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best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9dd2707a610464e2b7c338bee1bf31cc68c76d429b52894a679078776b5c2380
+size 3211515

main.py

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+# Partha Pratim Ray
+
+import os
+import cv2
+import numpy as np
+from ultralytics import YOLO
+from PIL import Image
+import gradio as gr
+import pandas as pd
+
+# Paths
+model_path = "best.pt"  # Ensure the best.pt is in the local directory or provide full path
+
+if not os.path.exists(model_path):
+    raise FileNotFoundError(f"Model file not found at {model_path}.")
+
+# Load the YOLO model
+model = YOLO(model_path)
+
+##################################
+# Hardcoded metrics for classification
+overall_top1_accuracy = 0.9142  # Replace with your Top-1 accuracy
+overall_top5_accuracy = 0.9926  # Replace with your Top-5 accuracy
+
+# Metrics DataFrame
+metrics_data = [
+    ["Overall Top-1 Accuracy", f"{overall_top1_accuracy * 100:.2f}%"],
+    ["Overall Top-5 Accuracy", f"{overall_top5_accuracy * 100:.2f}%"]
+]
+metrics_df = pd.DataFrame(metrics_data, columns=["Metric", "Value"])
+##################################
+
+def run_inference(img: np.ndarray, model):
+    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    results = model.predict(img_rgb)  # Run prediction
+    result_probs = results[0].probs
+
+    # Get top-1 and top-5 predictions
+    top1_class = result_probs.top1
+    top5_classes = result_probs.top5
+    top1_conf = result_probs.top1conf.item()
+    top5_conf = result_probs.top5conf
+
+    # Generate annotated image
+    annotated_img = results[0].plot()
+
+    # Format results
+    top1_result = f"Class: {model.names[top1_class]}, Confidence: {top1_conf:.2f}"
+    top5_results = [
+        f"{model.names[c]}: {conf:.2f}" for c, conf in zip(top5_classes, top5_conf)
+    ]
+
+    return annotated_img, top1_result, top5_results
+
+def process_image(image):
+    img = np.array(image)
+    img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+
+    # Run classification inference
+    annotated_img, top1_result, top5_results = run_inference(img_bgr, model)
+
+    # Convert annotated image back to PIL format
+    annotated_img_pil = Image.fromarray(cv2.cvtColor(annotated_img, cv2.COLOR_BGR2RGB))
+
+    # Return the annotated image, Top-1, and Top-5 predictions, along with metrics
+    return annotated_img_pil, f"Top-1: {top1_result}", "\n".join(top5_results), metrics_df
+
+with gr.Blocks() as demo:
+    gr.Markdown("# YOLO Dog ImageWoof Classification Web App")
+    gr.Markdown("Upload an image, and the model will classify it and show precomputed validation metrics.")
+
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(type="pil", label="Upload Image")
+            submit_btn = gr.Button("Run Inference")
+        with gr.Column():
+            annotated_image = gr.Image(type="pil", label="Annotated Image")  # Updated to show annotated image
+            top1_output = gr.Textbox(label="Top-1 Prediction")
+            top5_output = gr.Textbox(label="Top-5 Predictions")
+            metrics_table = gr.DataFrame(value=metrics_df, label="Validation Metrics")
+
+    submit_btn.click(
+        fn=process_image,
+        inputs=input_image,
+        outputs=[annotated_image, top1_output, top5_output, metrics_table]
+    )
+
+demo.launch()
+

requirements.txt

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+numpy
+opencv-python
+Pillow
+gradio
+pandas
+ultralytics