Update app.py

app.py

@@ -18,4 +18,206 @@ response = scraper.get(url, headers=headers)
 
 print(response.status_code)
 print(response.text)
+import warnings
+import logging
+
+# Suppress deprecation warnings about experimental query params functions
+warnings.filterwarnings(
+    "ignore", 
+    message="Please replace `st.experimental_get_query_params` with `st.query_params`"
+)
+warnings.filterwarnings(
+    "ignore", 
+    message="Please replace `st.experimental_set_query_params` with `st.query_params`"
+)
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+# Adjust the Streamlit loggers to only show errors
+logging.getLogger("streamlit.deprecation").setLevel(logging.ERROR)
+logging.getLogger("streamlit.runtime.scriptrunner").setLevel(logging.ERROR)
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+from archeanvision import ArcheanVisionAPI
+
+# ---------------------------- #
+#         AUTO-REFRESH         #
+# ---------------------------- #
+st.set_page_config(
+    page_title="Dashboard Auto-Refresh",
+    layout="wide"
+)
+
+REFRESH_INTERVAL = 260  # 160 seconds
+st.markdown(f"<meta http-equiv='refresh' content='{REFRESH_INTERVAL}'>", unsafe_allow_html=True)
+# ---------------------------- #
+
+LOGO_IMAGE_URL = "https://archeanvision.com/assets/archeanvision.png"
+st.sidebar.image(LOGO_IMAGE_URL, use_container_width=True, caption="ArcheanVision")
+
+# Replace with your API key
+#API_KEY = "0be4b0d76de6cf65b81e0a2de91b137f432e5a66e6d0c622d73c4b83e613e948"
+
+def get_selected_market(market_list):
+    """
+    Returns the selected market from the URL query params or defaults to the first item.
+    Also updates the query param if the user picks a different market from the dropdown.
+    
+    Using experimental_* methods for compatibility with older Streamlit versions (<1.25).
+    """
+    # 1. Read current query parameters (EXPERIMENTAL)
+    params = st.experimental_get_query_params()
+
+    # 2. Check if 'market' param is set; otherwise default to the first market
+    if "market" in params:
+        default_market = params["market"]
+        # If it's a list, pick the first element
+        if isinstance(default_market, list):
+            default_market = default_market[0]
+    else:
+        default_market = market_list[0]
+
+    # 3. Determine the index to use in the selectbox
+    if default_market in market_list:
+        default_index = market_list.index(default_market)
+    else:
+        default_index = 0
+
+    # 4. Create the dropdown
+    selected = st.selectbox("Select a market:", market_list, index=default_index)
+
+    # 5. If user picks a new market, update URL param (EXPERIMENTAL)
+    if selected != default_market:
+        params["market"] = selected
+        st.experimental_set_query_params(**params)
+
+    return selected
+
+def main():
+    st.title("Active AI Crypto Markets - ArcheanVision")
+
+    st.markdown("""
+    ### What is ArcheanVision?
+
+    **ArcheanVision** is an autonomous multi-market trading agent.  
+    It operates simultaneously on multiple crypto assets, monitoring price movements
+    in real time and delivering **data** as well as **signals** (BUY, SELL, etc.)
+    to automate and optimize decision-making.
+
+    - **AI Agent**: Continuously analyzes crypto markets.  
+    - **Multi-Market**: Manages multiple assets at once.  
+    - **Live Data**: Access to streaming data feeds (SSE).  
+    - **Buy/Sell Signals**: Generated in real-time to seize market opportunities.  
+
+    Below is a dashboard showcasing the active markets, their 24h data 
+    (1,440 most recent data points), and their associated signals.
+
+    ---
+    **Join our Discord as a beta tester** to help improve the agent and the system.  
+    - Official platform: [https://archeanvision.com](https://archeanvision.com)  
+    - Discord link: [https://discord.gg/k9xHuM7Jr8](https://discord.gg/k9xHuM7Jr8)
+    """)
+
+    # 1. Initialize the API
+    api = ArcheanVisionAPI(api_key=API_KEY)
+    
+    # 2. Retrieve active markets
+    active_markets = api.get_active_markets()
+    if not active_markets:
+        st.error("No active markets found through the API.")
+        return
+
+    # 3. Build a list of markets
+    market_list = []
+    if isinstance(active_markets, list):
+        for item in active_markets:
+            if isinstance(item, dict) and "market" in item:
+                market_list.append(item["market"])
+            elif isinstance(item, str):
+                market_list.append(item)
+            else:
+                st.warning(f"Item missing 'market' key: {item}")
+    else:
+        st.error("The structure of 'active_markets' is not a list as expected.")
+        return
+    
+    if not market_list:
+        st.error("The market list is empty or 'market' keys not found.")
+        return
+
+    # 4. Get the selected market from (experimental) query params or default
+    selected_market = get_selected_market(market_list)
+    
+    if selected_market:
+        st.subheader(f"Selected Market: {selected_market}")
+        st.write(f"Fetching data for **{selected_market}** ...")
+        
+        # 5. Retrieve market data (1,440 points ~24h)
+        market_data = api.get_market_data(selected_market)
+        if not market_data:
+            st.error(f"No data found for market {selected_market}.")
+            return
+        
+        # 6. Convert to DataFrame & parse 'close_time'
+        df = pd.DataFrame(market_data)
+        if "close_time" in df.columns:
+            df['close_time'] = pd.to_datetime(df['close_time'], unit='ms', errors='coerce')
+        else:
+            st.error("The 'close_time' column is missing from the retrieved data.")
+            return
+
+        st.write("### Market Data Overview")
+        st.dataframe(df.head())
+
+        # 7. Check columns before plotting
+        required_cols = {"close", "last_predict_15m", "last_predict_1h"}
+        if not required_cols.issubset(df.columns):
+            st.error(
+                f"The required columns {required_cols} are not all present. "
+                f"Available columns: {list(df.columns)}"
+            )
+            return
+
+        # 8. Create a Plotly line chart
+        fig = px.line(
+            df,
+            x='close_time',
+            y=['close', 'last_predict_15m', 'last_predict_1h'],
+            title=f"{selected_market} : Close Price & Predictions",
+            labels={
+                'close_time': 'Time',
+                'value': 'Price',
+                'variable': 'Metric'
+            }
+        )
+        st.plotly_chart(fig, use_container_width=True)
+        
+        # 9. Retrieve & display signals for the selected market
+        st.write(f"### Signals for {selected_market}")
+        signals = api.get_market_signals(selected_market)
+        if not signals:
+            st.warning(f"No signals found for market {selected_market}.")
+        else:
+            df_signals = pd.DataFrame(signals)
+            
+            # Convert 'date' if present
+            if 'date' in df_signals.columns:
+                df_signals['date'] = pd.to_datetime(df_signals['date'], unit='ms', errors='coerce')
+            
+            # Fix Arrow errors for dict columns
+            for col in df_signals.columns:
+                if df_signals[col].apply(lambda x: isinstance(x, dict)).any():
+                    df_signals[col] = df_signals[col].apply(lambda x: str(x) if isinstance(x, dict) else x)
+
+            # Sort signals by date descending if desired
+            if 'date' in df_signals.columns:
+                df_signals = df_signals.sort_values('date', ascending=False)
+
+            st.write("Total number of signals:", len(df_signals))
+            st.write("Preview of the last 4 signals:")
+            st.dataframe(df_signals.head(4))
+
+if __name__ == "__main__":
+    main()