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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 pytube import YouTube |
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from moviepy.editor import * |
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import speech_recognition as sr |
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import pandas as pd |
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import numpy as np |
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import google.generativeai as genai |
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from tqdm.auto import tqdm |
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import time |
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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">Gemini Playground 😎</h1>""" |
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SUBTITLE = """<h2 align="center">Play with Gemini Pro and Gemini Pro Vision 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/SkalskiP/ChatGemini?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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safety_settings = [ |
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{ |
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"category": "HARM_CATEGORY_DANGEROUS", |
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"threshold": "BLOCK_NONE", |
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}, |
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{ |
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"category": "HARM_CATEGORY_HARASSMENT", |
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"threshold": "BLOCK_NONE", |
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}, |
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{ |
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"category": "HARM_CATEGORY_HATE_SPEECH", |
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"threshold": "BLOCK_NONE", |
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}, |
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{ |
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"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", |
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"threshold": "BLOCK_NONE", |
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}, |
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{ |
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"category": "HARM_CATEGORY_DANGEROUS_CONTENT", |
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"threshold": "BLOCK_NONE", |
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},] |
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def extract_text_from(vid_link): |
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video_url = vid_link |
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yt = YouTube(video_url) |
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text = "" |
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audio_stream = yt.streams.get_audio_only() |
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audio_stream.download(filename='tmp.mp4') |
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audio_clip = AudioFileClip('tmp.mp4') |
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audio_clip.write_audiofile('tmp.wav') |
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r = sr.Recognizer() |
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with sr.AudioFile('tmp.wav') as source: |
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audio_data = r.record(source) |
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try: |
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text = r.recognize_google(audio_data, language='ar') |
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except sr.UnknownValueError: |
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print("Google Speech Recognition could not understand audio") |
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except sr.RequestError as e: |
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print("Could not request results from Google Speech Recognition service; {0}".format(e)) |
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return text |
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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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def bot( |
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google_key: str, |
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image_prompt: Optional[Image.Image], |
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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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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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txt_in = chatbot[-1][0] |
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if "youtube" in txt_in: |
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text_prompt = extract_text_from(txt_in) |
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else: |
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text_prompt = txt_in |
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genai.configure(api_key=google_key) |
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generation_config = genai.types.GenerationConfig( |
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temperature=0.7, |
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max_output_tokens=2048, |
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stop_sequences=preprocess_stop_sequences(stop_sequences=stop_sequences), |
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top_k=40, |
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top_p=0.95) |
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if image_prompt is None: |
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prompt= "استخرج كلمات مفتاحية من النص التالي: "+text_prompt |
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model = genai.GenerativeModel('gemini-pro') |
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response = model.generate_content( |
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prompt, |
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stream=True, |
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generation_config=generation_config,safety_settings=safety_settings) |
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response.resolve() |
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out1 = response.text |
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prompt = "أذكر لي آية من القران الكريم تتحدث عن أحد هذه المواضيع او اكثر: "+ out1 + " واشرح الآيه وفسرها باللغة العربية." |
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response = model.generate_content( |
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prompt, |
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stream=True, |
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generation_config=generation_config, safety_settings=safety_settings) |
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response.resolve() |
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else: |
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image_prompt = preprocess_image(image_prompt) |
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model = genai.GenerativeModel('gemini-pro-vision') |
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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, safety_settings=safety_settings) |
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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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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 = [ |
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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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image_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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chatbot_component |
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] |
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with gr.Blocks() as demo: |
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gr.HTML(TITLE) |
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gr.HTML(SUBTITLE) |
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gr.HTML(DUPLICATE) |
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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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image_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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demo.queue(max_size=99).launch(debug=False, show_error=True) |
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