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 = """

🎮Chat with Gemini 1.5 Pro🔥 (Deprecated)

""" SUBTITLE = """

New version here: https://huggingface.co/spaces/NotAiLOL/Gemini-Playground-Beta-Preview

Try Gemini 1.5 Pro Experimental 0801 🐦‍🔥 -- Beat GPT-4o in Lmsys Leaderboard (2024/8/4)

""" NOTICES = """ Notices: - UPDATES (2024-8-12): END OF SUPPORT, new version: https://huggingface.co/spaces/NotAiLOL/Gemini-Playground-Beta-Preview - This version will be removed on the 1st Sep 2024. """ DUPLICATE = """
Duplicate Space Duplicate the Space and run securely with your GOOGLE API KEY.
""" 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, # model_name: str, # files: Optional[List[str]], # temperature: float, # max_output_tokens: int, # stop_sequences: str, # top_k: int, # top_p: float, # 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.") # 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(model_name) # response = model.generate_content( # text_prompt + image_prompt, # stream=True, # generation_config=generation_config) # else: # messages = preprocess_chat_history(chatbot) # model = genai.GenerativeModel(model_name) # response = model.generate_content( # messages, # stream=True, # generation_config=generation_config) # # 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 # ------------------------------------------------------------------- def bot( google_key: str, model_name: str, files: Optional[List[str]], temperature: float, max_output_tokens: int, stop_sequences: str, top_k: int, top_p: float, 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.") 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(model_name) response = model.generate_content( text_prompt + image_prompt, stream=True, generation_config=generation_config) else: messages = preprocess_chat_history(chatbot) model = genai.GenerativeModel(model_name) response = model.generate_content( messages, stream=True, generation_config=generation_config ) # streaming effect chatbot[-1][1] = "" for chunk in response: if not chunk.text: print("chunk.text is empty") continue print(f"chunk.text: {chunk.text}") try: for i in range(0, len(chunk.text)): section = chunk.text[i:i + 1] chatbot[-1][1] += section time.sleep(0.01) yield chatbot except IndexError as e: print(f"IndexError: {e}") # Handle the error appropriately # ------------------------------------------------------------------- model_selection = gr.Dropdown( ["gemini-1.5-flash", "gemini-1.5-pro", "gemini-1.5-pro-exp-0801" ], label="Select Gemini Model", value="gemini-1.5-pro" ) 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=8192, value=4096, 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 4096." )) 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 = [ google_key_component, model_selection, upload_button_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) gr.HTML(SUBTITLE) gr.Markdown(NOTICES) gr.HTML(DUPLICATE) with gr.Column(): google_key_component.render() chatbot_component.render() text_prompt_component.render() with gr.Row(): model_selection.render() 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("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)