app.py

TITLE = """<h1 align="center">Gemini Playground 💬</h1>"""
SUBTITLE = """<h2 align="center">Play with Gemini Pro and Gemini Pro Vision</h2>"""

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

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

# Cargar las variables de entorno desde el archivo .env
load_dotenv()

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

# Obtener la clave de la API de las variables de entorno
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")

# Verificar que la clave de la API esté configurada
if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in environment variables.")

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]]:
    return [sequence.strip() for sequence in stop_sequences.split(",")] if stop_sequences else None

def preprocess_image(image: Image.Image) -> Optional[Image.Image]:
    if 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 upload(files: Optional[List[str]], chatbot: CHAT_HISTORY) -> CHAT_HISTORY:
    for file in files:
        image = Image.open(file).convert('RGB')
        image_preview = preprocess_image(image)
        if image_preview:
            # Display a preview of the uploaded image
            gr.Image(image_preview).render()
        image_path = cache_pil_image(image)
        chatbot.append(((image_path,), None))
    return chatbot

def user(text_prompt: str, chatbot: CHAT_HISTORY):
    if text_prompt:
        chatbot.append((text_prompt, None))
    return "", chatbot

def detect_language(text: str) -> str:
    try:
        return detect(text)
    except:
        return "en"  # En caso de no poder detectar el idioma, se usa el inglés por defecto

def bot(
    files: Optional[List[str]],
    temperature: float,
    max_output_tokens: int,
    stop_sequences: str,
    top_k: int,
    top_p: float,
    chatbot: CHAT_HISTORY
):
    if not GOOGLE_API_KEY:
        raise ValueError("GOOGLE_API_KEY is not set.")

    # Configurar la API con la clave
    genai.configure(api_key=GOOGLE_API_KEY)
    
    # Detectar el idioma del último mensaje del usuario
    last_user_message = chatbot[-1][0] if chatbot and chatbot[-1][0] else ""
    detected_language = detect_language(last_user_message)

    # Configurar la generación para que utilice el idioma detectado
    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,
        language=detected_language  # Usar el idioma detectado
    )

    text_prompt = [chatbot[-1][0]] if chatbot and chatbot[-1][0] and isinstance(chatbot[-1][0], str) else []
    image_prompt = [preprocess_image(Image.open(file).convert('RGB')) for file in files] if files else []
    model_name = 'gemini-1.5-flash'
    model = genai.GenerativeModel(model_name)
    response = model.generate_content(text_prompt + image_prompt, stream=True, generation_config=generation_config)

    chatbot[-1][1] = ""
    for chunk in response:
        for i in range(0, len(chunk.text), 10):
            section = chunk.text[i:i + 10]
            chatbot[-1][1] += section
            time.sleep(0.01)
            yield chatbot

chatbot_component = gr.Chatbot(
    label='Gemini',
    bubble_full_width=False,
    scale=2,
    height=300
)
text_prompt_component = gr.Textbox(
    placeholder="Message...", show_label=False, autofocus=True, scale=8
)
upload_button_component = gr.UploadButton(
    label="Upload Images", file_count="multiple", file_types=["image"], scale=1
)
run_button_component = gr.Button(value="Run", variant="primary", scale=1)
temperature_component = gr.Slider(
    minimum=0,
    maximum=1.0,
    value=0.4,
    step=0.05,
    label="Temperature",
)
max_output_tokens_component = gr.Slider(
    minimum=1,
    maximum=2048,
    value=1024,
    step=1,
    label="Token limit",
)
stop_sequences_component = gr.Textbox(
    label="Add stop sequence",
    value="",
    type="text",
    placeholder="STOP, END",
)
top_k_component = gr.Slider(
    minimum=1,
    maximum=40,
    value=32,
    step=1,
    label="Top-K",
)
top_p_component = gr.Slider(
    minimum=0,
    maximum=1,
    value=1,
    step=0.01,
    label="Top-P",
)

user_inputs = [
    text_prompt_component,
    chatbot_component
]

bot_inputs = [
    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)
    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=[text_prompt_component, chatbot_component],
        queue=False
    ).then(
        fn=bot, inputs=bot_inputs, outputs=[chatbot_component],
    )

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

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

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