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app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+import torch.utils.data
+from torch import Tensor
+from torch.utils.data import DataLoader, Dataset, WeightedRandomSampler
+import torchvision
+from torchvision import datasets, transforms, models
+
+MODEL_NAME = 'ResNeXt-101-64x4d'
+model = torch.load(MODEL_NAME+'.ptm')
+
+labels = ['Apple__black_rot', 'Apple__healthy', 'Apple__rust', 'Apple__scab', 'Cassava__bacterial_blight', 'Cassava__brown_streak_disease', 'Cassava__green_mottle', 'Cassava__healthy', 'Cassava__mosaic_disease', 'Cherry__healthy', 'Cherry__powdery_mildew', 'Chili__healthy', 'Chili__leaf curl', 'Chili__leaf spot', 'Chili__whitefly', 'Chili__yellowish', 'Coffee__cercospora_leaf_spot', 'Coffee__healthy', 'Coffee__red_spider_mite', 'Coffee__rust', 'Corn__common_rust', 'Corn__gray_leaf_spot', 'Corn__healthy', 'Corn__northern_leaf_blight', 'Cucumber__diseased', 'Cucumber__healthy', 'Gauva__diseased', 'Gauva__healthy', 'Grape__black_measles', 'Grape__black_rot', 'Grape__healthy', 'Grape__leaf_blight_(isariopsis_leaf_spot)', 'Jamun__diseased', 'Jamun__healthy', 'Lemon__diseased', 'Lemon__healthy', 'Mango__diseased', 'Mango__healthy', 'Peach__bacterial_spot', 'Peach__healthy', 'Pepper_bell__bacterial_spot', 'Pepper_bell__healthy', 'Pomegranate__diseased', 'Pomegranate__healthy', 'Potato__early_blight', 'Potato__healthy', 'Potato__late_blight', 'Rice__brown_spot', 'Rice__healthy', 'Rice__hispa', 'Rice__leaf_blast', 'Rice__neck_blast', 'Soybean__bacterial_blight', 'Soybean__caterpillar', 'Soybean__diabrotica_speciosa', 'Soybean__downy_mildew', 'Soybean__healthy', 'Soybean__mosaic_virus', 'Soybean__powdery_mildew', 'Soybean__rust', 'Soybean__southern_blight', 'Strawberry___leaf_scorch', 'Strawberry__healthy', 'Sugarcane__bacterial_blight', 'Sugarcane__healthy', 'Sugarcane__red_rot', 'Sugarcane__red_stripe', 'Sugarcane__rust', 'Tea__algal_leaf', 'Tea__anthracnose', 'Tea__bird_eye_spot', 'Tea__brown_blight', 'Tea__healthy', 'Tea__red_leaf_spot', 'Tomato__bacterial_spot', 'Tomato__early_blight', 'Tomato__healthy', 'Tomato__late_blight', 'Tomato__leaf_mold', 'Tomato__mosaic_virus', 'Tomato__septoria_leaf_spot', 'Tomato__spider_mites_(two_spotted_spider_mite)', 'Tomato__target_spot', 'Tomato__yellow_leaf_curl_virus', 'Wheat__brown_rust', 'Wheat__healthy', 'Wheat__septoria', 'Wheat__yellow_rust']
+
+def predict(img):
+    img = predictTransform(img).unsqueeze(0).to(DEVICE)
+    with torch.inference_mode():
+        prediction = torch.nn.functional.softmax(model(img)[0], dim=0)
+        confidences = {labels[i]: float(prediction[i]) for i in range(len(labels))}
+    return confidences
+
+title = "Plant Disease Classifier"
+description = "Please upload a photo containing a plant leaf."
+iface = gr.Interface(predict, 
+                     inputs=gr.Image(shape=(224, 224)), 
+                     outputs=gr.Label(num_top_classes=7), 
+                     live=True, 
+                     title=title,
+                     description=description,
+                     interpretation='default').launch(share=True)
+					 

requirements.txt

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+torch
+torchvision