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| import easyocr | |
| import requests | |
| import io | |
| from PIL import Image | |
| from typing import List, Dict, Any | |
| import os | |
| import numpy as np | |
| from gradio_client import Client | |
| def process_image_file(image_url: str) -> List[Dict[str, Any]]: | |
| # Fetch the image content from the URL | |
| response = requests.get(image_url) | |
| # Check if the request was successful | |
| if response.status_code == 200: | |
| # Load the image from the response content using PIL | |
| image_stream = io.BytesIO(response.content) | |
| image = Image.open(image_stream) | |
| # Convert the image to a NumPy array, which is supported by EasyOCR | |
| image_np = np.array(image) | |
| # Use EasyOCR to extract text from the image | |
| reader = easyocr.Reader(['en']) | |
| result = reader.readtext(image_np) | |
| print("*" * 50 + image_url) | |
| # Combine the extracted text from EasyOCR | |
| extracted_text = "\n".join([detection[1] for detection in result]) | |
| if len(extracted_text.split())<5 : | |
| # Use the BLIP model for image captioning | |
| client = Client("HARISH20205/blip-image-caption") | |
| caption_result = client.predict(image_url=image_url, api_name="/predict") | |
| content = "\nImage Caption:\n" + str(caption_result) | |
| return [{ | |
| "file_name": os.path.basename(image_url), | |
| "text": content, | |
| }] | |
| # Format the content | |
| content = "Image Data:\n" + extracted_text | |
| return [{ | |
| "file_name": os.path.basename(image_url), | |
| "text": content, | |
| }] | |
| else: | |
| return [{ | |
| "file_name": os.path.basename(image_url), | |
| "text": "Failed to retrieve image.", | |
| }] |