Create app.py

app.py

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+from ultralytics import YOLO
+import gradio as gr
+import numpy as np
+import cv2
+
+# Load the trained model
+model = YOLO('/content/drive/MyDrive/MS-Thesis/Multi-Class Classification/runs/classify/train4/weights/best.pt')  # Replace with the path to your trained model
+
+# Prediction function
+def predict_image(image):
+    try:
+        # Convert the input image to the format expected by the model
+        image = np.array(image)
+        image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+        # Make prediction
+        results = model.predict(image_bgr)
+        
+        # Get the predicted class and confidence using the correct attributes
+        predicted_class = results[0].names[results[0].probs.top1]
+        confidence = results[0].probs.top1conf
+        
+        # Annotate image with predicted class and confidence
+        annotated_image = image.copy()
+        cv2.putText(annotated_image, f"{predicted_class}: {confidence:.2f}",
+                    (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
+        
+        # Convert the annotated image back to RGB for display
+        annotated_image_rgb = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
+        
+        return annotated_image_rgb, f"Predicted: {predicted_class} with {confidence:.2f} confidence"
+    
+    except Exception as e:
+        # Return an error message if something goes wrong
+        return None, f"Error: {str(e)}"
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(label="Upload an Image"),
+    outputs=[gr.Image(label="Annotated Image"), gr.Text(label="Prediction")],
+    title="Fruit Freshness Classifier",
+    description="Upload an image of a fruit, and the model will predict whether it is Fresh, Mild, or Rotten, and display the result on the image."
+)
+
+# Launch the interface
+interface.launch()