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LSTM_Trainer

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Ltlu commited on May 24, 2025

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a7584b6

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1 Parent(s): bd8d5b3

Update app.py

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app.py +31 -21

app.py CHANGED Viewed

@@ -1,20 +1,23 @@
 import gradio as gr
-def make_html(training_text, iterations, gen_length, layer1_size, layer2_size):
-    # Sanitize training text for JS
     safe_text = training_text.replace("\\", "\\\\").replace("\"", "\\\"").replace("\n", "\\n")
-    html = f"""
-    <div>
       <h3>Live LSTM Training Output (updates every 10s)</h3>
       <pre id="output">Starting training...</pre>
-      <script src="https://karpathy.github.io/recurrentjs/recurrent.js"></script>
       <script>
         const text = "{safe_text}".split("");
         const totalIterations = {iterations};
         const genLength = {gen_length};
-        const lstm = new RNN("lstm", {{ hiddenSizes: [{layer1_size}, {layer2_size}] }});
         const trainer = new RNNTrainer(lstm, {{ learningRate: 0.01, momentum: 0.1, batchSize: 5 }});
         let iteration = 0;
@@ -33,28 +36,35 @@ def make_html(training_text, iterations, gen_length, layer1_size, layer2_size):
             clearInterval(interval);
             document.getElementById("output").innerText += "\\n\\nTraining complete!";
           }}
-        }}, 10000); // every 10 seconds
       </script>
-    </div>
     """
-    return html
-with gr.Blocks() as demo:
-    gr.Markdown("## 🧠 Live LSTM Trainer with recurrent.js (Browser-Based)")
-    training_text = gr.Textbox(label="Training Text", lines=6, placeholder="Paste your training data here...")
-    with gr.Row():
-        iterations = gr.Slider(10, 200, value=50, step=10, label="Total Training Epochs")
-        gen_length = gr.Slider(20, 500, value=100, step=10, label="Characters to Generate")
-    with gr.Row():
-        layer1_size = gr.Slider(16, 256, value=128, step=16, label="Neurons in Layer 1")
-        layer2_size = gr.Slider(16, 256, value=128, step=16, label="Neurons in Layer 2")
     run_button = gr.Button("Start Training")
-    output_html = gr.HTML()
-    run_button.click(fn=make_html, inputs=[training_text, iterations, gen_length, layer1_size, layer2_size], outputs=output_html)
 demo.launch()

 import gradio as gr
+import os
+def generate_html(training_text, iterations, gen_length, layer1, layer2):
     safe_text = training_text.replace("\\", "\\\\").replace("\"", "\\\"").replace("\n", "\\n")
+    html_content = f"""
+    <html>
+    <head>
+      <script src="https://karpathy.github.io/recurrentjs/recurrent.js"></script>
+    </head>
+    <body>
       <h3>Live LSTM Training Output (updates every 10s)</h3>
       <pre id="output">Starting training...</pre>
       <script>
         const text = "{safe_text}".split("");
         const totalIterations = {iterations};
         const genLength = {gen_length};
+        const lstm = new RNN("lstm", {{ hiddenSizes: [{layer1}, {layer2}] }});
         const trainer = new RNNTrainer(lstm, {{ learningRate: 0.01, momentum: 0.1, batchSize: 5 }});
         let iteration = 0;
             clearInterval(interval);
             document.getElementById("output").innerText += "\\n\\nTraining complete!";
           }}
+        }}, 10000);
       </script>
+    </body>
+    </html>
     """
+    # Save the HTML to the /tmp directory (accessible in Hugging Face Spaces)
+    html_path = "/tmp/train.html"
+    with open(html_path, "w") as f:
+        f.write(html_content)
+    # Return iframe HTML to embed the file
+    iframe_code = f'<iframe src="file={html_path}" width="100%" height="400px"></iframe>'
+    return iframe_code
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 Live recurrent.js LSTM Trainer (in-browser training)")
+    training_text = gr.Textbox(label="Training Text", lines=6, placeholder="Paste text here")
+    iterations = gr.Slider(10, 200, value=50, step=10, label="Training Epochs")
+    gen_length = gr.Slider(20, 500, value=100, step=10, label="Characters to Generate")
+    layer1 = gr.Slider(16, 256, value=128, step=16, label="Neurons in Layer 1")
+    layer2 = gr.Slider(16, 256, value=128, step=16, label="Neurons in Layer 2")
     run_button = gr.Button("Start Training")
+    html_output = gr.HTML()
+    run_button.click(fn=generate_html,
+                     inputs=[training_text, iterations, gen_length, layer1, layer2],
+                     outputs=html_output)
 demo.launch()