LLM.py

from pathlib import Path
import textwrap
import json
import google.generativeai as genai
from IPython.display import display
from IPython.display import Markdown
import langchain

def to_markdown(text):
  text = text.replace('•', '  *')
  return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))

MODEL_CONFIG = {
  "temperature": 0.2,
  "top_p": 1,
  "top_k": 32,
  "max_output_tokens": 4096,
}

## Safety Settings of Model
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"
  }
]



"""## LOAD GEMINI MODEL WITH MODEL CONFIGURATIONS"""

model = genai.GenerativeModel(model_name = "gemini-pro-vision",
                              generation_config = MODEL_CONFIG,
                              safety_settings = safety_settings)

def image_format(image_path):
    img = Path(image_path)

    if not img.exists():
        raise FileNotFoundError(f"Could not find image: {img}")

    image_parts = [
        {
            "mime_type": "image/png", ## Mime type are PNG - image/png. JPEG - image/jpeg. WEBP - image/webp
            "data": img.read_bytes()
        }
    ]
    return image_parts

def gemini_output(image_path, system_prompt, user_prompt):

    image_info = image_format(image_path)
    input_prompt= [system_prompt, image_info[0], user_prompt]
    response = model.generate_content(input_prompt)
    return response.text


    
import os
os.getenv('AIzaSyBH44mYvw6rGCTT5bOYEmIi8RcjgrQpxLs')

genai.configure(api_key="AIzaSyBH44mYvw6rGCTT5bOYEmIi8RcjgrQpxLs")

def get_llm_response(packet_description,user_details):
    system_prompt = """
                You are a specialist in nutritional facts.
                Input images in the form of wrappers of packaged good will be provided to you,
                and your task is to respond with the text document of all the ingredients present in it give it in a list format.
                take the nutritional facts table part and return how much quantity of the specific nutrients present in that food
                """

    image_path = "lays.jpg"

    user_prompt = "Give me the information about it"


    prompt = f'''
    Please extract the following information from the given text and return it is as a JSON object and each heading is a variable(only in lower cases and white spaces are replaced with underscores)  :,and i want these json variables "ingredients", "nutritional_value", "personalized_report" and "how_does_this_product_affect_the_goal_of_the_user"(regarding the goal of the user like weightgain or weight loss or muscle gain like hoe does this product affect the goal of the user i have specified the goal of the user in goal key in the user details) and the whole value string for the key variable should be within a single line.  in json format, and value of the key should be in the format like speaking to the user itself providing the knowledge based on key variable. regarding the heading variable(key)
    note all the ingredients,explain only the complex ingredients in a very very  simple terms .Give side effects of the semi-complex and complex ingredients
    give me all the nutritional value and names . And also i want it to be with the perfect json format without comments 


    this is the body of text to extract the information from:
    {packet_description}

    Personalized to this users details. Explain how the ingredients will affect the user. Give a personalised report:
    {user_details}
    '''


    model = genai.GenerativeModel('gemini-pro')

    response = model.generate_content(prompt)

    to_markdown(response.text)
    # print(response.text)
    return response.text
    # data = json.loads(response.text)
    # print(data)
    # # data = {
    # #   'name':'user',
    # #   'age':'19'
    # # }
    # temp = json.dumps(data)
    # with open('myFile.json','w') as file:
    #   file.write(temp)