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import os |
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import time |
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from typing import List, Tuple, Optional |
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from pathlib import Path |
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from moviepy.editor import VideoFileClip, concatenate_videoclips |
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import google.generativeai as genai |
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import gradio as gr |
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from PIL import Image |
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print("google-generativeai:", genai.__version__) |
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GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY") |
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TITLE = """<h1 align="center">🕹️ Google Gemini Playground 🔥</h1>""" |
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SUBTITLE = """<h2 align="center">Play with Gemini Text and Vision Model API 🖇️</h2>""" |
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DUPLICATE = """ |
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<div style="text-align: center; display: flex; justify-content: center; align-items: center;"> |
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<a href="https://huggingface.co/spaces/tsereno/Gemini-Powered-App?logs=container&duplicate=true"> |
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<img src="https://bit.ly/3gLdBN6" alt="Duplicate Space" style="margin-right: 10px;"> |
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</a> |
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<span>Duplicate the Space and run securely with your |
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<a href="https://makersuite.google.com/app/apikey">GOOGLE API KEY</a>. |
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</span> |
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</div> |
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""" |
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IMAGE_WIDTH = 512 |
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def preprocess_stop_sequences(stop_sequences: str) -> Optional[List[str]]: |
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if not stop_sequences: |
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return None |
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return [sequence.strip() for sequence in stop_sequences.split(",")] |
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def preprocess_image(image: Image.Image) -> Optional[Image.Image]: |
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image_height = int(image.height * IMAGE_WIDTH / image.width) |
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return image.resize((IMAGE_WIDTH, image_height)) |
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def user(text_prompt: str, chatbot: List[Tuple[str, str]]): |
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return "", chatbot + [[text_prompt, None]] |
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from moviepy.editor import VideoFileClip, concatenate_videoclips |
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def bot( |
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google_key: str, |
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model_name: str, |
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image_prompt: Optional[Image.Image], |
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video_prompt: List[Path], |
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temperature: float, |
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max_output_tokens: int, |
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stop_sequences: str, |
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top_k: int, |
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top_p: float, |
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text_prompt_component: str, |
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chatbot: List[Tuple[str, str]] |
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): |
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google_key = google_key if google_key else GOOGLE_API_KEY |
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if not google_key: |
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raise ValueError( |
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"GOOGLE_API_KEY is not set. " |
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"Please follow the instructions in the README to set it up.") |
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text_prompt = chatbot[-1][0] |
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genai.configure(api_key=google_key) |
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generation_config = genai.types.GenerationConfig( |
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temperature=temperature, |
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max_output_tokens=max_output_tokens, |
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stop_sequences=preprocess_stop_sequences(stop_sequences=stop_sequences), |
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top_k=top_k, |
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top_p=top_p) |
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if video_prompt is not None: |
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if len(video_prompt) > 1: |
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video_clips = [VideoFileClip(str(video_path)) for video_path in video_prompt] |
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merged_video = concatenate_videoclips(video_clips) |
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video_path = "merged_video.mp4" |
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merged_video.write_videofile(video_path) |
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else: |
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video_path = str(video_prompt[0]) |
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model = genai.GenerativeModel(model_name) |
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video_file = genai.upload_file(path=video_path) |
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while video_file.state.name == "PROCESSING": |
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print('.', end='') |
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time.sleep(10) |
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video_file = genai.get_file(video_file.name) |
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if video_file.state.name == "FAILED": |
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raise ValueError(video_file.state.name) |
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response = model.generate_content( |
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contents=[video_file, text_prompt], |
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stream=True, |
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generation_config=generation_config, |
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request_options={"timeout": 600}) |
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response.resolve() |
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elif image_prompt is not None: |
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image_prompt = preprocess_image(image_prompt) |
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model = genai.GenerativeModel(model_name) |
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response = model.generate_content( |
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contents=[text_prompt, image_prompt], |
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stream=True, |
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generation_config=generation_config) |
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response.resolve() |
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else: |
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model = genai.GenerativeModel(model_name) |
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response = model.generate_content( |
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text_prompt, |
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stream=True, |
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generation_config=generation_config) |
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response.resolve() |
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chatbot[-1][1] = "" |
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for chunk in response: |
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for i in range(0, len(chunk.text), 10): |
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section = chunk.text[i:i + 10] |
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chatbot[-1][1] += section |
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time.sleep(0.01) |
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yield chatbot |
