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from PIL import Image
import google.generativeai as genai # type: ignore
import streamlit as st
from dotenv import load_dotenv
import os
load_dotenv()
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
# load gemini pro vision
model = genai.GenerativeModel("gemini-1.5-flash")
def input_image_details(upload_file):
if upload_file is not None:
# read the file into bytes
bytes_data= upload_file.getvalue()
image_parts = [
{
"mime_type":upload_file.type,
"data":bytes_data
}
]
return image_parts
else:
raise FileNotFoundError("No file uploaded")
def get_gemni_response(prompt, image,user_input):
response = model.generate_content([prompt, image[0], user_input])
return response.text
#initilize our streamlite app
st.set_page_config(page_title="Multilanguage Invoice Extractor ")
st.header("MultiLanguage Invoice Extractor")
user_input = st.text_input("Input Prompt", key="input")
upload_file = st.file_uploader("choose an image of the invoice..", type=["jpg","jpeg","png"])
# to show the uploaded image
if upload_file is not None:
image = Image.open(upload_file)
st.image(image, caption="upload Image.", use_column_width=True)
submit = st.button("Tell me about the Invice")
prompt = """
Your are an expert in understanding invoice. We will upload a image as invoice
and you will have to answer any questions based on the upload invoice image
"""
# if submit buttion is clicked
if submit:
image_data = input_image_details(upload_file)
response = get_gemni_response(prompt, image_data, user_input)
st.subheader("The response is")
st.write(response) |