full_prompt = f"{prefilled_text} {text_prompt}"
        chatbot.append((full_prompt, None))
    return "", chatbot


def bot(
    google_key: str,
    files: Optional[List[str]],
    temperature: float,
    max_output_tokens: int,
    stop_sequences: str,
    top_k: int,
    top_p: float,
    chatbot: CHAT_HISTORY
):
    if len(chatbot) == 0:
        return ''

    google_key = google_key if google_key else GOOGLE_API_KEY
    if not google_key:
        raise ValueError(
            "GOOGLE_API_KEY is not set. "
            "Please follow the instructions in the README to set it up.")

    genai.configure(api_key=google_key)
    generation_config = genai.types.GenerationConfig(
        temperature=temperature,
        max_output_tokens=max_output_tokens,
        stop_sequences=preprocess_stop_sequences(stop_sequences=stop_sequences),
        top_k=top_k,
        top_p=top_p)

    if files:
        text_prompt = [chatbot[-1][0]] \
            if chatbot[-1][0] and isinstance(chatbot[-1][0], str) \
            else []
        image_prompt = [Image.open(file).convert('RGB') for file in files]
        model = genai.GenerativeModel('gemini-pro-vision')
        response = model.generate_content(
            text_prompt + image_prompt,
            stream=True,
            generation_config=generation_config)
    else:
        messages = preprocess_chat_history(chatbot)
        model = genai.GenerativeModel('gemini-pro')
        response = model.generate_content(
            messages,
            stream=True,
            generation_config=generation_config)

    generated_text = ''
    for chunk in response:
        generated_text += chunk.text

    return generated_text

output_text_component = gr.Textbox(
    label="Generated Text",
    value="",
    placeholder="Generated text will appear here",
    scale=8,
    multiline=True,
)

def copy_text():
    output_text_component.copy()

output_text_component_copy = gr.HTML(
    "<svg xmlns='http://www.w3.org/2000/svg' width='24' height='24' viewBox='0 0 24 24' "
    "fill='none' stroke='currentColor' stroke-width='2' stroke-linecap='round' "
    "stroke-linejoin='round' class='feather feather-copy' onclick='copyText()'>"
    "<rect x='9' y='9' width='13' height='13' rx='2' ry='2'></rect>"
    "<path d='M9 15h4'></path><path d='M15 9v6'></path></svg>"
    "<script>"
    "function copyText() {"
    "var copyText = document.getElementById('output-text');"
    "copyText.select();"
    "document.execCommand('copy');"
    "alert('Copied to clipboard!');"
    "}"
    "</script>"
)

text_prompt_component = gr.Textbox(
    placeholder="Hi there! [press Enter]",
    show_label=False,
    autofocus=True,
    scale=8,
)

chatbot_component = gr.Chatbot(
    label='Gemini',
    bubble_full_width=False,
    avatar_images=AVATAR_IMAGES,
    scale=2,
    height=400
)

user_inputs = [
    text_prompt_component,
    chatbot_component
]

bot_inputs = [
    google_key_component,
    upload_button_component,
    temperature_component,
    max_output_tokens_component,
    stop_sequences_component,
    top_k_component,
    top_p_component,
    chatbot_component
]

with gr.Blocks() as demo:
    gr.HTML(TITLE)
    gr.HTML(SUBTITLE)
    gr.HTML(DUPLICATE)
    with gr.Column():
        chatbot_component.render()
        with gr.Row():
            text_prompt_component.render()
            upload_button_component.render()
            run_button_component.render()
        with gr.Accordion("Parameters", open=False):
            temperature_component.render()
            max_output_tokens_component.render()
            stop_sequences_component.render()
            with gr.Accordion("Advanced", open=False):
                top_k_component.render()
                top_p_component.render()

    run_button_component.click(
        fn=user,
        inputs=user_inputs,
        outputs=[output_text_component, chatbot_component],
        queue=False
    ).then(
        fn=bot, inputs=bot_inputs, outputs=[output_text_component_copy],
    )

    text_prompt_component.submit(
        fn=user,
        inputs=user_inputs,
        outputs=[output_text_component, chatbot_component],
        queue=False
    ).then(
        fn=bot, inputs=bot_inputs, outputs=[output_text_component_copy],
    )

    upload_button_component.upload(
        fn=upload,
        inputs=[upload_button_component, chatbot_component],
        outputs=[output_text_component, chatbot_component],
        queue=False
    )

demo.queue(max_size=99).launch(debug=False, show_error=True)