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google_key_component = gr.Textbox( |
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label="GOOGLE API KEY", |
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value="", |
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type="password", |
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placeholder="...", |
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info="You have to provide your own GOOGLE_API_KEY for this app to function properly", |
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visible=GOOGLE_API_KEY is None |
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) |
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image_prompt_component = gr.Image(type="pil", label="Image", scale=1) |
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video_prompt_component = gr.File(label="Video", file_count="multiple") |
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model_selection = gr.Dropdown(["gemini-1.5-flash-latest", "gemini-1.5-pro-latest"],label="Select Gemini Model",value="gemini-1.5-pro-latest") |
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chatbot_component = gr.Chatbot( |
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label='Gemini', |
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bubble_full_width=False, |
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scale=2 |
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) |
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text_prompt_component = gr.Textbox( |
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placeholder="Hi there!", |
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label="Ask me anything and press Enter" |
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) |
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run_button_component = gr.Button() |
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temperature_component = gr.Slider( |
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minimum=0, |
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maximum=1.0, |
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value=0.4, |
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step=0.05, |
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label="Temperature", |
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info=( |
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"Temperature controls the degree of randomness in token selection. Lower " |
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"temperatures are good for prompts that expect a true or correct response, " |
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"while higher temperatures can lead to more diverse or unexpected results. " |
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)) |
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max_output_tokens_component = gr.Slider( |
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minimum=1, |
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maximum=2048, |
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value=1024, |
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step=1, |
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label="Token limit", |
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info=( |
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"Token limit determines the maximum amount of text output from one prompt. A " |
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"token is approximately four characters. The default value is 2048." |
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)) |
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stop_sequences_component = gr.Textbox( |
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label="Add stop sequence", |
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value="", |
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type="text", |
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placeholder="STOP, END", |
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info=( |
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"A stop sequence is a series of characters (including spaces) that stops " |
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"response generation if the model encounters it. The sequence is not included " |
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"as part of the response. You can add up to five stop sequences." |
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)) |
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top_k_component = gr.Slider( |
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minimum=1, |
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maximum=40, |
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value=32, |
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step=1, |
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label="Top-K", |
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info=( |
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"Top-k changes how the model selects tokens for output. A top-k of 1 means the " |
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"selected token is the most probable among all tokens in the model's " |
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"vocabulary (also called greedy decoding), while a top-k of 3 means that the " |
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"next token is selected from among the 3 most probable tokens (using " |
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"temperature)." |
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)) |
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top_p_component = gr.Slider( |
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minimum=0, |
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maximum=1, |
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value=1, |
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step=0.01, |
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label="Top-P", |
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info=( |
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"Top-p changes how the model selects tokens for output. Tokens are selected " |
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"from most probable to least until the sum of their probabilities equals the " |
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"top-p value. For example, if tokens A, B, and C have a probability of .3, .2, " |
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"and .1 and the top-p value is .5, then the model will select either A or B as " |
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"the next token (using temperature). " |
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)) |
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user_inputs = [text_prompt_component, |
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chatbot_component |
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] |
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bot_inputs = [ |
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google_key_component, |
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model_selection, |
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image_prompt_component, |
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video_prompt_component, |
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temperature_component, |
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max_output_tokens_component, |
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stop_sequences_component, |
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top_k_component, |
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top_p_component, |
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text_prompt_component, |
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chatbot_component |
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] |
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with gr.Blocks() as demo: |
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with gr.Column(): |
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google_key_component.render() |
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with gr.Row(): |
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model_selection.render() |
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image_prompt_component.render() |
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video_prompt_component.render() |
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chatbot_component.render() |
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text_prompt_component.render() |
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run_button_component.render() |
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with gr.Accordion("Parameters", open=False): |
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temperature_component.render() |
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max_output_tokens_component.render() |
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stop_sequences_component.render() |
