Update app.py

app.py

@@ -1,7 +1,25 @@
+import datasets
 import gradio as gr
+import torch
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
 
-example=[["https://s3.amazonaws.com/moonup/production/uploads/1664123118229-62e3c8b3e107fc739ac56137.jpeg"]]
+dataset = datasets.load_dataset('beans')
 
-interface = gr.Interface.load(classify, inputs='image',examples=example,outputs='label',title='Bean leaf Classification')
+extractor = AutoFeatureExtractor.from_pretrained("s")
+
+model = AutoModelForImageClassification.from_pretrained("Blaise-MR/diseaseClassifier")
+
+labels = dataset['train'].features['labels'].names
+
+def classify(im):
+  features = 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
+
+
+interface = gr.Interface(classify, inputs='image', outputs='label', title='Bean plant disease classifier',description='Detect diseases in beans using images of leaves')
 
 interface.launch()