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