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import streamlit as st
from dotenv import load_dotenv
load_dotenv() ## load all env vriables from .env
import os
from PIL import Image
import google.generativeai as genai
genai.configure(api_key = os.getenv('GOOGLE_API_KEY'))
# create a 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
## create a function to return images bayets data
def input_image_details(uploaded_file):
if uploaded_file is not None :
# read the file into a 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')
## initialize our streamlit app
st.set_page_config(page_title = 'MultiLanguage Invoice Extractor')
st.header("MultiLanguage Invoice Extractor using Gemini Vision PRO ")
input = st.text_input('Input Prompt: ',key = 'input')
uploaded_file = st.file_uploader('Choose an image of the Invoice',type = ["jpg",'jpeg','png'])
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 a expert in understaning invoices. we will upload an image as invoice
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_details(uploaded_file)
response = get_gemini_response(input_prompt,image_data,input)
st.subheader("The Response is ")
st.write(response)
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