add base code

.github/workflows/run.yaml

@@ -0,0 +1,20 @@
+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push --force https://MakiAi:$HF_TOKEN@huggingface.co/spaces/MakiAi/UE5_LAgentVisual main

README.md

@@ -1 +1,14 @@
-# UE5_LAgentVisual
+---
+title: UE5 LAgentVisual
+emoji: 🔥
+colorFrom: pink
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.28.2
+app_file: app.py
+pinned: false
+---
+
+# UE5_LAgentVisual
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+import re
+import pandas as pd
+import plotly.graph_objects as go
+
+def extract_data_from_log(file_content):
+    pattern = r"Iter:\s+(\d+)\s+\|\s+Avg Reward:\s+([-\d.]+)\s+\|\s+Avg Return:\s+([-\d.]+)\s+\|\s+Avg Value:\s+([-\d.]+)\s+\|\s+Avg Episode Length:\s+([-\d.]+)"
+    data = {'Iteration': [], 'Avg Reward': [], 'Avg Return': [], 'Avg Value': [], 'Avg Episode Length': []}
+
+    for line in file_content:
+        match = re.search(pattern, line)
+        if match:
+            data['Iteration'].append(int(match.group(1)))
+            data['Avg Reward'].append(float(match.group(2)))
+            data['Avg Return'].append(float(match.group(3)))
+            data['Avg Value'].append(float(match.group(4)))
+            data['Avg Episode Length'].append(float(match.group(5)))
+
+    return pd.DataFrame(data)
+
+def moving_average(data, window_size):
+    return data.rolling(window=window_size).mean()
+
+def plot_metric(df, metric, window_size):
+    ma_df = moving_average(df, window_size)
+    fig = go.Figure()
+
+    # Add traces for raw data and moving average
+    fig.add_trace(go.Scatter(x=df['Iteration'], y=df[metric], mode='lines', name=metric))
+    fig.add_trace(go.Scatter(x=df['Iteration'], y=ma_df[metric], mode='lines', name=f'{metric} (MA)'))
+
+    # Update layout
+    fig.update_layout(title=f'{metric} and Moving Average',
+                      xaxis_title='Iteration',
+                      yaxis_title=metric)
+    return fig
+
+# Streamlit app
+st.title("UE5 Learning to Drive Data Visualizer")
+
+uploaded_file = st.file_uploader("Upload your log file", type=["log"])
+window_size = st.slider("Select window size for moving average", min_value=1, max_value=100, value=10)
+
+if uploaded_file is not None:
+    file_content = uploaded_file.readlines()
+    file_content = [line.decode("utf-8") for line in file_content]
+    
+    df = extract_data_from_log(file_content)
+
+    st.header("Average Reward")
+    st.plotly_chart(plot_metric(df, 'Avg Reward', window_size), use_container_width=True)
+
+    st.header("Average Return")
+    st.plotly_chart(plot_metric(df, 'Avg Return', window_size), use_container_width=True)
+
+    st.header("Average Value")
+    st.plotly_chart(plot_metric(df, 'Avg Value', window_size), use_container_width=True)
+
+    st.header("Average Episode Length")
+    st.plotly_chart(plot_metric(df, 'Avg Episode Length', window_size), use_container_width=True)