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# importing libraries
import streamlit as st
import google.generativeai as genai
from dotenv import load_dotenv
from PIL import Image
import os
load_dotenv() # load all the environment variables from .env
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
# Function to load Gemini Pro Vision
# In Gemini Pro, model takes it in a list
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_details(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")
# streamlit setup
st.set_page_config(layout="wide", page_title="Multilanguage 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 image")
input_prompt = """
You are an expert in understanding invoices.
You will receive input images as invoices &
you will have to answer questions based on the input 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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