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from dotenv import load_dotenv
load_dotenv() ## load all the environment variables from .env
import streamlit as st
import os
from PIL import Image
import google.generativeai as genai
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
## 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
def input_image_details(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, # 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")
input=st.text_input("Input Prompt: ",key="input")
uploaded_file=st.file_uploader("Choose an image of the invoice...", 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. 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) |