Update app.py

app.py

@@ -7,12 +7,13 @@ 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") 
+API_KEY = os.environ.get("ULTRALYTICS_API_KEY", "a6684f3c4a050ec709948f66db7a391633a2064fe7") 
 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.
+    Takes an image, sends it to the Ultralytics API, and returns the
+    classification name and confidence score.
     """
     # Convert the Gradio image (numpy array) to a file-like object
     image_pil = Image.fromarray(image)
@@ -34,11 +35,25 @@ def classify_image(image):
         # Check for a successful response
         response.raise_for_status()
 
-        # Return the JSON response from the API
-        return response.json()
+        # Parse the JSON response
+        result_data = response.json()
+        
+        # Extract the relevant information
+        # The structure is data -> images -> [0] -> results -> [0]
+        if result_data.get("images") and result_data["images"][0].get("results"):
+            top_result = result_data["images"][0]["results"][0]
+            name = top_result.get("name")
+            confidence = top_result.get("confidence")
+            
+            # Return the extracted values for the two output components
+            return name, f"{confidence:.2f}"
+        else:
+            return "No classification found", "N/A"
 
     except requests.exceptions.RequestException as e:
-        return f"API Request Error: {e}"
+        return f"API Error: {e}", ""
+    except (KeyError, IndexError):
+        return "Could not parse API response.", ""
     finally:
         # Clean up the temporary image file
         if os.path.exists(image_path):
@@ -48,7 +63,11 @@ def classify_image(image):
 
 # 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")
+
+# Define separate outputs for name and confidence
+name_output = gr.Label(label="Classification")
+confidence_output = gr.Textbox(label="Confidence Score")
+
 
 # List of example images
 example_images = [
@@ -63,9 +82,9 @@ example_images = [
 iface = gr.Interface(
     fn=classify_image,
     inputs=image_input,
-    outputs=json_output,
+    outputs=[name_output, confidence_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.",
+    description="Upload a picture or use your camera to classify an image using a pre-trained model. The model's prediction and confidence score will be displayed.",
     examples=example_images
 )