from dotenv import load_dotenv
import streamlit as st
import os
from PIL import Image
import google.generativeai as genai

load_dotenv()

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

# Function to load Gemini Pro Vision
model = genai.GenerativeModel('gemini-pro-vision')

def get_gemini_response(input,image, prompt):
    response = model.generate_content([input, image[0], prompt])
    return response.text

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

        image_parts = [
            {
                "mime_type": uploaded_file.type,
                'data' : bytes_data
            }
        ]
        return image_parts
    else:
        raise FileNotFoundError("No file Uploaded")
    

st.set_page_config(page_title='Multi Langauge Invoice Extractor')
st.header("Multi Langauge Invoice Extractor")
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 about the image")

input_prompt="""
    You are an expert in understanding invoices. We will upload a image as invoice
    and you will have to answer any questions based on the uploaded invoice image
"""

if submit:
    image_data = input_image_setup(uploaded_file)
    response = get_gemini_response(input_prompt, image_data, input)
    st.subheader("The Response is")
    st.write(response)