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with gr.Accordion("Advanced", open=False): |
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top_k_component.render() |
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top_p_component.render() |
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run_button_component.click( |
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fn=user, |
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inputs=user_inputs, |
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outputs=[text_prompt_component, chatbot_component], |
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queue=False |
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).then( |
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fn=bot, inputs=bot_inputs, outputs=[chatbot_component] |
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) |
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text_prompt_component.submit( |
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fn=user, |
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inputs=user_inputs, |
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outputs=[text_prompt_component, chatbot_component], |
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queue=False |
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).then( |
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fn=bot, inputs=bot_inputs, outputs=[chatbot_component] |
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) |
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gr.Examples( |
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fn=bot, |
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inputs=bot_inputs, |
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outputs=[chatbot_component], |
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examples= |
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[ |
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[ |
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"", |
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"gemini-1.5-pro-latest", |
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None, |
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None, |
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.4, |
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1024, |
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"", |
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32, |
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1, |
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"How far is the moon from the earth?", |
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[("", "")] |
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], |
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[ |
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"", |
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"gemini-1.5-pro-latest", |
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None, |
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None, |
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.4, |
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1024, |
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"", |
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32, |
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1, |
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"What is 2+2?", |
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[("", "")] |
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], |
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[ |
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"", |
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"gemini-1.5-pro-latest", |
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"./example1.webp", |
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None, |
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.4, |
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1024, |
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"", |
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32, |
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1, |
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"If there is an image of golf simulator screen, list all the stat values displayed and summarize faults and provide recommendations in the form of low effort fixes for optimizing the golf swing. If there is a video of golf swing, summarize faults and provide recommendations in the form of low effort fixes for optimizing the golf swing.", |
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[("", "")] |
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], |
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[ |
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"", |
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"gemini-1.5-pro-latest", |
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"./example2.jpg", |
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None, |
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.4, |
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1024, |
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"", |
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32, |
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1, |
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"If there is an image of golf simulator screen, list all the stat values displayed and summarize faults and provide recommendations in the form of low effort fixes for optimizing the golf swing. If there is a video of golf swing, summarize faults and provide recommendations in the form of low effort fixes for optimizing the golf swing.", |
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[("", "")] |
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], |
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[ |
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"", |
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"gemini-1.5-pro-latest", |
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None, |
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["./example1.mp4"], |
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.4, |
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1024, |
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"", |
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32, |
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1, |
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"If there is an image of golf simulator screen, list all the stat values displayed and summarize faults and provide recommendations in the form of low effort fixes for optimizing the golf swing. If there is a video of golf swing, summarize faults and provide recommendations in the form of low effort fixes for optimizing the golf swing.", |
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[("", "")] |
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], |
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[ |
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"", |
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"gemini-1.5-pro-latest", |
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None, |
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["./example2.mp4"], |
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.4, |
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1024, |
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"", |
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32, |
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1, |
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"If there is an image of golf simulator screen, list all the stat values displayed and summarize faults and provide recommendations in the form of low effort fixes for optimizing the golf swing. If there is a video of golf swing, summarize faults and provide recommendations in the form of low effort fixes for optimizing the golf swing.", |
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[("", "")] |
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], |
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[ |
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"", |
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"gemini-1.5-pro-latest", |
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None, |
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["./example3.mp4"], |
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.4, |
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1024, |
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"", |
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32, |
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1, |
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"Transcribe", |
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[("", "")] |
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] |
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], |
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) |
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demo.queue(max_size=99).launch(debug=False, show_error=True) |