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"""
Gradio app for document OCR processing with Mistral OCR.
Features:
- File upload to storage API
- Document processing using Mistral OCR
- Display of OCR results
"""
import os
import requests
import gradio as gr
import asyncio
import json
import tempfile
from typing import Dict, Any, Optional
from pathlib import Path
# Mistral AI
from mistralai import Mistral
# API Configuration
STORAGE_API_URL = "https://storage-bucket-api.vercel.app/upload"
MISTRAL_API_KEY = "5oHGQTYDGD3ecQZSqdLsr5ZL4nOsfGYj" # In production, use environment variables
# Initialize Mistral client
client = Mistral(api_key=MISTRAL_API_KEY)
class MistralOCRProcessor:
"""Handles document OCR processing using Mistral AI"""
def __init__(self, client: Mistral = None):
self.client = client or Mistral(api_key=MISTRAL_API_KEY)
async def process_document(self, document_path: str) -> Dict[str, Any]:
"""
Process a document using Mistral OCR
Args:
document_path: Local path to the document to process
Returns:
Dict containing OCR results or error information
"""
try:
# For local files, we need to upload to a temporary URL first
upload_result = await StorageManager().upload_file(document_path)
if not upload_result.get("success"):
return {
"success": False,
"result": None,
"error": f"Upload failed: {upload_result.get('error')}"
}
document_url = upload_result.get("storage_url")
if not document_url:
return {
"success": False,
"result": None,
"error": "No storage URL returned from upload"
}
# Process with Mistral OCR
ocr_response = self.client.ocr.process(
model="mistral-ocr-latest",
document={
"type": "document_url",
"document_url": document_url
},
include_image_base64=True
)
# Convert response to dict if it's a Pydantic model
if hasattr(ocr_response, 'model_dump'):
result = ocr_response.model_dump()
else:
result = ocr_response
return {
"success": True,
"result": result,
"document_url": document_url,
"error": None
}
except Exception as e:
return {
"success": False,
"result": None,
"error": f"OCR processing error: {str(e)}"
}
class StorageManager:
"""Handles file uploads to the storage service"""
def __init__(self, api_url: str = STORAGE_API_URL):
self.api_url = api_url
async def upload_file(self, file_path: str) -> Dict[str, Any]:
"""
Upload a file to the storage service
Args:
file_path: Path to the file to upload
Returns:
Dict containing upload result or error information
"""
try:
with open(file_path, 'rb') as f:
files = {'file': (os.path.basename(file_path), f)}
response = requests.post(self.api_url, files=files)
response.raise_for_status()
result = response.json()
if not result.get('success'):
raise Exception(result.get('message', 'Upload failed'))
return {
"success": True,
"storage_url": result.get('storage_url'),
"original_filename": result.get('original_filename'),
"file_size": result.get('file_size'),
"error": None
}
except Exception as e:
return {
"success": False,
"storage_url": None,
"original_filename": os.path.basename(file_path),
"file_size": os.path.getsize(file_path) if os.path.exists(file_path) else 0,
"error": f"Upload failed: {str(e)}"
}
# Initialize processors
ocr_processor = MistralOCRProcessor()
storage_manager = StorageManager()
async def process_document_ocr(file_path: str) -> Dict[str, Any]:
"""
Process a document through the complete OCR pipeline
Args:
file_path: Path to the document file
Returns:
Dict containing processing results
"""
# Process with Mistral OCR (handles upload internally)
result = await ocr_processor.process_document(file_path)
if not result.get("success"):
return {
"success": False,
"upload": {"success": False},
"ocr": None,
"error": result.get("error", "Unknown error")
}
# Get the original filename from the file path
original_filename = Path(file_path).name
file_size = os.path.getsize(file_path)
return {
"success": True,
"upload": {
"success": True,
"storage_url": result.get("document_url"),
"original_filename": original_filename,
"file_size": file_size
},
"ocr": result.get("result"),
"error": None,
"storage_url": result.get("document_url")
}
# Gradio Interface
def create_gradio_interface():
"""Create and return the Gradio interface"""
with gr.Blocks(title="Document OCR Processor", theme=gr.themes.Soft()) as demo:
gr.Markdown("# Document OCR Processor")
gr.Markdown("Upload a document (PDF, JPG, JPEG, PNG) to process with Mistral OCR")
with gr.Row():
with gr.Column(scale=2):
file_input = gr.File(label="Upload Document", type="filepath")
process_btn = gr.Button("Process Document", variant="primary")
with gr.Accordion("Debug Info", open=False):
status_text = gr.Textbox(label="Status", interactive=False)
with gr.Column(scale=3):
with gr.Tabs():
with gr.TabItem("OCR Results"):
ocr_output = gr.JSON(label="OCR Output")
with gr.TabItem("Extracted Text"):
text_output = gr.Textbox(label="Extracted Text", lines=20, max_lines=50)
with gr.TabItem("Upload Info"):
upload_info = gr.JSON(label="Upload Information")
def update_status(message):
return message
async def process_file(file_path):
try:
status = "Starting document processing..."
yield {status_text: update_status(status)}
# Process the document
result = await process_document_ocr(file_path)
if not result["success"]:
error_msg = result.get('error', 'Unknown error')
yield {
status_text: update_status(f"❌ {error_msg}"),
ocr_output: None,
text_output: "",
upload_info: None
}
return
# Extract text from OCR result
extracted_text = ""
ocr_data = result.get("ocr", {})
# Handle different OCR result formats
if isinstance(ocr_data, dict):
if "text" in ocr_data:
extracted_text = ocr_data["text"]
elif "pages" in ocr_data and isinstance(ocr_data["pages"], list):
extracted_text = "\n\n".join(
page.get("text", "")
for page in ocr_data["pages"]
if page and isinstance(page, dict) and "text" in page
)
# Prepare upload info
upload_info_data = {
"original_filename": result["upload"].get("original_filename"),
"file_size": result["upload"].get("file_size"),
"storage_url": result["upload"].get("storage_url"),
}
yield {
status_text: update_status("✅ Document processed successfully"),
ocr_output: ocr_data,
text_output: extracted_text,
upload_info: upload_info_data
}
except Exception as e:
import traceback
error_trace = traceback.format_exc()
error_msg = f"Unexpected error: {str(e)}"
yield {
status_text: update_status(f"❌ {error_msg}"),
ocr_output: None,
text_output: "",
upload_info: None
}
# Connect the process button to the processing function
process_btn.click(
fn=process_file,
inputs=file_input,
outputs=[status_text, ocr_output, text_output, upload_info]
)
# Auto-process when a file is uploaded
file_input.change(
fn=lambda x: "Ready to process. Click 'Process Document' to continue.",
inputs=file_input,
outputs=status_text
)
return demo.launch(server_name="0.0.0.0", server_port=7860)
if __name__ == "__main__":
# Create and launch the interface
create_gradio_interface()
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