add app code & requirements

app.py

@@ -0,0 +1,20 @@
+from datasets import load_dataset
+import gradio as gr
+import torch
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+
+# Define dataset, feature extractor, and model
+dataset = 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

requirements.txt

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