Add application file

app.py

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+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import gradio as gr
+
+# Load the saved model and tokenizer
+model_path = "trained_model"
+model = T5ForConditionalGeneration.from_pretrained(model_path)
+tokenizer = T5Tokenizer.from_pretrained(model_path)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+
+# Define the function to generate a sequence based on the input text
+def generate_sequence(input_text):
+    test_input_tokens = tokenizer(input_text, return_tensors='pt').to(device)
+
+    model.eval()
+    with torch.no_grad():
+        test_output_tokens = model.generate(test_input_tokens['input_ids'], num_return_sequences=1)
+
+    test_output_sequence = tokenizer.decode(test_output_tokens[0], skip_special_tokens=True)
+    return test_output_sequence
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=generate_sequence,
+    inputs=gr.inputs.Textbox(lines=3, label="Input Text"),
+    outputs=gr.outputs.Textbox(label="Generated Sequence"),
+    title="MIDI Sequence Generator",
+    description="Generate a MIDI sequence based on a text description",
+    examples=[
+        "A popular classical piano piece composed by Ludwig van Beethoven",
+        "A beautiful and melancholic classical piano piece composed by Frédéric Chopin",
+    ]
+)
+
+# Launch the Gradio interface
+iface.launch(share=True, debug=True)

requirements.txt

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+gradio
+torch
+transformers

trained_model/config.json

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+{
+  "_name_or_path": "t5-small",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 6,
+  "num_heads": 8,
+  "num_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.28.1",
+  "use_cache": true,
+  "vocab_size": 32128
+}

trained_model/generation_config.json

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+{
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+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.28.1"
+}

trained_model/pytorch_model.bin

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+size 242069785

trained_model/special_tokens_map.json

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trained_model/spiece.model

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trained_model/tokenizer_config.json

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+}