Delete main.py

main.py

@@ -1,89 +0,0 @@
-# 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()
-