Create app.py

app.py

@@ -0,0 +1,73 @@
+import gradio as gr
+import json
+import requests
+from PIL import Image
+import os
+
+# --- Constants for the API ---
+API_URL = "https://predict.ultralytics.com"
+# It's recommended to use Hugging Face secrets for API keys
+API_KEY = os.environ.get("ULTRALYTICS_API_KEY") 
+MODEL_ID = "https://hub.ultralytics.com/models/RsLHnWMhiBPqy3iFZAgr"
+
+def classify_image(image):
+    """
+    Takes an image, sends it to the Ultralytics API, and returns the classification.
+    """
+    # Convert the Gradio image (numpy array) to a file-like object
+    image_pil = Image.fromarray(image)
+    image_path = "temp_image.jpg"
+    image_pil.save(image_path)
+
+    headers = {"x-api-key": API_KEY}
+    data = {
+        "model": MODEL_ID,
+        "imgsz": 640,
+        "conf": 0.25,
+        "iou": 0.45
+    }
+
+    try:
+        with open(image_path, "rb") as f:
+            response = requests.post(API_URL, headers=headers, data=data, files={"file": f})
+        
+        # Check for a successful response
+        response.raise_for_status()
+
+        # Return the JSON response from the API
+        return response.json()
+
+    except requests.exceptions.RequestException as e:
+        return f"API Request Error: {e}"
+    finally:
+        # Clean up the temporary image file
+        if os.path.exists(image_path):
+            os.remove(image_path)
+
+# --- Gradio Interface ---
+
+# Define the input and output components
+image_input = gr.Image(type="numpy", label="Upload an Image or Use Webcam")
+json_output = gr.JSON(label="Classification Results")
+
+# List of example images
+example_images = [
+    ["images/img1.jpg"],
+    ["images/img2.jpg"],
+    ["images/img3.jpg"],
+    ["images/img4.jpg"],
+    ["images/img5.jpg"],
+]
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=image_input,
+    outputs=json_output,
+    title="Image Classification with Ultralytics API",
+    description="Upload a picture or use your camera to classify an image using a pre-trained model. The results from the API will be displayed in JSON format.",
+    examples=example_images
+)
+
+# Launch the application
+iface.launch()