added requirements.txt

app.py

@@ -1,7 +1,33 @@
+import transformers
 import gradio as gr
+import datasets
+import torch
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+from transformers import ViTFeatureExtractor, ViTForImageClassification
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+dataset = load_dataset('beans', 'full_size')
+
+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
+
+
+description = "Bean leaf health classification wit Google's ViT"
+title = "Bean Leaf Health Check"
+examples = [["'angular_leaf_spot': 0.9999030828475952, 'bean_rust': 5.320278796716593e-05, 'healthy': 4.378804806037806e-05"]]
+
+
+gr_interface = gr.Interface(classify, inputs='image', outputs='label', title='Bean Classification', description='Monitor your crops health in easier way')
+
+
+gr_interface.launch(debug=True)

requirements.txt

@@ -0,0 +1,5 @@
+datasets
+transformers
+evaluate
+gradio
+torch