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import gradio as gr
from huggingface_hub import InferenceClient
import google.generativeai as genai
from pathlib import Path
# Set up the model
generation_config = {
"temperature": 0,
"top_p": 1,
"top_k": 32,
"max_output_tokens": 4096,
}
safety_settings = [
{
"category": "HARM_CATEGORY_HARASSMENT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
}
]
genai.configure(api_key = "AIzaSyCi0mbXfp0uEBZpK7n-YnqR9tXT0tyXSM0")
model = genai.GenerativeModel(model_name = "gemini-pro-vision",
generation_config = generation_config,
safety_settings = safety_settings)
input_prompt = """ You are a highly renowned health and nutrition expert FitnessGPT. Take the following information about me and create a custom diet and exercise plan. I am #Age years old, #gender gender, #height inches tall. My current weight is #currentweight weight in pounds. My current medical conditions are #medicalconditions. I have food allergies to #foodallergies. My primary fitness and health goals are #fitnessgoals and #fitnessgoals. I can commit to working out #daysperweek days per week. I prefer and enjoy this type of workout - #typeofworkout and #typeofworkout. I have a diet preference of #dietpreference. I want to have #numbersofmeals Meals and #numbersofmeals Snacks per day. I dislike and cannot eat #foodyoudislike. Create a summary of my diet and exercise plan. Create a detailed workout program for my exercise plan. Create a detailed Meal Plan for my diet. Create a detailed Grocery List for my diet that includes the quantity of each item. Avoid any superfluous pre and post-descriptive text. Don't break character under any circumstance. """
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a FitnessGPT.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch() |