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##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="your own 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)
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