| import gradio as gr |
| import torch |
| import torch.nn as nn |
| import numpy as np |
| from huggingface_hub import PyTorchModelHubMixin |
|
|
|
|
| MODULATION_TYPES = ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"] |
|
|
| class ModulationClassifier( |
| nn.Module, |
| PyTorchModelHubMixin, |
| library_name="modulation-classifier", |
| pipeline_tag="tabular-classification", |
| license="cc-by-nc-sa-4.0", |
| ): |
| def __init__(self, num_classes: int = 11, input_channels: int = 2, input_length: int = 128): |
| super().__init__() |
| self.conv1 = nn.Conv1d(input_channels, 64, kernel_size=8, padding="same") |
| self.relu1 = nn.ReLU() |
| self.pool1 = nn.MaxPool1d(2) |
| self.conv2 = nn.Conv1d(64, 128, kernel_size=5, padding="same") |
| self.relu2 = nn.ReLU() |
| self.pool2 = nn.MaxPool1d(2) |
| self.flatten = nn.Flatten() |
| self.fc1 = nn.Linear(128 * (input_length // 4), 256) |
| self.relu3 = nn.ReLU() |
| self.dropout = nn.Dropout(0.5) |
| self.fc2 = nn.Linear(256, num_classes) |
|
|
| def forward(self, x): |
| x = self.pool1(self.relu1(self.conv1(x))) |
| x = self.pool2(self.relu2(self.conv2(x))) |
| x = self.flatten(x) |
| x = self.dropout(self.relu3(self.fc1(x))) |
| x = self.fc2(x) |
| return x |
| model = ModulationClassifier.from_pretrained("tandrewu1/5G-Signal-Modulation-Classifier") |
| model.eval() |
|
|
|
|
| def predict(iq_text): |
| try: |
| lines = iq_text.strip().split("\n") |
| i_samples = [float(x) for x in lines[0].split(",")] |
| q_samples = [float(x) for x in lines[1].split(",")] |
|
|
| if len(i_samples) != 128 or len(q_samples) != 128: |
| return {"Error": 1.0} |
|
|
| iq_data = np.array([[i_samples, q_samples]], dtype=np.float32) |
| tensor_input = torch.tensor(iq_data) |
|
|
| with torch.no_grad(): |
| output = model(tensor_input) |
| probabilities = torch.nn.functional.softmax(output[0], dim=0) |
|
|
| result = {MODULATION_TYPES[i]: prob.item() for i, prob in enumerate(probabilities)} |
| return result |
|
|
| except Exception as e: |
| return {"Error": 1.0} |
|
|
| demo = gr.Interface( |
| fn=predict, |
| inputs=gr.Textbox( |
| label="IQ Samples (line 1: 128 I values comma-separated, line 2: 128 Q values comma-separated)", |
| lines=4, |
| placeholder="0.01, -0.03, 0.05, ...\n0.02, 0.01, -0.04, ...", |
| ), |
| outputs=gr.Label(label="Predicted Modulation", num_top_classes=5), |
| title="5G Signal Modulation Classifier", |
| description="Classify radio signal modulation type from raw IQ samples. Paste 128 I-channel values on line 1 and 128 Q-channel values on line 2.", |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
