Update app.py

app.py

@@ -1,14 +1,73 @@
+import os
+import torch
 import gradio as gr
-from gradio import inputs
 
-titulo = "Clasificador de imagenes"
-descripcion = "Este es un Demo que hace un reconocimiento de imagenes y clasifica según lo que el modelo de reconocimiento de imagenes de huggingface/google/vit-base-patch16-224 encuentre."
+from PIL import Image
+from torchvision import transforms
 
-modelo = "huggingface/google/vit-base-patch16-224"
-entrada = gr.inputs.Image(label="Carga una imagen aquí")
 
-# Assuming you're directly loading a model, which might not work as explained earlier.
-# You may need to adjust this part according to the actual way to load and use models with Gradio.
-iface = gr.Interface.load(modelo, inputs=entrada, title=titulo, description=descripcion)
+"""
+Built following:
+https://huggingface.co/spaces/pytorch/ResNet/tree/main
+https://www.gradio.app/image_classification_in_pytorch/
+"""
 
-iface.launch()
+# Get classes list
+os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")
+
+# Load PyTorch model
+model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)
+model.eval()
+
+# Download an example image from the pytorch website
+torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
+
+# Inference!
+def inference(input_image):
+    preprocess = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    input_tensor = preprocess(input_image)
+    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+
+    # Move the input and model to GPU for speed if available
+    if torch.cuda.is_available():
+        input_batch = input_batch.to('cuda')
+        model.to('cuda')
+
+    with torch.no_grad():
+        output = model(input_batch)
+    # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
+    probabilities = torch.nn.functional.softmax(output[0], dim=0)
+
+    # Read the categories
+    with open("imagenet_classes.txt", "r") as f:
+        categories = [s.strip() for s in f.readlines()]
+    # Show top categories per image
+    top5_prob, top5_catid = torch.topk(probabilities, 5)
+    result = {}
+    for i in range(top5_prob.size(0)):
+        result[categories[top5_catid[i]]] = top5_prob[i].item()
+    return result
+
+# Define ins outs placeholders
+inputs = gr.inputs.Image(type='pil')
+outputs = gr.outputs.Label(type="confidences",num_top_classes=5)
+
+# Define style
+title = "Image Recognition Demo"
+description = "This is a prototype application which demonstrates how artifical intelligence based systems can recognize what object(s) is present in an image. This fundamental task in computer vision known as `Image Classification` has applications stretching from autonomous vehicles to medical imaging. To use it, simply upload your image, or click one of the examples images to load them, which I took at <a href='https://espacepourlavie.ca/en/biodome' target='_blank'>Montréal Biodôme</a>! Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1512.03385' target='_blank'>Deep Residual Learning for Image Recognition</a> | <a href='https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py' target='_blank'>Github Repo</a></p>"
+
+# Run inference
+gr.Interface(inference, 
+            inputs, 
+            outputs, 
+            examples=["example1.jpg", "example2.jpg"], 
+            title=title, 
+            description=description, 
+            article=article,
+            analytics_enabled=False).launch()