Create app.py

app.py

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+# Install required packages
+!pip install transformers
+!pip install streamlit
+!pip install torch
+!pip install datasets gradio
+
+import gradio as gr
+from transformers import pipeline
+
+# Load a pre-trained image classification pipeline from Hugging Face
+model = pipeline("image-classification", model="google/vit-base-patch16-224")
+
+# Define the prediction function
+def classify_image(image):
+    predictions = model(image)
+    return {pred["label"]: pred["score"] for pred in predictions}
+
+# Set up the Gradio interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(),
+    title="Image Classification App",
+    description="Upload an image, and the app will classify it using a Vision Transformer (ViT) model."
+)
+
+# Launch the app
+if _name_ == "_main_":
+    interface.launch()