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from ultralytics import YOLO
from huggingface_hub import hf_hub_download
import gradio as gr
import numpy as np
from PIL import Image
import pytesseract
# Load YOLO model from HuggingFace
repo_config = dict(
repo_id="arnabdhar/YOLOv8-nano-aadhar-card", # Ensure the repo is accessible
filename="model.pt",
local_dir="./models"
)
model = YOLO(hf_hub_download(**repo_config))
# Get id-to-label mapping
id2label = model.names
def predict(image):
try:
image_np = np.array(image)
detections = model.predict(image_np)[0]
results = []
for box, confidence, class_id in zip(detections.xyxy, detections.confidence, detections.class_id):
x1, y1, x2, y2 = map(int, box)
label = id2label[class_id]
# Crop the detected region
cropped_region = image_np[y1:y2, x1:x2]
# Perform OCR on the cropped region
ocr_text = pytesseract.image_to_string(cropped_region, config='--psm 6')
results.append(f"Detected {label}: {ocr_text.strip()} with confidence {confidence:.2f}")
return "\n".join(results) # Return as a single string for easier display in Gradio
except Exception as e:
return f"Error: {str(e)}" # Basic error handling
# Create Gradio interface
iface = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs="text")
iface.launch()
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