Upload 2 files

main.py

@@ -0,0 +1,123 @@
+# Import necessary libraries
+import torch
+
+# Load the model
+model = torch.load("model.pth")
+
+agents = [
+    'Brimstone',
+    'Viper',
+    'Omen',
+    'Killjoy',
+    'Cypher',
+    'Sova',
+    'Sage',
+    'Phoenix',
+    'Jett',
+    'Reyna',
+    'Raze',
+    'Breach',
+    'Skye',
+    'Yoru',
+    'Astra',
+    'KAY/O',
+    'Chamber',
+    'Neon',
+    'Fade',
+    'Harbor',
+    'Gekko',
+    'Deadlock',
+    'Iso',
+]
+maps = [
+    'Ascent',
+    'Bind',
+    'Breeze',
+    'Fracture',
+    'Haven',
+    'Icebox',
+    'Lotus',
+    'Pearl',
+    'Split',
+    'Sunset',
+]
+ranks = [
+    'Iron 1',
+    'Iron 2',
+    'Iron 3',
+    'Bronze 1',
+    'Bronze 2',
+    'Bronze 3',
+    'Silver 1',
+    'Silver 2',
+    'Silver 3',
+    'Gold 1',
+    'Gold 2',
+    'Gold 3',
+    'Platinum 1',
+    'Platinum 2',
+    'Platinum 3',
+    'Diamond 1',
+    'Diamond 2',
+    'Diamond 3',
+    'Ascendant 1',
+    'Ascendant 2',
+    'Ascendant 3',
+    'Immortal 1',
+    'Immortal 2',
+    'Immortal 3',
+    'Radiant',
+]
+
+def preprocess_data(data):
+    # Preprocess the data (replace this with your specific preprocessing steps)
+    processed_data = ranks.index(processed_data[0])
+    processed_data[1] = maps.index(processed_data[1])
+    processed_data[2:7] = [agents.index(agent) for agent in processed_data[2:7]]
+
+    inputs = torch.tensor(processed_data, dtype = torch.float32)
+
+    return processed_data
+
+# Define your prediction function
+def make_prediction(data):
+    # Preprocess the data (replace this with your specific preprocessing steps)
+    processed_data = preprocess_data(data)
+
+    # Feed the data to the model
+    output = model(processed_data)
+
+    # Post-process the output (replace this with your specific post-processing steps)
+    prediction = model(data)
+    prediction = prediction.item()
+    prediction = 0 if prediction < 0 else prediction
+
+    winrate = str(round(prediction * 100)) + '%'
+
+    print(f"Calculated Winrate: {winrate}")
+
+    return winrate
+
+# Example usage
+#data = ...  # your input data
+#prediction = predict(data)
+
+#print(f"Prediction: {prediction}")
+
+import gradio as gr
+
+# Create Gradio interface with relevant inputs
+interface = gr.Interface(
+    fn=make_prediction,
+    inputs=[
+        # Input for rank
+        gr.Dropdown(label="Rank", choices=ranks, default=ranks[0]),
+        # Input for map
+        gr.Dropdown(label="Map", choices=maps, default=maps[0]),
+        # Input for agents
+        gr.MultiSelect(label="Agent Picks (1-5)", choices=agents, default=[agents[0]]),
+    ],
+    outputs="text",
+)
+
+interface.launch()

model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b24468490743bdc701e980f21a54591e2bebc53c0091d49d1093d32c92a946a
+size 39604