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import gradio as gr
from ocr_processor import extract_text_from_image
from llm_processor import load_llm_model, generate_json_from_text
# Load LLM model on startup
load_llm_model()
def process_invoice(file_bytes):
if file_bytes is None:
return "No file uploaded", {"error": "No file uploaded"}
# Step 1: Extract text
raw_text = extract_text_from_image(file_bytes)
if not raw_text or "No text detected" in raw_text:
return raw_text, {"error": "No text could be extracted from the image."}
# Step 2: JSON generation
json_data = generate_json_from_text(raw_text)
return raw_text, json_data
# Custom CSS with Tailwind CDN
custom_css = """
<link href="https://cdn.jsdelivr.net/npm/tailwindcss@2.2.19/dist/tailwind.min.css" rel="stylesheet">
body {
font-family: 'Inter', sans-serif;
background: linear-gradient(135deg, #f3f4f6 0%, #e5e7eb 100%);
}
.gradio-container {
max-width: 1200px;
margin: 0 auto;
padding: 2rem;
}
h1 {
font-size: 2.5rem;
font-weight: 700;
color: #1f2937;
text-align: center;
margin-bottom: 1rem;
}
h3 {
font-size: 1.25rem;
color: #4b5563;
text-align: center;
margin-bottom: 2rem;
}
.gr-button {
background-color: #2563eb;
color: white;
padding: 0.75rem 1.5rem;
border-radius: 0.5rem;
font-weight: 600;
transition: background-color 0.3s ease;
}
.gr-button:hover {
background-color: #1e40af;
}
.gr-textbox, .gr-json {
border-radius: 0.5rem;
border: 1px solid #d1d5db;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
.gr-file-upload {
border: 2px dashed #d1d5db;
border-radius: 0.5rem;
padding: 1rem;
background-color: white;
}
"""
# Gradio UI
with gr.Blocks(css=custom_css) as demo:
gr.Markdown(
"""
# 🧾 Invoice Processing App
Upload an invoice image to extract **OCR text** and generate **structured JSON** output.
""",
elem_classes="text-center"
)
with gr.Row(variant="panel"):
with gr.Column(scale=1):
input_file = gr.File(
label="Upload Invoice Image",
type="binary",
file_types=[".png", ".jpg", ".jpeg"],
elem_classes="gr-file-upload"
)
process_btn = gr.Button(
"Process Invoice",
variant="primary",
elem_classes="gr-button mt-4 w-full"
)
with gr.Row(variant="panel"):
with gr.Column(scale=1):
raw_text_output = gr.Textbox(
label="Extracted OCR Text",
lines=10,
placeholder="Extracted text will appear here...",
elem_classes="gr-textbox"
)
with gr.Column(scale=1):
json_output = gr.JSON(
label="Structured JSON",
elem_classes="gr-json"
)
process_btn.click(
process_invoice,
inputs=input_file,
outputs=[raw_text_output, json_output]
)
demo.launch() |