Added Inference API

inference.py

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+import torch
+from MorseH_Model import MorseHModel
+from transformers import pipeline
+
+# Load model
+model_path = "pytorch_model.bin"  # Your model weights file
+model = MorseHModel(input_size=<input_size>, output_size=<output_size>, max_length=<max_len>)
+model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
+model.eval()
+
+# Define preprocessing and decoding functions
+def encode(text):
+    # Convert text to a list of indices
+    return [<label_encoder>.transform([c])[0] for c in text]
+
+def predict(input_data):
+    with torch.no_grad():
+        inputs = torch.tensor([encode(input_data)])
+        output = model(inputs)
+        _, predicted = torch.max(output, 2)
+        return predicted[0]
+
+def decode(prediction):
+    morse_code = ''.join(['.' if c == 0 else '-' for c in prediction if c != 2])
+    return morse_code
+
+# Hugging Face Inference API function
+def inference(text):
+    morse_code = [decode(predict(word)) for word in text.split()]
+    return " ".join(morse_code)

model_index.json

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+{
+    "pipeline_tag": "text-to-morse",
+    "tags": ["morse-code", "pytorch", "custom-model"]
+}
+