import streamlit as st
import os
import google.generativeai as genai
from dotenv import load_dotenv
load_dotenv() #Load all env variables
from PIL import Image

genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

# Backend Function to load Gemini Pro Vision and get response
def get_gemini_repsonse(input, image, prompt):
    model = genai.GenerativeModel('gemini-pro-vision')
    response = model.generate_content([input, image[0], prompt])
    return response.text


def input_image_setup(uploaded_file):
    # Check if a file has been uploaded
    if uploaded_file is not None:
        # Read the file into bytes
        bytes_data = uploaded_file.getvalue()

        image_parts = [
            {
                "mime_type": uploaded_file.type,  # Get the mime type of the uploaded file
                "data": bytes_data
            }
        ]
        return image_parts
    else:
        raise FileNotFoundError("No file uploaded")


##initialize our streamlit app

st.set_page_config(page_title="Cal Advisor App")

st.header("Cal Advisor App")
input = st.text_input("Input Prompt: ", key="input")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
image = ""
if uploaded_file is not None:
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Image.", use_column_width=True)

submit = st.button("Tell me the total calories")

input_prompt = """
You are an expert in nutritionist where you need to see the food items from the image
               and calculate the total calories, also provide the details of every food items with calories intake
               is below format
               1. Item 1 - no of calories
               2. Item 2 - no of calories
               ----
               ----
"""

# If submit button is clicked
if submit:
    image_data = input_image_setup(uploaded_file)
    response = get_gemini_repsonse(input_prompt, image_data, input)
    st.subheader("The Response is")
    st.write(response)