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README.md

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+---
+title: puzzle
+emoji: 👁
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 5.20.1
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import torch
+from huggingface_hub import hf_hub_download
+
+def load_model_from_hub(repo_id, filename):
+    model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+    model = torch.load(model_path, weights_only=False, map_location='cpu')
+    model.eval()
+    return model
+
+def predict(text, model):
+    with torch.no_grad():
+        output = model(text)
+        return float(output)
+
+def create_gradio_app():
+    repo_id = "jane-street/2025-03-10"
+    model_filename = "model.pt"
+    model = load_model_from_hub(repo_id, model_filename)
+    
+    with gr.Blocks() as demo:
+        gr.Markdown('''        
+        Today I went on a hike and found a pile of tensors hidden underneath a neolithic burial mound!
+        
+        I sent it over to the local neural plumber, and they managed to cobble together this.
+        
+        **[model.pt](https://huggingface.co/jane-street/2025-03-10/blob/main/model.pt)**
+                    
+        Anyway, I'm not sure what it does yet, but it must have been important to this past civilization. 
+        Maybe start by looking at the last two layers. 
+        ''')
+        
+        input_text = gr.Textbox(label="Model Input", value='vegetable dog') # two words?
+        output = gr.Number(label="Model Output")
+        
+        input_text.submit(fn=lambda x: predict(x, model), inputs=input_text, outputs=output)
+        
+        gr.Markdown('''
+        If you do figure it out, please let us know at *archaeology@janestreet.com*.
+        ''')
+
+    demo.queue(max_size=1_000)
+    
+    return demo
+
+    
+if __name__ == "__main__":
+    app = create_gradio_app()
+    app.launch(show_api=False)

requirements.txt

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+torch
+gradio
+huggingface_hub
+cloudpickle