Create app.py

app.py

@@ -0,0 +1,50 @@
+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)
+    
+    # Convert the image back to RGB for display in Gradio
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    return image
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=annotate_image,
+    inputs=gr.Image(type="pil"),
+    outputs="image",
+    title="TB-Bacillus Detection",
+    description="Upload an image to get annotated results from the model."
+)
+
+# Launch the app
+iface.launch()