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import gradio as gr
from PIL import Image
import google.generativeai as genai
import os
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# Configure the API key for Google Gemini
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
# Function to process the image and get response from Gemini model
def get_gemini_response(uploaded_file_path, query):
try:
# Define input prompt
input_prompt = """
You are an expert in understanding invoices. You will receive input images as invoices and
you will have to answer questions based on the input image.
"""
# Validate the image file path
if not uploaded_file_path or not os.path.exists(uploaded_file_path):
return "Please upload a valid image."
# Read the image file as binary data
with open(uploaded_file_path, "rb") as f:
image_data = f.read()
# Prepare the image parts for the model
mime_type = f"image/{uploaded_file_path.split('.')[-1]}" # Dynamically detect mime type
image_parts = [{"mime_type": mime_type, "data": image_data}]
# Load the Gemini model and get the response
model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content([input_prompt, image_parts[0], query])
return response.text
except Exception as e:
return f"Error: {e}"
# Define Gradio interface
with gr.Blocks() as invoice_extractor:
gr.Markdown("# Invoice Extractor")
gr.Markdown(
"""
Upload an invoice image and ask specific questions about it.
The system uses Google's Gemini model to extract and interpret the invoice details.
"""
)
image_input = gr.Image(label="Upload Invoice Image", type="filepath") # Use type="filepath"
query_input = gr.Textbox(label="Enter your query about the invoice", placeholder="e.g., What is the total amount?")
output_response = gr.Textbox(label="Response", lines=5)
# Button to process the image and query
submit_btn = gr.Button("Process Invoice")
# Set the button to call the processing function
submit_btn.click(
get_gemini_response,
inputs=[image_input, query_input],
outputs=output_response
)
# Launch the app
invoice_extractor.launch()
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