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 = """

✨Tomoniai's Gemini Pro Chat✨

""" 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() # 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 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)