File size: 9,842 Bytes
4e1e279 e45c1e6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 |
import os
import time
from typing import List, Tuple, Optional
from pathlib import Path
import google.generativeai as genai
import gradio as gr
from PIL import Image
GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
TITLE = """<h1 align="center">🏋️ Online Personal Trainer 💪</h1>"""
SUBTITLE = """<h3 align="center">Upload your workout video and let the AI analyze your form 🖇️</h3>"""
Prompt = """
You are the world's best fitness expert. Your goal is to analyze in detail how people perform their exercises and sports movements. Watch the provided video carefully and give them constructive feedback in at least 10 sentences. Focus on the following aspects:
Form and Technique: Identify any issues with the form and technique of the exercises being performed. Provide specific suggestions for improvement.
Repetitions and Sets: Count the number of repetitions and sets for each exercise. Ensure they match the intended workout plan.
Pacing and Timing: Evaluate the pacing and timing of the exercises. Suggest any adjustments needed to optimize performance.
Overall Performance: Give an overall assessment of the workout, highlighting strengths and areas for improvement.
Remember to be encouraging and supportive in your feedback. Your goal is to help them improve and stay motivated. Thank you!
"""
#If the image does not show an exercise, respond with: 'What are you doing? This is no time for games! Upload a real exercise video.'
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 user(text_prompt: str, chatbot: List[Tuple[str, str]]):
return "", chatbot + [[text_prompt, None]]
def bot(
google_key: str,
model_name: str,
video_prompt,
temperature: float,
max_output_tokens: int,
stop_sequences: str,
top_k: int,
top_p: float,
text_prompt_component: str,
chatbot: List[Tuple[str, str]]
):
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.")
user_input = chatbot[-1][0]
combined_prompt = Prompt + "\n" + user_input
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 video_prompt is not None:
model = genai.GenerativeModel(model_name)
video_file = genai.upload_file(path=video_prompt)
while video_file.state.name == "PROCESSING":
print('.', end='')
time.sleep(10)
video_file = genai.get_file(video_file.name)
if video_file.state.name == "FAILED":
raise ValueError(video_file.state.name)
response = model.generate_content(
contents=[video_file, combined_prompt],
stream=True,
generation_config=generation_config,
request_options={"timeout": 600})
response.resolve()
else:
model = genai.GenerativeModel(model_name)
response = model.generate_content(
combined_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
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
)
video_prompt_component = gr.Video(label="Video", autoplay=True)
model_selection = gr.Dropdown(["gemini-1.5-flash-latest", "gemini-1.5-pro-latest"], label="Select Gemini Model", value="gemini-1.5-pro-latest")
chatbot_component = gr.Chatbot(
label='Gemini',
bubble_full_width=False,
scale=3, height=500
)
text_prompt_component = gr.Textbox(
placeholder="Hi there!",
label="Ask me anything and press Enter"
)
run_button_component = gr.Button()
temperature_component = gr.Slider(
minimum=0,
maximum=1.0,
value=0.6,
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 = [
google_key_component,
model_selection,
video_prompt_component,
temperature_component,
max_output_tokens_component,
stop_sequences_component,
top_k_component,
top_p_component,
text_prompt_component,
chatbot_component
]
with gr.Blocks() as demo:
gr.HTML(TITLE)
gr.HTML(SUBTITLE)
with gr.Column():
google_key_component.render()
with gr.Row():
video_prompt_component.render()
chatbot_component.render()
text_prompt_component.render()
run_button_component.render()
with gr.Accordion("Parameters", open=False):
model_selection.render()
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]
)
gr.Examples(
fn=bot,
inputs=bot_inputs,
outputs=[chatbot_component],
examples=
[
[
"",
"gemini-1.5-pro-latest",
"./example1.mp4",
.7,
1024,
"",
32,
1,
"Give me some tips to improve my deadlift.",
[("", "")]
],
[
"",
"gemini-1.5-pro-latest",
"./example2.mp4",
.7,
1024,
"",
32,
1,
"How is my form?",
[("", "")]
],
[
"",
"gemini-1.5-pro-latest",
"./example3.mp4",
.7,
1024,
"",
32,
1,
"What improvements can I make?",
[("", "")]
],
[
"",
"gemini-1.5-pro-latest",
"./example4.mp4",
.7,
1024,
"",
32,
1,
"I just started working out. I'm not sure I'm doing it right. Can you check?",
[("", "")]
]
],
#cache_examples="lazy",
)
demo.queue(max_size=99).launch(debug=False, show_error=True,show_api=False) |