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-1.5-pro")

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)