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import gradio as gr | |
import requests | |
import cv2 | |
import numpy as np | |
import os | |
API_URL = "https://detect.roboflow.com" | |
API_KEY = os.getenv("API_KEY") | |
MODEL_ID = "biofarma-x-mit-hacking-medicine-hackathon/1" | |
def annotate_image(image): | |
# Convert the input image to a format suitable for OpenCV | |
image = np.array(image) | |
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) | |
# Save the input image | |
cv2.imwrite("input_image.jpg", image) | |
# Prepare the request | |
url = f"{API_URL}/{MODEL_ID}?api_key={API_KEY}" | |
with open("input_image.jpg", "rb") as file: | |
response = requests.post(url, files={"file": file}) | |
result = response.json() | |
# Annotate the image | |
for prediction in result['predictions']: | |
x, y, width, height = prediction['x'], prediction['y'], prediction['width'], prediction['height'] | |
left = int(x - width / 2) | |
top = int(y - height / 2) | |
right = int(x + width / 2) | |
bottom = int(y + height / 2) | |
cv2.rectangle(image, (left, top), (right, bottom), (0, 0, 255), 2) | |
cv2.putText(image, f"{prediction['confidence']:.2f}", (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) | |
# Get the number of detections | |
detection_count = len(result['predictions']) | |
# Convert the image back to RGB for display in Gradio | |
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
return image, detection_count | |
# Create the Gradio interface | |
iface = gr.Interface( | |
fn=annotate_image, | |
inputs=gr.Image(type="pil"), | |
outputs=[gr.Image(label='Hasil'), gr.Text(label='Total bakteri terdeteksi')], | |
title="TB-Bacillus Detection", | |
description="Upload an image to get annotated results from the model." | |
) | |
# Launch the app | |
iface.launch() |