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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) |