Update app.py

app.py

@@ -0,0 +1,57 @@
+import gradio as gr
+import torch
+from transformers import pipeline
+from timeit import default_timer as timer
+
+username = "fmagot01"  ## Complete your username
+model_id = f"{username}/vit-base-beans"
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+pipe = pipeline("image-classification", model=model_id, device=device)
+
+# def predict_trunc(filepath):
+#     preprocessed = pipe.preprocess(filepath)
+#     truncated = pipe.feature_extractor.pad(preprocessed,truncation=True, max_length = 16_000*30)
+#     model_outputs = pipe.forward(truncated)
+#     outputs = pipe.postprocess(model_outputs)
+
+#     return outputs
+
+
+def classify_image(filepath):
+    """
+      Goes from
+      [{'score': 0.8339303731918335, 'label': 'healthy'},
+    {'score': 0.11914275586605072, 'label': 'bean_rust'},]
+     to
+     {"health":  0.8339303731918335, "bean_rust":0.11914275586605072}
+    """
+    start_time = timer()
+    preds = pipe(filepath)
+
+    outputs = {}
+    pred_time = round(timer() - start_time, 5)
+    for p in preds:
+        outputs[p["label"]] = p["score"]
+    return outputs, pred_time
+
+
+title = "Classifier of Leaf Images"
+description = """
+This demo shows the application of the fintuned image classification model using [Beans](https://huggingface.co/datasets/beans). You can upload your own image or select an image from the examples below.
+
+It will output 3 different labels: Healthy, Bean Rust and Angular leaf Spot. Bean rust is a type of disease that leaves can get. Angular leaf spot refers to irregular spots that a leaf can get (not a disease) and healthy leaves do not have any of these.
+"""
+
+filenames = ['leaftest1.jpeg', "leaftest2.jpeg", "leaftest3.jpeg"]
+filenames = [[f"./{f}"] for f in filenames]
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="filepath"),
+    outputs=[gr.outputs.Label(label="Predictions"),
+             gr.Number(label="Prediction time (s)")
+            ],
+    title=title,
+    description=description,
+    examples=filenames,
+)
+demo.launch()