|
import os |
|
import time |
|
from typing import List, Tuple, Optional, Dict |
|
|
|
import google.generativeai as genai |
|
import gradio as gr |
|
from PIL import Image |
|
|
|
print("google-generativeai:", genai.__version__) |
|
|
|
GG_API_KEY = os.environ.get("GG_API_KEY") |
|
oaiusr = os.environ.get("OAI_USR") |
|
oaipwd = os.environ.get("OAI_PWD") |
|
|
|
TITLE = """<h2 align="center">✨Tomoniai's Gemini Pro Chat✨</h2>""" |
|
|
|
AVATAR_IMAGES = ("./user.png", "./botg.png") |
|
|
|
IMAGE_WIDTH = 512 |
|
|
|
|
|
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 preprocess_chat_history( |
|
history: List[Tuple[Optional[str], Optional[str]]] |
|
) -> List[Dict[str, List[str]]]: |
|
messages = [] |
|
for user_message, model_message in history: |
|
if 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 user(text_prompt: str, chatbot: List[Tuple[str, str]]): |
|
return "", chatbot + [[text_prompt, None]] |
|
|
|
|
|
def bot( |
|
image_prompt: Optional[Image.Image], |
|
temperature: float, |
|
max_output_tokens: int, |
|
stop_sequences: str, |
|
top_k: int, |
|
top_p: float, |
|
chatbot: List[Tuple[str, str]] |
|
): |
|
|
|
text_prompt = chatbot[-1][0] |
|
genai.configure(api_key=GG_API_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 image_prompt is None: |
|
model = genai.GenerativeModel('gemini-pro') |
|
response = model.generate_content( |
|
preprocess_chat_history(chatbot), |
|
stream=True, |
|
generation_config=generation_config) |
|
response.resolve() |
|
else: |
|
image_prompt = preprocess_image(image_prompt) |
|
model = genai.GenerativeModel('gemini-pro-vision') |
|
response = model.generate_content( |
|
contents=[text_prompt, image_prompt], |
|
stream=True, |
|
generation_config=generation_config) |
|
response.resolve() |
|
|
|
|
|
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 |
|
|
|
|
|
image_prompt_component = gr.Image(type="pil", label="Image", scale=1, height=400) |
|
chatbot_component = gr.Chatbot( |
|
label='Gemini', |
|
bubble_full_width=False, |
|
avatar_images=AVATAR_IMAGES, |
|
scale=8, |
|
height=400 |
|
) |
|
text_prompt_component = gr.Textbox( |
|
placeholder="Hi there!", |
|
scale=8, |
|
label="Ask me anything and press Enter" |
|
) |
|
run_button_component = gr.Button(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 default 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). " |
|
)) |
|
|
|
user_inputs = [ |
|
text_prompt_component, |
|
chatbot_component |
|
] |
|
|
|
bot_inputs = [ |
|
image_prompt_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) |
|
with gr.Column(): |
|
with gr.Row(): |
|
image_prompt_component.render() |
|
chatbot_component.render() |
|
with gr.Row(): |
|
text_prompt_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], |
|
) |
|
|
|
demo.queue(max_size=99).launch(auth=(oaiusr, oaipwd),show_api=False, debug=False, show_error=True) |
|
|
