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APP

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+import requests
+import pandas as pd
+import gradio as gr
+
+# Assuming we have an API endpoint that provides the required data
+API_ENDPOINT = "https://api.example.com/crypto/top-volume/12h"
+
+def fetch_top_cryptos():
+    # Placeholder for API call and data processing
+    # In a real scenario, replace this with a call to the actual API
+    response = requests.get(API_ENDPOINT)
+    if response.status_code == 200:
+        data = response.json()
+        # Assuming the API returns data in a format that can be directly converted to a DataFrame
+        df = pd.DataFrame(data)
+        # Selecting and formatting the relevant columns
+        df = df[['name', 'volume_invested']]
+        return df
+    else:
+        return "Error fetching data"
+
+# Gradio interface
+iface = gr.Interface(
+    fn=fetch_top_cryptos,
+    inputs=None,
+    outputs="dataframe",
+    title="Top Cryptocurrencies by Volume",
+    description="Shows the top cryptocurrencies by volume invested in the past 12 hours."
+)
+
+# The API key for deploying on Hugging Face Spaces
+api_key = "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"
+
+# Launch the app locally for testing
+if __name__ == "__main__":
+    iface.launch()
+
+# To deploy on Hugging Face Spaces, use the `launch` method with `share=True` and the `api_key` parameter