create app.py

app.py

@@ -0,0 +1,29 @@
+import datasets
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+import gradio
+
+dataset = datasets.load_dataset('beans')
+
+extractor = AutoFeatureExtractor.from_pretrained("saved_model_files")
+model = AutoModelForImageClassification.from_pretrained("saved_model_files")
+
+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(
+                      fn= classify,
+                      inputs= "image",
+                      outputs= "label",
+                      title = 'Leaf Classication',
+                      description = "Many farming businesses are turning machine learning to monitor their crops automatically with great accuracy. This application can be used to detect Angular Leaf Spot and Bean Rust!" , 
+                      #examples = [["/content/4702250d-2b9b-4dc6-b991-552d03ecedc4.png"] , ["/content/collard-greens-contain-many-nutrients.jpg"]]
+                      )
+
+interface.launch()