Added error handling for model prediction

app.py

@@ -2,19 +2,53 @@ import gradio as gr
 from deepforest import main
 import cv2
 
-
 def show_trees(img_path):
-    model = main.deepforest()
-    model.use_release()
-    img = model.predict_image(path=img_path, return_plot=True)
+    try:
+        model = main.deepforest()
+        model.use_release()
+    except Exception as model_error:
+        raise gr.Error(f"Error initializing the deepforest model: {model_error}")
+
+    img=cv2.imread(img_path)
+    
+    if img is None:
+        raise gr.Error(f"Image path is not valid.")
+    
+    if img.shape[0]<1000 and img.shape[1]<1000:
+        img = model.predict_image(path=img_path, return_plot=True)
+    elif img.shape[0]>1000 or img.shape[1]>1000:
+        img=model.predict_image(path=img_path,return_plot=True,thickness=2)
+    else:
+        img = model.predict_image(path=img_path, return_plot=True,thickness=3) 
+    
+    if img is None:
+        raise gr.Error("No predictions were made. Check your test image. Ensure it conists")
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+   
     return img
 
 def show_birds(img_path):
-    model = main.deepforest()
-    model.use_bird_release()
-    img = model.predict_image(path=img_path, return_plot=True)
+    try:
+        model = main.deepforest()
+        model.use_bird_release()
+    except Exception as model_error:
+        raise gr.Error(f"Error initializing the deepforest model: {model_error}")
+    img=cv2.imread(img_path)
+    
+    if img is None:
+        raise gr.Error(f"Image path is not valid.")
+    
+    if img.shape[0]<1000 and img.shape[1]<1000:
+        img = model.predict_image(path=img_path, return_plot=True)
+    elif img.shape[0]>1000 or img.shape[1]>1000:
+        img=model.predict_image(path=img_path,return_plot=True,thickness=2)
+    else:
+        img = model.predict_image(path=img_path, return_plot=True,thickness=3) 
+    
+    if img is None:
+        raise gr.Error("No predictions were made. Check your test image. Ensure it conists")
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+   
     return img
 
 with gr.Blocks() as demo:
@@ -36,6 +70,7 @@ with gr.Blocks() as demo:
             input_image=gr.Image(label="Input image",type="filepath")
         with gr.Column():
             output_image=gr.Image(label="Predicted Image")
+            
     submit_button_birds = gr.Button("Predict birds")
     submit_button_birds.click(show_birds,inputs=input_image,outputs=output_image)