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# Standard library imports
import os
import re
import io
# External libraries for image processing and visualization
from PIL import Image
# Azure AI services
from azure.core.credentials import AzureKeyCredential
from azure.ai.formrecognizer import DocumentAnalysisClient
# Gradio for creating interfaces
import gradio as gr
# Other libraries
import numpy as np
import openai
# Azure Cognitive Services endpoint and key
endpoint = "https://herbariumsamplerecognition.cognitiveservices.azure.com/"
key = os.environ['KEY1']
def sanitize_filename(filename):
# Remove characters that are not alphanumeric, spaces, dots, or underscores
return re.sub(r'[^\w\s\.-]', '', filename)
def extract_info(text):
# Set your OpenAI API key
openai.api_key = os.environ['KEY2']
# Prepare the prompt for the API
prompt = f"From the provided text, return only the relevant information in a JSON format according to the Darwin Core standard for biodiversity specimen. Note: make sure that each output has a 'country' field. If you do not find an explicit country, make your best guess at the country using the context of the other text.\n{text}\n{text}"
try:
# Send the request to the API
response = openai.ChatCompletion.create(
model="gpt-4-1106-preview",
messages=[{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}]
)
# Extract the response
return response.choices[0].message['content'] if response.choices else "No response from the API."
except Exception as e:
return f"An error occurred: {str(e)}"
def analyze_read(image_stream):
try:
document_analysis_client = DocumentAnalysisClient(
endpoint=endpoint, credential=AzureKeyCredential(key)
)
poller = document_analysis_client.begin_analyze_document(
"prebuilt-read", image_stream)
result = poller.result()
# Collect the content from the document
document_content = result.content
extracted_info = extract_info(document_content)
return extracted_info
except Exception as e:
return f"An error occurred: {str(e)}"
def model_function(image):
# Convert the NumPy array to a PIL Image object
image = Image.fromarray(np.uint8(image)).convert('RGB')
# Convert the uploaded image to a byte stream
image_bytes = io.BytesIO()
image.save(image_bytes, format='JPEG') # Using 'JPEG' as the format
image_bytes = image_bytes.getvalue()
output_text = analyze_read(image_bytes)
return output_text
title = "HerbariaOCR"
description = "Upload your Herbaria specimen and let the Azure-GPT pipeline work its magic!\nYou will find all the textual data from the image extracted in the DarwinCore json format standard."
article = "Check out [the GitHub repository](https://github.com/BU-Spark/HerbariaOCR) that this demo is based off of."
iface = gr.Interface(fn=model_function,title=title,
description=description,article=article, inputs="image", outputs="text")
iface.launch(share=True) |