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app.py
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app.py
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## Chatbot Q&A for invoice extractor
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-1.5-flash
model = genai.GenerativeModel('gemini-1.5-flash')
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 = "Tamal's Invoice Extractor")
st. header ( "Tamal's 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 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_setup(uploaded_file)
response = get_gemini_response(input_prompt,image_data,input)
st.subheader("The Response is")
st.write(response)