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import streamlit as st
import os
import google.generativeai as genai
from PIL import Image
from dotenv import load_dotenv
load_dotenv()
# Configuring the google api key...
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
# Function to load Gemini-pro-vision model...
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_setup(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")
##initialize our streamlit app
st.set_page_config(page_title="Extract your invoice")
st.header("Extract your invoice")
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 Invoice...!!!", use_column_width=True)
submit = st.button("Tell me about the prompt")
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 uploaded invoice input image
"""
if submit:
image_data = input_image_setup(uploaded_file)
response=get_gemini_response(input_prompt,image_data,input)
st.subheader("The Response is")
st.write(response)
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