fix app.py for inference logic

app.py

@@ -3,24 +3,17 @@ from PIL import Image
 import numpy as np
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 
-processor_pipe = AutoImageProcessor.from_pretrained("jtas/fish_classification")
-model_pipe = AutoModelForImageClassification.from_pretrained("jtas/fish_classification")
+processor = AutoImageProcessor.from_pretrained("jtas/fish_classification")
+classifier = AutoModelForImageClassification.from_pretrained("jtas/fish_classification")
 
-def classify_pipe(image):
-    inputs = processor_pipe(images=image, return_tensors="pt")
+def fish_classification(image):
+    inputs = processor(images=image, return_tensors="pt")
 
-    outputs = model_pipe(**inputs)
-    logits = outputs.logits
+    outputs = classifier(**inputs)
 
-    # 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 outputs
 
-    # Return the predicted label and score
-    return {"label": predicted_label, "score": pipe_score}
+label_fish = gr.components.Json()
 
-# Create a Gradio interface
-iface = gr.Interface(fn=classify_pipe, inputs=gr.Image(), outputs="json", title="Fish Classification")
+iface = gr.Interface(fn=fish_classification, inputs=gr.Image(), outputs=label_fish, title="Fish Classification")
 iface.launch()