Update app.py

app.py

@@ -1,12 +1,11 @@
 import streamlit as st
 from PIL import Image
-import torch
-import cv2
 import numpy as np
+import yolov5
 
 @st.cache_resource(show_spinner=False)
 def load_model():
-    model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5n-license-plate.pt', force_reload=True)
+    model = yolov5.load('keremberke/yolov5n-license-plate')
     model.conf = 0.25
     model.iou = 0.45
     return model
@@ -20,23 +19,26 @@ def main():
         st.info("Por favor sube una imagen.")
         return
 
-    model = load_model()
     image = Image.open(img_file).convert("RGB")
+    st.image(image, caption="Imagen original", use_column_width=True)
+
+    model = load_model()
+
     img = np.array(image)
 
     results = model(img, size=640)
-    results.render()
 
-    annotated = results.ims[0]
-    st.image(annotated, caption="Resultado de detección", use_column_width=True)
+    results.render()  
+    st.image(results.ims[0], caption="Resultado con detecciones", use_column_width=True)
 
+   
     detections = results.pred[0]
     if detections is None or len(detections) == 0:
-        st.warning("No se detectaron placas.")
+        st.warning("No se detectaron matrículas.")
     else:
-        st.success(f"Se detectaron {len(detections)} objeto(s) con placa.")
+        st.success(f"Se detectaron {len(detections)} matrícula(s):")
         for *box, conf, cls in detections.cpu().numpy():
-            st.write(f"– Confianza: {conf:.2f}")
+            st.write(f"📍 Confianza: {conf:.2f}")
 
 if __name__ == "__main__":
     main()