app.py

from dotenv import load_dotenv

load_dotenv()

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


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

##initialize our streamlit app
st.set_page_config(page_title="MultiLanguage Invoice Extracter")

st.header("Gemini Invoice Extracter")
input = st.text_input("Input Prompt: ",key="input")
uploaded_file = st.file_uploader("Choose an image of the invoice...",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. We will upload a image as invoices and you will have to answer any questions based on the uploaded invoice image.
"""  

## If submit 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)