Create app.py

app.py

@@ -0,0 +1,60 @@
+import os
+import pandas as pd
+import torch
+from PIL import Image
+from ultralytics import YOLO
+import gradio as gr
+
+class YOLODetect():
+    def __init__(self, modelo):
+        self.modelo = modelo
+
+    def predecir(self, source, imgsz=1280, conf=0.7, iou=0.50):
+        self.results = self.modelo.predict(source=source, save=True, imgsz=imgsz, conf=conf, iou=iou)
+        return self.results
+
+    def render(self):
+        result = self.results[0]
+        file_name = os.path.join(result.save_dir, result.path)
+        render = Image.open(file_name)
+        return render
+
+# Inicializa el modelo YOLOv8
+path_best_model = 'yolov8n.pt'
+modelo_yolo = YOLO(path_best_model)
+
+def detect_objects(im, size, iou, conf):
+    '''Wrapper para Gradio'''
+    g = (int(size) / max(im.size))  # gain
+    im = im.resize(tuple([int(x * g) for x in im.size]), Image.LANCZOS) # resize with antialiasing
+
+    model = YOLODetect(modelo_yolo)
+    results = model.predecir(source=im, imgsz=int(size), conf=conf, iou=iou)
+
+    objects_detected = results[0].boxes.cls.tolist()  # Clases detectadas.
+    objects_conf = results[0].boxes.conf.tolist()  # Probabilidad de detección por clase detectada.
+
+    objects_nested_list = pd.DataFrame({'Clase': objects_detected, 'Probabilidad': objects_conf})
+
+    result_img = model.render()
+    return result_img, objects_nested_list
+
+def save_feedback(size, iou, conf, 
+                object_count_detected,
+                objects_list,
+                user_text, feedback_text, check_status):
+    try:
+        # Aquí puede ir el código para almacenar los datos en una base de datos.
+        return "Se guardó el feedback exitosamente."
+    except Exception as err:
+        print(err)
+        return "Error al guardar el feedback."
+
+# Configura la interfaz de Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("# YOLOv8 Detección de objetos")
+
+    with gr.Row():
+        iou_threshold = gr.Slider(label="NMS IoU Threshold (0.0 - 1.0)", minimum=0.0, maximum=1.0, value=0.8)
+        conf_threshold = gr.Slider(label="Umbral o threshold (0.0 - 1.0)", minimum=0.0, maximum=1.0, value=0.9)
+        size = gr.Dropdown(label="Tamaño de la