Create app.py

app.py

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+import torch
+import gradio as gr
+import torch.nn as nn
+import torch.nn.functional as F
+
+class NeuralNetwork(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        """
+        Initializes a neural network model.
+
+        Args:
+            input_size (int): The size of the input layer.
+            hidden_size (int): The size of the hidden layer.
+            output_size (int): The size of the output layer.
+        """
+        super(NeuralNetwork, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_size, output_size)
+
+    def forward(self, input_image):
+        """
+        Performs a forward pass through the neural network.
+
+        Args:
+            input_image (torch.Tensor): The input image tensor.
+
+        Returns:
+            torch.Tensor: The output tensor of the neural network.
+        """
+        input_image = self.relu(self.fc1(input_image))
+        input_image = self.fc2(input_image)
+        return input_image
+
+# Load the pre-trained model
+model = NeuralNetwork(14, 64, 2)
+model.load_state_dict(torch.load("model.pth"))
+
+# List of all Valorant agents
+maps = [
+    'Ascent',
+    'Bind',
+    'Breeze',
+    'Fracture',
+    'Haven',
+    'Icebox',
+    'Lotus',
+    'Pearl',
+    'Split',
+    'Sunset',
+]
+
+agents = [
+    'Brimstone',
+    'Viper',
+    'Omen',
+    'Killjoy',
+    'Cypher',
+    'Sova',
+    'Sage',
+    'Phoenix',
+    'Jett',
+    'Reyna',
+    'Raze',
+    'Breach',
+    'Skye',
+    'Yoru',
+    'Astra',
+    'Kayo',
+    'Chamber',
+    'Neon',
+    'Fade',
+    'Harbor',
+    'Gekko',
+    'Deadlock',
+    'Iso',
+]
+
+
+
+# Define the prediction function
+def predict(*args):
+    def test_convert(test):
+        test[3] = maps.index(test[3])
+        test[4:9] = [agents.index(index) for index in test[4:9]]
+        test[9:14] = [agents.index(index) for index in test[9:14]]
+
+        return test
+
+    data = list(args)
+    data = test_convert(data)
+    data = torch.tensor(data, dtype=torch.float32)
+
+    outputs = model(data)
+    highest_score = (torch.max(outputs), torch.argmax(outputs).item())
+
+    if highest_score[0] < 13:
+        outputs[highest_score[1]] = 13
+    else:
+        if outputs[1-highest_score[1]] < highest_score[0] - 2:
+            outputs[1-highest_score[1]] = highest_score[0] - 2
+
+    score_a = round(outputs[0].item())
+    score_b = round(outputs[1].item())
+
+    return f'Predicted score: {score_a} - {score_b}'
+
+
+
+# Define the output component
+with gr.Blocks() as demo:
+    # Frame for date and map
+    
+    with gr.Row():
+        with gr.Column(min_width="0px", scale=1):
+            year_input = gr.Number(label="Year", value=23)
+        with gr.Column(min_width="0px", scale=1):
+            month_input = gr.Number(label="Month", value=2)
+        with gr.Column(min_width="0px", scale=1):
+            day_input = gr.Number(label="Day", value=23)
+        with gr.Column(scale=3):
+            map_input = gr.Dropdown(maps, label="Map", value='Ascent')
+        with gr.Column(scale=3):
+            pass
+
+    # Frames for agents' dropdowns
+    with gr.Row():
+        with gr.Column():
+            # Team 1 agent dropdowns
+            team1_agent1_input = gr.Dropdown(choices=agents, label="Team 1 - Agent 1", value='Brimstone')
+            team1_agent2_input = gr.Dropdown(choices=agents, label="Team 1 - Agent 2", value='Viper')
+            team1_agent3_input = gr.Dropdown(choices=agents, label="Team 1 - Agent 3", value='Omen')
+            team1_agent4_input = gr.Dropdown(choices=agents, label="Team 1 - Agent 4", value='Killjoy')
+            team1_agent5_input = gr.Dropdown(choices=agents, label="Team 1 - Agent 5", value='Cypher')
+
+        with gr.Column():
+            # Team 2 agent dropdowns
+            team2_agent1_input = gr.Dropdown(choices=agents, label="Team 2 - Agent 1", value='Sova')
+            team2_agent2_input = gr.Dropdown(choices=agents, label="Team 2 - Agent 2", value='Sage')
+            team2_agent3_input = gr.Dropdown(choices=agents, label="Team 2 - Agent 3", value='Phoenix')
+            team2_agent4_input = gr.Dropdown(choices=agents, label="Team 2 - Agent 4", value='Jett')
+            team2_agent5_input = gr.Dropdown(choices=agents, label="Team 2 - Agent 5", value='Reyna')
+            # ... add all dropdowns for Team 2
+
+        with gr.Column():
+
+            translate_btn = gr.Button(value="Translate")
+            # Add any outputs you have
+            score_difference_output = gr.Textbox(label="Score Difference")
+            translate_btn.click(fn=predict, inputs=[year_input, month_input, day_input, map_input, team1_agent1_input, team1_agent2_input, team1_agent3_input, team1_agent4_input, team1_agent5_input, team2_agent1_input, team2_agent2_input, team2_agent3_input, team2_agent4_input, team2_agent5_input], outputs=score_difference_output)
+
+print('Lauching interface!')
+demo.launch()