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import os
import requests
import json
import dotenv
from PIL import Image
import io
import base64
import numpy as np
from gradio_client import Client
# Load environment variables from .env file
dotenv.load_dotenv()
# Get Hugging Face token from environment
hf_token = os.getenv("HF_TOKEN")
# Create client for the Hugging Face Space with authentication
client = Client("wjbmattingly/carcal-api", hf_token=hf_token)
# Example usage
if __name__ == "__main__":
# Path to input image
image_path = "test.jpg"
# Check if image exists
if not os.path.exists(image_path):
print(f"Error: Image file not found: {image_path}")
exit(1)
# Print available API endpoints (for reference)
print("Available API endpoints:")
for endpoint in client.endpoints:
print(f" - {endpoint}")
print(f"\nProcessing image: {image_path}")
# Convert the image to base64
with open(image_path, "rb") as image_file:
image_bytes = image_file.read()
# Convert to base64 string
base64_image = base64.b64encode(image_bytes).decode("utf-8")
# Format as expected by Gradio's ImageData model
image_data = {
"data": f"data:image/jpeg;base64,{base64_image}",
"is_file": False
}
# Call the run_example function with the appropriate parameters
result = client.predict(
image_data, # Base64 encoded image
"Qwen/Qwen2.5-VL-7B-Instruct", # Model selection
False, # NER disabled
"person, organization, location, date, event", # Default NER labels
api_name="/run_example" # API endpoint name
)
# Process and display the result
if result:
print("\nExtracted Text:")
if isinstance(result, list):
full_text = ""
for segment in result:
if isinstance(segment, list) and len(segment) > 0:
text = segment[0]
full_text += text
print(text, end="")
print("\n")
else:
print(result)
else:
print("No result returned from API")
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