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app.py

@@ -14,13 +14,26 @@ import pennylane as qml
 dev = qml.device('default.qubit', wires=10)
 
 # Hugging Face and DuckDB function placeholders
+from datasets import load_dataset, Dataset
+
 def store_in_hf_dataset(data):
-    # Implement storing data in the Hugging Face dataset
-    pass
+    # Convert data to Hugging Face Dataset format
+    dataset = Dataset.from_dict({
+        'id': [item[0] for item in data],
+        'hamiltonian': [item[2] for item in data],
+        'qasm_code': [item[3] for item in data],
+        'trotter_code': [item[4] for item in data],
+        'num_qubits': [item[5] for item in data],
+        'trotter_order': [item[6] for item in data],
+        'timestamp': [str(item[7]) for item in data],
+    })
+    # Push to Hugging Face dataset hub (replace with your dataset path)
+    dataset.push_to_hub("your-username/BoltzmannEntropy-QuantumLLMInstruct")
 
 def load_from_hf_dataset():
-    # Implement loading data from the Hugging Face dataset
-    return []
+    # Load from Hugging Face dataset
+    dataset = load_dataset("your-username/BoltzmannEntropy-QuantumLLMInstruct")
+    return dataset
 
 # Function to buffer the plot and return as PIL image
 def buffer_plot_and_get(fig):
@@ -146,7 +159,6 @@ def load_results(load_from_hf_checkbox, load_from_duckdb_checkbox):
     if load_from_duckdb_checkbox:
         return load_from_duckdb()  # Load from DuckDB
 
-# Gradio app
 # Gradio app
 with gr.Blocks() as app:
     gr.Markdown("# Quantum Hamiltonian Generator")
@@ -193,5 +205,4 @@ with gr.Blocks() as app:
 
         load_button.click(load_results, inputs=[load_option], outputs=output_display)
 
-
 app.launch(share=True)