app.py

##Invoice Extractor

from dotenv import load_dotenv

load_dotenv() #it will take all env variables from .env file

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

##congigure API Key

genai.configure(api_key="add your own created api key")

##function to load gemini pro vision model and get response

def get_gemini_response(input,image,prompt):

    ##loading the gemini model
    model =genai.GenerativeModel('gemini-pro-vision')
    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")


#streamlit app

st.set_page_config(page_title="Invoice Extractor")

st.header("Gemini Application")
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 invoice")

input_prompt ="""
you are an expert in understanding invoices. you will 
receive input images as invoices and you will have to
answer questions based on the input image.
"""

## If asubmit button is clicked

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)