Create app.py

app.py

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+import gradio as gr
+from gradio_huggingfacehub_search import HuggingfaceHubSearch
+from transformers import pipeline
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🐇 Transformers Pipeline Playground")
+    gr.Markdown(
+        "Search for a model on the Hub en explore its output performance on CPU. Some interesting categories are [Text Classification](https://huggingface.co/models?pipeline_tag=image-classification&sort=trending), [Token Classification](https://huggingface.co/models?pipeline_tag=token-classification&sort=trending), [Question Answering](https://huggingface.co/models?pipeline_tag=question-answering&sort=trending) or [Image Classification](https://huggingface.co/models?pipeline_tag=image-classification&sort=trending)."
+    )
+    search_in = HuggingfaceHubSearch(
+        label="Hub Search",
+        placeholder="Search for a model",
+        search_type="model",
+        sumbit_on_select=True,
+    )
+
+    @gr.render(inputs=[search_in], triggers=[search_in.submit])
+    def get_interface_from_repo(repo_id: str, progress: gr.Progress = gr.Progress()):
+        progress(0.0, desc="Loading model")
+        pipe = pipeline(model=repo_id)
+        progress(1.0, desc="Model loaded")
+        gr.Interface.from_pipeline(pipe)
+
+
+if __name__ == "__main__":
+    demo.launch()