Update README.md

detailed prediction

README.md

@@ -73,6 +73,67 @@ predicted_class = list(class_names.keys())[prediction.argmax()]
 print(f"Predicted class: {predicted_class}")
 ```
 
+
+Here's a detailed output of model prediction:
+
+```python
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import json
+
+import tensorflow as tf
+from huggingface_hub import snapshot_download
+
+# Download the entire model directory
+model_dir = snapshot_download(repo_id="eligapris/maize-diseases-detection",
+    local_dir="path/to/model")
+
+# Load the model
+model = tf.saved_model.load('path/to/model')
+
+# Now you can use the model for inference
+
+# Load and preprocess the image
+img = Image.open('/path/to/image.jpg')
+img = img.resize((300, 300 * img.size[1] // img.size[0]))  
+img_array = np.array(img)[None]
+
+# Make prediction
+inp = tensorflow.constant(img_array, dtype='float32')
+prediction = model(inp)[0].numpy()
+
+# Load class names and details
+with open('model/classes_detailed.json', 'r') as f:
+    data = json.load(f)
+
+class_names = data['classes']
+class_details = data['details']
+
+# Get the predicted class
+predicted_class = list(class_names.keys())[prediction.argmax()]
+predicted_class_label = class_names[predicted_class]
+
+print(f"Predicted class: {predicted_class} (Label: {predicted_class_label})")
+
+# Print detailed information about the predicted class
+if predicted_class in class_details:
+    details = class_details[predicted_class]
+    print("\nDetailed Information:")
+    for key, value in details.items():
+        if isinstance(value, list):
+            print(f"{key.capitalize()}:")
+            for item in value:
+                print(f"  - {item}")
+        else:
+            print(f"{key.capitalize()}: {value}")
+
+# Print general notes
+print("\nGeneral Notes:")
+for note in data['general_notes']:
+    print(f"- {note}")
+```
+
 ### Training data
 
 The model was trained on a dataset derived from the PlantVillage and PlantDoc datasets, specifically curated for maize leaf diseases. The dataset consists of: