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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, Union
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_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 bot(
files: Optional[List[str]],
model_choice: str,
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)
generation_config = genai.types.GenerationConfig(
temperature=0.7, # Valor predeterminado
max_output_tokens=8192, # Fijar el límite de tokens a 8,192
top_k=10, # Valor predeterminado
top_p=0.9 # Valor predeterminado
)
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 = genai.GenerativeModel(model_choice)
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)
model_choice_component = 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
)
user_inputs = [
text_prompt_component,
chatbot_component
]
bot_inputs = [
upload_button_component,
model_choice_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()
model_choice_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)