app.py

###        Invoice extractor

##Importing All the modules
import streamlit as st
import os
from PIL import Image
import google.generativeai as genai
from dotenv import load_dotenv


# Load all environment Variables
load_dotenv()

##Configuring the api key
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

## Function to load Gemini Vison Pro Vision Model and Get response

def get_gemini_response(input,image,prompt):

    ##Loading the desired Model
    model= genai.GenerativeModel("gemini-pro-vision")
    response=model.generate_content([input,image[0],prompt])
    return response.text

## Function to extract data from Image Uploaded
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")
    
#       Initializing our Streamlit Prompt

st.set_page_config(page_title="Gemini Image Demo")

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 image")

##  DEFINING A SYSTEM PROMPT

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

if submit:
    image_data = input_image_setup(uploaded_file)
    response = get_gemini_response(input_prompt,image_data,input)

    st.subheader("The Response Generated by your model Gemini Pro Vision is: ")
    st.write(response)

    st.balloons()