Update app.py

app.py

@@ -1,18 +1,61 @@
 from huggingface_hub import from_pretrained_fastai
 import gradio as gr
+from fastai.basics import *
+from fastai.vision import models
 from fastai.vision.all import *
+from fastai.metrics import *
+from fastai.data.all import *
+from fastai.callback import *
+import PIL
+import torchvision.transforms as transforms
 
-# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
-repo_id = "Alesteba/deep_model_03"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
+model = torch.jit.load("pract3.pth")
+model = model.cpu()
 
-learner = from_pretrained_fastai(repo_id)
-labels = learner.dls.vocab
+def transform_image(image):
+    
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    
+    my_transforms = transforms.Compose([transforms.ToTensor(),
+                                        transforms.Normalize(
+                                            [0.485, 0.456, 0.406],
+                                            [0.229, 0.224, 0.225])])
+    image_aux = image
+    
+    return my_transforms(image_aux).unsqueeze(0).to(device)
 
-# Definimos una función que se encarga de llevar a cabo las predicciones
 def predict(img):
-    #img = PILImage.create(img)
-    pred,pred_idx,probs = learner.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    img = PIL.Image.fromarray(img, "RGB")
+    image = transforms.Resize((480,640))(img)
+    tensor = transform_image(image=image) 
+    
+    model.to(device)
+    with torch.no_grad():
+        outputs = model(tensor)
+    
+    outputs = torch.argmax(outputs,1)
+    mask = np.array(outputs.cpu())
+    mask[mask==1]=255
+    mask[mask==2]=150
+    mask[mask==3]=76
+    mask[mask==4]=29
+    mask=np.reshape(mask,(480,640))
+    return Image.fromarray(mask.astype('uint8'))
+
+# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
+# repo_id = "Alesteba/deep_model_03"
+
+# learner = from_pretrained_fastai(repo_id)
+# labels = learner.dls.vocab
+
+
+
+# # Definimos una función que se encarga de llevar a cabo las predicciones
+# def predict(img):
+#     #img = PILImage.create(img)
+#     pred,pred_idx,probs = learner.predict(img)
+#     return {labels[i]: float(probs[i]) for i in range(len(labels))}
     
 # Creamos la interfaz y la lanzamos. 
 gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Label(num_top_classes=3),examples=['color_154.jpg','color_155.jpg']).launch(share=False)