Update app.py

app.py

@@ -1,204 +1,10 @@
-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
+import requests
 import os
-from dotenv import load_dotenv
-load_dotenv()
-# ---------------------------- #
-#         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 = os.getenv("API_KEY")
+headers = {"Authorization": f"Bearer {API_KEY}"}
+url = "https://archeanvision.com/api/signals/available"
+response = requests.get(url, headers=headers)
+print(response.status_code)
+print(response.text)
 
-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()