update app.py

app.py

@@ -1,3 +1,26 @@
 import gradio as gr
+from PIL import Image
+import numpy as np
+from transformers import AutoImageProcessor, AutoModelForImageClassification
 
-gr.load("models/jtas/fish_classification").launch()
+processor_pipe = AutoImageProcessor.from_pretrained("jtas/fish_classification")
+model_pipe = AutoModelForImageClassification.from_pretrained("jtas/fish_classification")
+
+def classify_pipe(image):
+    inputs = processor_pipe(images=image, return_tensors="pt")
+
+    outputs = model_pipe(**inputs)
+    logits = outputs.logits
+
+    # Get the predicted label
+    predicted_class_idx = logits.argmax(-1).item()
+    labels = model_pipe.config.id2label
+    pipe_score = np.argmax(logits, dim=1).max().item()
+    predicted_label = labels[predicted_class_idx]
+
+    # Return the predicted label and score
+    return {"label": predicted_label, "score": pipe_score}
+
+# Create a Gradio interface
+iface = gr.Interface(fn=classify_pipe, inputs=gr.Image(), outputs="json", title="Fish Classification")
+iface.launch()