Create app.py

app.py

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+
+#app.py:
+# from huggingface_hub import from_pretrained_fastai
+import gradio as gr
+from fastcore.xtras import Path
+from fastai.callback.hook import summary
+from fastai.callback.progress import ProgressCallback
+from fastai.callback.schedule import lr_find, fit_flat_cos
+from fastai.data.block import DataBlock
+from fastai.data.external import untar_data, URLs
+from fastai.data.transforms import get_image_files, FuncSplitter, Normalize
+from fastai.layers import Mish
+from fastai.losses import BaseLoss
+from fastai.optimizer import ranger
+from fastai.torch_core import tensor
+from fastai.vision.augment import aug_transforms
+from fastai.vision.core import PILImage, PILMask
+from fastai.vision.data import ImageBlock, MaskBlock, imagenet_stats
+from fastai.vision.learner import unet_learner
+from PIL import Image
+import numpy as np
+from torch import nn
+from torchvision.models.resnet import resnet34
+import torch
+import torch.nn.functional as F
+
+
+# # repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
+repo_id = "islasher/segm-grapes"
+
+
+# # Definimos una función que se encarga de llevar a cabo las predicciones
+
+
+# # Cargar el modelo y el tokenizador
+learn = load_learner(repo_id)
+#learner = from_pretrained_fastai(repo_id)
+
+import torchvision.transforms as transforms
+def transform_image(image):
+    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):
+    image = transforms.Resize((480,640))(img)
+    tensor = transform_image(image=image)
+    with torch.no_grad():
+        outputs = model(tensor)
+    
+    outputs = torch.argmax(outputs,1)
+
+    mask = np.array(outputs.cpu())
+    mask[mask==1]=150
+    mask[mask==3]=76 #pole # y no 74
+# mask[mask==5]=74 #pole
+    mask[mask==2]=29 #wood # y no 25
+# mask[mask==6]=25 #wood
+    mask[mask==4]=255 #grape
+    mask=np.reshape(mask,(480,640)) #en modo matriz
+    return Image.fromarray(mask.astype('uint8'))
+    
+# Creamos la interfaz y la lanzamos. 
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Image(shape=(480,640)),examples=['color_154.jpg','color_155.jpg']).launch(share=False)