Add application file

app.py

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+import torch
+import gradio as gr
+from transformers import AutoModelForImageClassification, AutoFeatureExtractor
+
+model_id = f"chanelcolgate/vit-base-patch16-224-finetunned-flower"
+labels = ["daisy", "dandelion", "roses", "sunflowers", "tulips"]
+
+
+def classify_image(image):
+    model = AutoModelForImageClassification.from_pretrained(model_id)
+    feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+    inp = feature_extractor(image, return_tensors="pt")
+    outp = model(**inp)
+    pred = torch.nn.functional.softmax(outp.logits, dim=-1)
+    preds = pred[0].cpu().detach().numpy()
+    confidence = {label: float(preds[i]) for i, label in enumerate(labels)}
+    return confidence
+
+
+interface = gr.Interface(
+    fn=classify_image,
+    inputs="image",
+    examples=["flower-1.jpeg", "flower-2.jpeg"],
+    outputs="label",
+).launch()

requirements.txt

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+torch
+transformers