app.py

import os
import time
import uuid
from typing import List, Tuple, Optional, Union
from PIL import Image
import google.generativeai as genai
import gradio as gr
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.")

# Configuración del modelo Gemini
generation_config = {
    "temperature": 1,
    "top_p": 0.95,
    "top_k": 40,
    "max_output_tokens": 8192,
    "response_mime_type": "text/plain",
}

genai.configure(api_key=GOOGLE_API_KEY)

# Inicializar los modelos para ambas pestañas
model_with_images = genai.GenerativeModel(
    model_name="gemini-1.5-flash",
    generation_config=generation_config
)

model_text_only = genai.GenerativeModel(
    model_name="gemini-1.5-flash",
    generation_config=generation_config
)

# Inicializar la sesión de chat para el chatbot sin imágenes
chat_text_only = model_text_only.start_chat(history=[])

# Función para transformar el historial de Gradio al formato de Gemini
def transform_history(history):
    new_history = []
    for chat_entry in history:
        new_history.append({"parts": [{"text": chat_entry[0]}], "role": "user"})
        new_history.append({"parts": [{"text": chat_entry[1]}], "role": "model"})
    return new_history

# Función de respuesta que maneja el historial para el chatbot sin imágenes
def bot_response(
    model_choice: str,
    system_instruction: str,
    text_prompt: str,
    chatbot: list,
) -> Tuple[list, str]:
    """
    Envía el mensaje al modelo, obtiene la respuesta y actualiza el historial.
    """
    if not text_prompt.strip():
        return chatbot, "Por favor, escribe un mensaje válido."

    # Transformar el historial al formato que espera Gemini
    transformed_history = transform_history(chatbot)

    # Configurar el modelo
    chat_text_only.history = transformed_history

    # Enviar el mensaje y obtener la respuesta
    response = chat_text_only.send_message(text_prompt)
    response.resolve()

    # Obtener el texto generado por el modelo
    generated_text = response.text

    # Actualizar el historial con la pregunta y la respuesta
    chatbot.append((text_prompt, generated_text))

    return chatbot, ""

# Funciones para manejar el chatbot con imágenes
def preprocess_image(image: Image.Image) -> Optional[Image.Image]:
    if image:
        image_height = int(image.height * 512 / image.width)
        return image.resize((512, image_height))

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

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

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

def bot(
    files: Optional[List[str]],
    model_choice: str,
    system_instruction: Optional[str],
    chatbot: list
):
    if not GOOGLE_API_KEY:
        raise ValueError("GOOGLE_API_KEY is not set.")

    genai.configure(api_key=GOOGLE_API_KEY)
    generation_config = genai.types.GenerationConfig(
        temperature=0.7,
        max_output_tokens=8192,
        top_k=10,
        top_p=0.9
    )

    if not system_instruction:
        system_instruction = "1"

    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_with_images = genai.GenerativeModel(
        model_name=model_choice,
        generation_config=generation_config,
        system_instruction=system_instruction
    )

    response = model_with_images.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

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

# Componentes comunes
chatbot_component_with_images = gr.Chatbot(label='Gemini with Images', scale=2, height=300)
chatbot_component_text_only = gr.Chatbot(label='Gemini Text Only', scale=2, height=300)
text_prompt_component = gr.Textbox(placeholder="Message...", show_label=False, autofocus=True, scale=8)
run_button_component = gr.Button(value="Run", variant="primary", scale=1)
upload_button_component = gr.UploadButton(label="Upload Images", file_count="multiple", file_types=["image"], scale=1)

# Componentes separados para cada pestaña
model_choice_component_text_only = gr.Dropdown(
    choices=["gemini-1.5-flash", "gemini-2.0-flash-exp", "gemini-1.5-pro"],
    value="gemini-1.5-flash",
    label="Select Model",
    scale=2
)

model_choice_component_with_images = gr.Dropdown(
    choices=["gemini-1.5-flash", "gemini-2.0-flash-exp", "gemini-1.5-pro"],
    value="gemini-1.5-flash",
    label="Select Model",
    scale=2
)

system_instruction_component = gr.Textbox(
    placeholder="Enter system instruction...",
    show_label=True,
    scale=8
)

with gr.Blocks() as demo:
    gr.HTML(TITLE)
    gr.HTML(SUBTITLE)
    with gr.Tabs():
        with gr.TabItem("Chatbot with Images"):
            with gr.Column():
                model_choice_component_with_images
                chatbot_component_with_images
                with gr.Row():
                    text_prompt_component
                    upload_button_component
                    run_button_component
                with gr.Accordion("System Instruction", open=False):
                    system_instruction_component

            run_button_component.click(
                fn=user,
                inputs=[text_prompt_component, chatbot_component_with_images],
                outputs=[text_prompt_component, chatbot_component_with_images],
                queue=False
            ).then(
                fn=bot,
                inputs=[upload_button_component, model_choice_component_with_images, system_instruction_component, chatbot_component_with_images],
                outputs=[chatbot_component_with_images],
            )

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

        with gr.TabItem("Chatbot Text Only"):
            with gr.Column():
                model_choice_component_text_only
                chatbot_component_text_only
                with gr.Row():
                    text_prompt_component
                    run_button_component
                with gr.Accordion("System Instruction", open=False):
                    system_instruction_component

            run_button_component.click(
                fn=bot_response,
                inputs=[model_choice_component_text_only, system_instruction_component, text_prompt_component, chatbot_component_text_only],
                outputs=[chatbot_component_text_only, text_prompt_component],
            )

            text_prompt_component.submit(
                fn=bot_response,
                inputs=[model_choice_component_text_only, system_instruction_component, text_prompt_component, chatbot_component_text_only],
                outputs=[chatbot_component_text_only, text_prompt_component],
            )

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