app, requirements, example images

app.py

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+import datasets
+import torch
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+
+dataset =  datasets.load_dataset('beans')
+
+feature_extractor = AutoFeatureExtractor.from_pretrained("saved_model_files")
+model = AutoModelForImageClassification.from_pretrained("saved_model_files")
+
+labels = dataset['train'].features['labels'].names
+
+def classify(im):
+  features = feature_extractor(im, return_tensors='pt')
+  logits = model(features["pixel_values"])[-1]
+  probability = torch.nn.functional.softmax(logits, dim=-1)
+  probs = probability[0].detach().numpy()
+  confidences = {label: float(probs[i]) for i, label in enumerate(labels)} 
+  return confidences
+
+import gradio as gr
+
+Instruction = "Submit bean-leaf images with different leaf conditions"
+title="Bean-leaf-disease Image classification demo"
+description = "Drop an Input image to classify, Observe the model prediction across 3 distinct categories."
+article = """
+            - Select an image from the examples provided as demo image
+            - Click submit button to make Image classification
+            - Click clear button to try new Image for classification
+          """
+
+interface = gr.Interface(
+    classify,
+    inputs='image',
+    outputs='label',
+    instructuction = Instruction,
+    title = title,
+    description = description,
+    article = article,
+    examples=["image1.jpg",
+              "image2.jpg",
+              "image3.jpg",
+              "image4.jpg"]
+    )
+
+interface.launch(debug=True)

requirements.txt

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+torch
+transformers