Update app.py (#4)

- Update app.py (a0c2042d1364d5e70ff04f7705390c24cce629b4)

app.py

@@ -1,7 +1,7 @@
 import numpy as np
 import gradio as gr
 import tensorflow as tf  # version 2.13.0
-from keras.models import load_model  # version matching your TensorFlow
+from keras.models import load_model
 import cv2
 import json
 
@@ -32,28 +32,36 @@ def analyse(img, plant_type):
     y_pred = dnn_model.predict(process_img)
     y_pred = y_pred[0]
 
-    # Identify predictions
-    p_id = plant_label_disease[plant_type.lower()][0]
-    for disease in plant_label_disease[plant_type.lower()]:
-        if y_pred[disease] > y_pred[p_id]:
-            p_id = disease
+    # Identify plant-specific predictions
+    plant_label_ids = plant_label_disease[plant_type.lower()]
+    plant_predicted_id = plant_label_ids[0]
+    for disease in plant_label_ids:
+        if y_pred[disease] > y_pred[plant_predicted_id]:
+            plant_predicted_id = disease
 
-    overall_predicted_id = np.argmax(y_pred)
+    # Determine overall prediction
+    overall_predicted_id = int(np.argmax(y_pred))
     overall_predicted_name = label_disease[str(overall_predicted_id)]
-    overall_predicted_acc = y_pred[overall_predicted_id]
+    overall_predicted_confidence = float(y_pred[overall_predicted_id])
 
-    plant_predicted_id = p_id
+    # Determine plant-specific prediction
     plant_predicted_name = label_disease[str(plant_predicted_id)]
-    plant_predicted_acc = y_pred[plant_predicted_id]
+    plant_predicted_confidence = float(y_pred[plant_predicted_id])
+
+    # Determine health status
+    is_plant_specific_healthy = "healthy" in plant_predicted_name.lower()
+    is_overall_healthy = "healthy" in overall_predicted_name.lower()
 
     # Return results as a JSON object
     result = {
-        "plant_predicted_id": int(plant_predicted_id),
-        "plant_predicted_name": plant_predicted_name,
-        "plant_predicted_accuracy": float(plant_predicted_acc),
-        "overall_predicted_id": int(overall_predicted_id),
-        "overall_predicted_name": overall_predicted_name,
-        "overall_predicted_accuracy": float(overall_predicted_acc),
+        "plant_specific_prediction_id": plant_predicted_id,
+        "plant_specific_prediction_name": plant_predicted_name,
+        "plant_specific_confidence": plant_predicted_confidence,
+        "is_plant_specific_healthy": is_plant_specific_healthy,
+        "overall_prediction_id": overall_predicted_id,
+        "overall_prediction_name": overall_predicted_name,
+        "overall_confidence": overall_predicted_confidence,
+        "is_overall_healthy": is_overall_healthy
     }
 
     return result