Delete app.py

app.py

@@ -1,105 +0,0 @@
-from huggingface_hub import from_pretrained_fastai
-import gradio as gr
-
-from fastai.vision.all import *
-
-import torchvision.transforms as transforms
-import torchvision.transforms as transforms
-
-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 *
-from pathlib import Path
-
-import random
-import PIL
-
-#Definimos las funciones de transformacion que hemos creado en la practica para poder tratar los datos de entrada y que funcione bien
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-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)
-
-class TargetMaskConvertTransform(ItemTransform):
-    def __init__(self):
-        pass
-    def encodes(self, x):
-        img,mask = x
-
-        #Convertimos a array
-        mask = np.array(mask)
-
-        mask[(mask!=255) & (mask!=150) & (mask!=76) & (mask!=74) & (mask!=29) & (mask!=25)]=0
-        mask[mask==255]=1
-        mask[mask==150]=2
-        mask[mask==76]=4
-        mask[mask==74]=4
-        mask[mask==29]=3
-        mask[mask==25]=3
-
-        # Back to PILMask
-        mask = PILMask.create(mask)
-        return img, mask
-
-from albumentations import (
-    Compose,
-    OneOf,
-    ElasticTransform,
-    GridDistortion,
-    OpticalDistortion,
-    HorizontalFlip,
-    Rotate,
-    Transpose,
-    CLAHE,
-    ShiftScaleRotate
-)
-
-def get_y_fn (x):
-    return Path(str(x).replace("Images","Labels").replace("color","gt").replace(".jpg",".png"))
-
-class SegmentationAlbumentationsTransform(ItemTransform):
-    split_idx = 0
-
-    def __init__(self, aug):
-        self.aug = aug
-
-    def encodes(self, x):
-        img,mask = x
-        aug = self.aug(image=np.array(img), mask=np.array(mask))
-        return PILImage.create(aug["image"]), PILMask.create(aug["mask"])
-
-#Cargamos el modelo
-repo_id = "luisvarona/Practica3"
-learn = from_pretrained_fastai(repo_id)
-model = learn.model
-model = model.cpu()
-
-
-# Funcion de predicción
-def predict(img_ruta):
-    img = PIL.Image.fromarray(img_ruta)
-    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]=29
-    mask[mask==4]=74
-    mask = np.reshape(mask,(480,640))
-    return Image.fromarray(mask.astype('uint8'))
-
-    
-# Creamos la interfaz y la lanzamos. 
-gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(480, 640)), outputs=gr.inputs.Image(shape=(480, 640)), examples=['color_184.jpg','color_189.jpg']).launch(share=False)