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 = """Gemini Playground 💬
"""
SUBTITLE = """Play with Gemini Pro and Gemini Pro Vision API
"""
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:
chatbot.append((text_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,
categories: Optional[List[str]],
threshold: str,
chatbot: CHAT_HISTORY,
):
if len(chatbot) == 0:
return chatbot
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."
)
safety_settings = []
for category in categories:
safety_settings.append({"category": category, "threshold": threshold})
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,
safety_settings=safety_settings,
)
else:
messages = preprocess_chat_history(chatbot)
model = genai.GenerativeModel("gemini-pro")
response = model.generate_content(
messages,
stream=True,
generation_config=generation_config,
safety_settings=safety_settings,
)
# streaming effect
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
google_key_component = gr.Textbox(
label="GOOGLE API KEY",
value="",
type="password",
placeholder="...",
info="You have to provide your own GOOGLE_API_KEY for this app to function properly",
visible=GOOGLE_API_KEY is None,
)
chatbot_component = gr.Chatbot(
label="Gemini",
bubble_full_width=False,
avatar_images=AVATAR_IMAGES,
scale=2,
height=400,
)
text_prompt_component = gr.Textbox(
placeholder="Hi there! [press Enter]", 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",
info=(
"Temperature controls the degree of randomness in token selection. Lower "
"temperatures are good for prompts that expect a true or correct response, "
"while higher temperatures can lead to more diverse or unexpected results. "
),
)
max_output_tokens_component = gr.Slider(
minimum=1,
maximum=2048,
value=1024,
step=1,
label="Token limit",
info=(
"Token limit determines the maximum amount of text output from one prompt. A "
"token is approximately four characters. The max value is 2048."
),
)
stop_sequences_component = gr.Textbox(
label="Add stop sequence",
value="",
type="text",
placeholder="STOP, END",
info=(
"A stop sequence is a series of characters (including spaces) that stops "
"response generation if the model encounters it. The sequence is not included "
"as part of the response. You can add up to five stop sequences."
),
)
top_k_component = gr.Slider(
minimum=1,
maximum=40,
value=32,
step=1,
label="Top-K",
info=(
"Top-k changes how the model selects tokens for output. A top-k of 1 means the "
"selected token is the most probable among all tokens in the model’s "
"vocabulary (also called greedy decoding), while a top-k of 3 means that the "
"next token is selected from among the 3 most probable tokens (using "
"temperature)."
),
)
top_p_component = gr.Slider(
minimum=0,
maximum=1,
value=1,
step=0.01,
label="Top-P",
info=(
"Top-p changes how the model selects tokens for output. Tokens are selected "
"from most probable to least until the sum of their probabilities equals the "
"top-p value. For example, if tokens A, B, and C have a probability of .3, .2, "
"and .1 and the top-p value is .5, then the model will select either A or B as "
"the next token (using temperature). "
),
)
category_dropdown_component = gr.Dropdown(
label="Category",
choices=[
"HARM_CATEGORY_DANGEROUS",
"HARM_CATEGORY_HARASSMENT",
"HARM_CATEGORY_HATE_SPEECH",
"HARM_CATEGORY_SEXUALLY_EXPLICIT",
],
value=[
"HARM_CATEGORY_DANGEROUS",
"HARM_CATEGORY_HARASSMENT",
"HARM_CATEGORY_HATE_SPEECH",
"HARM_CATEGORY_SEXUALLY_EXPLICIT",
],
info=(
"The category of a rating."
"These categories cover various kinds of harms that developers may wish to adjust."
),
multiselect=True,
)
threshold_dropdown_component = gr.Dropdown(
label="Threshold",
choices=[
"BLOCK_LOW_AND_ABOVE",
"BLOCK_MEDIUM_AND_ABOVE",
"BLOCK_ONLY_HIGH",
"BLOCK_NONE",
],
value="BLOCK_NONE",
info=("Block at and beyond a specified harm probability."),
)
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,
category_dropdown_component,
threshold_dropdown_component,
chatbot_component,
]
with gr.Blocks() as demo:
gr.HTML(TITLE)
gr.HTML(SUBTITLE)
gr.HTML(DUPLICATE)
with gr.Column():
google_key_component.render()
chatbot_component.render()
with gr.Row():
text_prompt_component.render()
clear_component = gr.ClearButton([text_prompt_component, chatbot_component])
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("Safe Setting", open=False):
category_dropdown_component.render()
threshold_dropdown_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)