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from dotenv import load_dotenv
load_dotenv()
import google.generativeai as genai
import streamlit as st
import os
from PIL import Image
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
model=genai.GenerativeModel('gemini-pro-vision')
def get_res(input,image,prompt):
res=model.generate_content([input,image[0],prompt])
return res.text
def input_image_setup(uploaded_file):
if uploaded_file is not None:
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
st.set_page_config("Multi-Language Invoice Extractor")
st.header("Multi-Language Invoice Extractor")
input=st.text_input("Input: ", key="input")
file = st.file_uploader("Choose an Image of the Invoice", type=["jpg","jpeg","png"])
image=""
if file is not None:
image=Image.open(file)
st.image(image, caption="Uploaded Image: ", use_column_width=True)
submit=st.button("Tell me")
input_prompt="""
ou are an expert in invoice analysis. I will upload an invoice image, and you need to answer any questions I ask based on the details in the image.
"""
if submit:
image_data= input_image_setup(file)
res = get_res(input_prompt,image_data,input)
st.subheader("Response: ")
st.write(res) |