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| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from modality_lstm import ModalityLSTM | |
| import torch.nn as nn | |
| from helper import score_to_modality | |
| from PIL import Image | |
| label_mapping = { | |
| 'car': [0,'images/Cars.jpg'], | |
| 'walk': [1,'images/walk.jpg'], | |
| 'bus': [2,'images/bus.jpg'], | |
| 'train': [3,'images/train.jpg'], | |
| 'subway': [4,'images/subway.jpg'], | |
| 'bike': [5,'images/bike.jpg'], | |
| 'run': [6,'images/walk.jpg'], | |
| 'boat': [7,'images/walk.jpg'], | |
| 'airplane': [8,'images/walk.jpg'], | |
| 'motorcycle': [9,'images/walk.jpg'], | |
| 'taxi': [10,'images/taxi.jpg'] | |
| } | |
| def pred(dist,speed,accel,timedelta,jerk,bearing,bearing_rate): | |
| batch_size = 1 | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| train_on_gpu = False | |
| output_size = 5 | |
| hidden_dim = 128 | |
| trip_dim = 7 | |
| n_layers = 2 | |
| drop_prob = 0.2 | |
| net = ModalityLSTM(trip_dim, output_size, batch_size, hidden_dim, n_layers, train_on_gpu, drop_prob, lstm_drop_prob=0.2) | |
| net.load_state_dict(torch.load("Model_Wieghts",map_location=torch.device('cpu'))) | |
| net.eval() | |
| a=torch.tensor([[dist,speed,accel,timedelta,jerk,bearing,bearing_rate]]) | |
| a=a.float() | |
| a=a.unsqueeze(0) | |
| l = torch.tensor([1]).long() | |
| b,c=net(a,l) | |
| b=b.squeeze(0) | |
| b=score_to_modality(b) | |
| b=b[0] | |
| print(b) | |
| for k,v in label_mapping.items(): | |
| if b == v[0]: | |
| return (str(k),Image.open(v[1])) | |
| def greet(name): | |
| return "Hello " + name + "!!" | |
| iface = gr.Interface(fn=pred, inputs=['number',"number","number",'number',"number","number","number"], outputs=["text",gr.outputs.Image(type="pil")]) | |
| iface.launch() |