Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Deploy Barcelo demo.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1FxaL8DcYgvjPrWfWruSA5hvk3J81zLY9
+
+![   ](https://www.vicentelopez.gov.ar/assets/images/logo-mvl.png)
+
+# Modelo
+
+YOLO es una familia de modelos de detección de objetos a escala compuesta entrenados en COCO dataset, e incluye una funcionalidad simple para Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite.
+
+
+## Gradio Inferencia
+
+![](https://i.ibb.co/982NS6m/header.png)
+
+Este Notebook se acelera opcionalmente con un entorno de ejecución de GPU
+
+
+----------------------------------------------------------------------
+
+ YOLOv5 Gradio demo
+
+*Author: Ultralytics LLC and Gradio*
+
+# Código
+"""
+
+!pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt gradio # install dependencies
+
+import gradio as gr
+import torch
+from PIL import Image
+
+# Images
+torch.hub.download_url_to_file('https://i.pinimg.com/originals/7f/5e/96/7f5e9657c08aae4bcd8bc8b0dcff720e.jpg', 'ejemplo1.jpg')
+torch.hub.download_url_to_file('https://i.pinimg.com/originals/c2/ce/e0/c2cee05624d5477ffcf2d34ca77b47d1.jpg', 'ejemplo2.jpg')
+
+# Model
+#model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # force_reload=True to update
+
+model = torch.hub.load('ultralytics/yolov5', 'custom', path='/content/best.pt')  # local model  o google colab
+#model = torch.hub.load('path/to/yolov5', 'custom', path='/content/yolov56.pt', source='local')  # local repo
+
+def yolo(im, size=640):
+    g = (size / max(im.size))  # gain
+    im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize
+
+    results = model(im)  # inference
+    results.render()  # updates results.imgs with boxes and labels
+    return Image.fromarray(results.imgs[0])
+
+
+inputs = gr.inputs.Image(type='pil', label=" Imagen Original")
+outputs = gr.outputs.Image(type="pil", label="Resultado")
+
+title = 'Trampas Barceló'
+        
+description = "Sistemas de Desarrollado por Subcretaría de Innovación del Municipio de Vicente Lopez"
+              
+article = "<p style='text-align: center'>YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes " \
+          "simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, " \
+          "and export to ONNX, CoreML and TFLite. <a href='https://colab.research.google.com/drive/1fbeB71yD09WK2JG9P3Ladu9MEzQ2rQad?usp=sharing'>Source code</a> |" \
+          "<a href='https://colab.research.google.com/drive/1FxaL8DcYgvjPrWfWruSA5hvk3J81zLY9?usp=sharing'>Colab Deploy</a> | <a href='https://github.com/ultralytics/yolov5'>PyTorch Hub</a></p>"
+          
+examples = [['ejemplo1.jpg'], ['ejemplo2.jpg']]
+gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(
+    debug=True)
+
+"""For YOLOv5 PyTorch Hub inference with **PIL**, **OpenCV**, **Numpy** or **PyTorch** inputs please see the full [YOLOv5 PyTorch Hub Tutorial](https://github.com/ultralytics/yolov5/issues/36).
+
+
+## Citation
+
+[![DOI](https://zenodo.org/badge/264818686.svg)](https://zenodo.org/badge/latestdoi/264818686)
+"""