Add user interface via Gradio

app.py

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+import gradio as gr
+import base64
+import os
+import requests
+import json
+import utils
+
+from dotenv import load_dotenv, find_dotenv
+
+
+# List of ML models
+list_models = ["facebook/detr-resnet-50", "facebook/detr-resnet-101", "hustvl/yolos-tiny", "hustvl/yolos-small"]
+list_models_simple = [os.path.basename(model) for model in list_models]
+
+
+# Retrieve API URLs from env file or global settings
+def retrieve_api():
+
+    env_path = find_dotenv('config_api.env')
+    if env_path:
+        load_dotenv(dotenv_path=env_path)
+        print("config_api.env file loaded successfully.")
+    else:
+        print("config_api.env file not found.")
+
+    # Use of AWS endpoint or local container by default
+    global AWS_API
+    AWS_API = os.getenv("AWS_API", default="http://localhost:8080")
+
+
+#@spaces.GPU
+def detect(image_path, model_id, threshold):
+    print("\n Object detection...")
+    print("\t ML model:", list_models[model_id])
+
+    with open(image_path, 'rb') as image_file:
+       image_bytes = image_file.read()
+
+    # API Call for object prediction with model type as query parameter
+    if AWS_API == "http://localhost:8080":
+        API_endpoint = AWS_API + "/2015-03-31/functions/function/invocations"
+    else:
+        API_endpoint = AWS_API + "/dev/detect"
+    print("\t API_Endpoint: ", API_endpoint)
+
+    # Encode the image data in base64
+    encoded_image = base64.b64encode(image_bytes).decode('utf-8')
+
+    # Prepare the payload
+    payload = {
+        'body': encoded_image
+    }
+
+    # Prepare the query string parameters
+    model_name = list_models_simple[model_id]
+    params = {
+        'model': model_name
+    }
+
+    response = requests.post(API_endpoint, json=payload, params=params)
+
+    if response.status_code == 200:
+        # Process the response
+        response_json = response.json()
+        print('\t API response', response_json)
+        print('\t API response - type', type(response_json))
+        prediction_dict = json.loads(response_json["body"])
+        print('\t API body prediction_dict', prediction_dict)
+        print('\t API body prediction_dict - type', type(prediction_dict))
+    else:
+        prediction_dict = {"Error": response.status_code}
+        gr.Error(f"\t API Error: {response.status_code}")
+
+    # Generate gradio output components: image and json
+    output_json, output_pil_img = utils.generate_gradio_outputs(image_path, prediction_dict, threshold)
+
+    return output_json, output_pil_img
+
+
+def demo():
+    with gr.Blocks(theme="base") as demo:
+        gr.Markdown("# Object detection task - use of AWS Lambda")
+        gr.Markdown(
+            """
+            This web application uses transformer models to detect objects on images.
+            Machine learning models were trained on the COCO dataset.
+            You can load an image and see the predictions for the objects detected.
+            
+            Note: This web application uses deployed ML models, available via AWS Lambda and AWS API Gateway.
+            """
+        )
+
+        with gr.Row():
+            with gr.Column():
+                model_id = gr.Radio(list_models, \
+                               label="Detection models", value=list_models[0], type="index", info="Choose your detection model")
+            with gr.Column():
+                threshold = gr.Slider(0, 1.0, value=0.9, label='Detection threshold', info="Choose your detection threshold")
+
+        with gr.Row():
+            input_image = gr.Image(label="Input image", type="filepath")
+            output_image = gr.Image(label="Output image", type="pil")
+            output_json = gr.JSON(label="JSON output", min_height=240, max_height=300)
+
+        with gr.Row():
+            submit_btn = gr.Button("Submit")
+            clear_button = gr.ClearButton()
+
+        gr.Examples(['samples/savanna.jpg', 'samples/boats.jpg'], inputs=input_image)
+
+        submit_btn.click(fn=detect, inputs=[input_image, model_id, threshold], outputs=[output_json, output_image])
+        clear_button.click(lambda: [None, None, None], \
+                        inputs=None, \
+                        outputs=[input_image, output_image, output_json], \
+                        queue=False)
+
+    demo.queue().launch(debug=True)
+
+if __name__ == "__main__":
+    retrieve_api()
+    demo()

utils.py

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+from PIL import Image
+import matplotlib.pyplot as plt
+import io
+
+
+# COCO classes
+CLASSES = [
+    'N/A', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A',
+    'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',
+    'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack',
+    'umbrella', 'N/A', 'N/A', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',
+    'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
+    'skateboard', 'surfboard', 'tennis racket', 'bottle', 'N/A', 'wine glass',
+    'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',
+    'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',
+    'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table', 'N/A',
+    'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard',
+    'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A',
+    'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier',
+    'toothbrush'
+]
+COLORS = [
+    [0.000, 0.447, 0.741],
+    [0.850, 0.325, 0.098],
+    [0.929, 0.694, 0.125],
+    [0.494, 0.184, 0.556],
+    [0.466, 0.674, 0.188],
+    [0.301, 0.745, 0.933],
+]
+
+
+# Update JSON dictionary with rounded values and class names
+def generate_output_json(json_dict):
+    json_dict['scores'] = [round(score, 3) for score in json_dict['scores']]
+    json_dict['boxes'] = [[round(coord, 3) for coord in box] for box in json_dict['boxes']]
+    json_dict['labels'] = [CLASSES[label] for label in json_dict['labels']]
+    return json_dict
+
+
+# Generate matplotlib figure from prediction scores and boxes
+def generate_output_figure(image_path, predictions, threshold):
+    pil_img = Image.open(image_path)
+
+    plt.figure(figsize=(16, 10))
+    plt.imshow(pil_img)
+    ax = plt.gca()
+    colors = COLORS * 100
+
+    print("\t Detailed information...")
+    for score, label, box in zip(predictions["scores"], predictions["labels"], predictions["boxes"]):
+        #box = [round(i, 2) for i in box]
+        print(
+            f"\t\t Detected {label} with confidence "
+            f"{score} at location {box}"
+        )
+
+        if score > threshold:
+            c = COLORS[hash(label) % len(COLORS)]
+            ax.add_patch(
+                plt.Rectangle((box[0], box[1]), box[2] - box[0], box[3] - box[1], fill=False, color=c, linewidth=3)
+            )
+            text = f"{label}: {score:0.2f}"
+            ax.text(box[0], box[1], text, fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
+    plt.axis("off")
+
+    return plt.gcf()
+
+
+#  Generate PIL image from matplotlib figure
+def generate_output_image(output_figure):
+    # Convert matplotlib figure to PIL image
+    #output_figure = plt.gcf()
+    buf = io.BytesIO()
+    output_figure.savefig(buf, bbox_inches="tight")
+    buf.seek(0)
+    output_pil_img = Image.open(buf)
+
+    return output_pil_img
+
+
+def generate_gradio_outputs(image_path, prediction_dict, threshold):
+    output_json = generate_output_json(prediction_dict)
+    output_figure = generate_output_figure(image_path, output_json, threshold)
+    output_pil_img = generate_output_image(output_figure)
+    return output_json, output_pil_img