Update app.py

app.py

@@ -1,118 +1,11 @@
-import os
-import sys
-
-current = os.path.dirname(os.path.realpath(__file__))
-parent = os.path.dirname(current)
-sys.path.append(parent)
-
-import albumentations as A
 import gradio as gr
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-from albumentations.pytorch import ToTensorV2
-from model import Classifier
-from PIL import Image
-
-# Load the model
-model = Classifier.load_from_checkpoint("KhadijaAsehnoune12/orange-disease-detector")
-model.eval()
-
-# Define labels
-labels = [
-    "Chancre citrique",
-    "Feuillage endommagé",
-    "Jaunissement des feuilles d'agrumes",
-    "Maladie de l'oïdium",
-    "Mouche blanche - Aleurode",
-    "Trou de balle",
-    "Verdissement des agrumes",
-    "cochenille blanche de l'oranger",
-    "déclin des agrumes",
-    "feuille saine",
-]
-
-# Preprocess function
-def preprocess(image):
-    image = np.array(image)
-    resize = A.Resize(224, 224)
-    normalize = A.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
-    to_tensor = ToTensorV2()
-    transform = A.Compose([resize, normalize, to_tensor])
-    image = transform(image=image)["image"]
-    return image
-
-
-# Define the function to make predictions on an image
-def predict(image):
-    try:
-        image = preprocess(image).unsqueeze(0)
-
-        # Prediction
-        # Make a prediction on the image
-        with torch.no_grad():
-            output = model(image)
-            # convert to probabilities
-            probabilities = torch.nn.functional.softmax(output[0])
-
-            # get top probabilities
-            topk_prob, topk_label = torch.topk(probabilities, 3)
-
-            # Return the top 3 predictions
-        return {
-            labels[label]: float(prob) for label, prob in zip(topk_label, topk_prob)
-        }
-    except Exception as e:
-        print(f"Error predicting image: {e}")
-        return []
-
-
-# Define the interface
-def app():
-    title = "Leaf Disease Image Classification"
-
-    gr.Interface(
-        title=title,
-        fn=predict,
-        inputs=gr.Image(type="pil"),
-        outputs=gr.Label(
-            num_top_classes=3,
-        ),
-        examples=[
-            "MoucheB.jpg",
-            "verdissement.jpg",
-        ],
-    ).launch()
-
-
-# Run the app
-if __name__ == "__main__":
-    app()
-
-
-# import gradio as gr
-# import requests
-# from PIL import Image
-# from torchvision import transforms
-# import torch
-
-# model = torch.hub.load('KhadijaAsehnoune12/orange-disease-detector', 'orange-disease-detector', pretrained=True ,trust_repo=True).eval()
-
-# # Download human-readable labels for ImageNet.
-# response = requests.get("https://git.io/JJkYN")
-# labels = response.text.split("\n")
-
-# def predict(inp):
-#   inp = transforms.ToTensor()(inp).unsqueeze(0)
-#   with torch.no_grad():
-#     prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
-#     confidences = {labels[i]: float(prediction[i]) for i in range(100)}
-#   return confidences
-
-# gr.Interface(fn=predict,
-#              inputs=gr.Image(type="pil"),
-#              outputs=gr.Label(num_top_classes=3),
-#              examples=["MoucheB.jpg" , "verdissement.jpg"],
-#              css=".footer{display:none !important}",
-#              title=None).launch()
 
+gr.Interface.load(
+             "KhadijaAsehnoune12/orange-disease-detector",
+             theme="default",
+             examples=[["alligator.jpg"], ["laptop.jpg"]],
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Label(num_top_classes=3),
+             examples=["MoucheB.jpg" , "verdissement.jpg"],
+             css=".footer{display:none !important}",
+             title=None).launch()