Create app.py

app.py

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+# prompt: gradio image 分类
+import fastai
+from fastai.vision import *
+from fastai.vision.all import load_learner,PILImage
+import gradio as gr
+from transformers import AutoModelForSequenceClassification
+# Load the model
+
+model = AutoModelForImageClassification.from_pretrained("Falconsai/nsfw_image_detection")
+model = load_learner(model)
+
+# Define an image classification function
+def classify_image(image):
+    img = PILImage.create(image)
+
+    # Make a prediction
+    pred_class, pred_idx, probs = model.predict(img)
+    
+    # Return the prediction as a dictionary
+    return {model.dls.vocab[i]: float(probs[i]) for i in range(len(probs))}
+
+# Create the Gradio interface
+image_input = gr.Image()
+label_output = gr.Label(num_top_classes=2)
+interface = gr.Interface(fn=classify_image, inputs=image_input, outputs=label_output)
+
+# Launch the interface
+interface.launch()