Update app.py

app.py

@@ -5,6 +5,15 @@ from PIL import Image
 # Load YOLOv5 model
 model = YOLO("best.pt")  # make sure best.pt is in the same directory
 
+# You can define a simple disease severity mapping like this:
+disease_severity = {
+    "Late_blight": "High",
+    "Early_blight": "Medium",
+    "Powdery_mildew": "Low",
+    "Healthy": "None",
+    # Add your disease names and severity levels here accordingly
+}
+
 # Prediction function
 def predict(image):
     results = model(image)
@@ -17,18 +26,13 @@ def predict(image):
     else:
         for box in boxes:
             class_id = int(box.cls[0])
-            full_label = names[class_id]
+            disease_name = names[class_id]
             confidence = float(box.conf[0])
+            
+            severity = disease_severity.get(disease_name, "Unknown")
 
-            # Split label into plant and disease
-            if "___" in full_label:
-                plant_name, disease_name = full_label.split("___")
-            else:
-                plant_name = "Unknown"
-                disease_name = full_label
-
-            output += f"🌿 Plant: {plant_name}\n"
             output += f"⚠️ Disease: {disease_name}\n"
+            output += f"❗ Seriousness Level: {severity}\n"
             output += f"🔍 Confidence: {confidence * 100:.2f}%\n\n"
 
     return output.strip()
@@ -38,5 +42,5 @@ gr.Interface(
     inputs=gr.Image(type="pil"),
     outputs="text",
     title="🌿 AgroScan: Plant Disease Detector",
-    description="Upload a plant leaf image to detect disease and identify the plant.",
+    description="Upload a plant leaf image to detect disease and get disease seriousness and confidence.",
 ).launch()