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()