Update app.py

app.py

@@ -20,6 +20,11 @@ print(response.status_code)
 print(response.text)
 import warnings
 import logging
+import os
+from dotenv import load_dotenv
+
+# Load environment variables from .env
+load_dotenv()
 
 # Suppress deprecation warnings about experimental query params functions
 warnings.filterwarnings(
@@ -32,14 +37,14 @@ warnings.filterwarnings(
 )
 warnings.filterwarnings("ignore", category=DeprecationWarning)
 
-# Adjust the Streamlit loggers to only show errors
+# Adjust Streamlit loggers to show only 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 cloudscraper
 
 # ---------------------------- #
 #         AUTO-REFRESH         #
@@ -49,49 +54,88 @@ st.set_page_config(
     layout="wide"
 )
 
-REFRESH_INTERVAL = 260  # 160 seconds
+REFRESH_INTERVAL = 260  # 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"
+# Get the API key from environment variables (stored in .env or Hugging Face Secrets)
+if not API_KEY:
+    st.error("API_KEY is not set. Please add it to your environment (e.g. .env file or Hugging Face Secrets).")
+    st.stop()
+
+# --- Helper Functions Using cloudscraper ---
+
+def get_active_markets_cloudscraper(api_key):
+    """Retrieves the list of active markets using cloudscraper to bypass Cloudflare."""
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "User-Agent": ("Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
+                       "AppleWebKit/537.36 (KHTML, like Gecko) "
+                       "Chrome/115.0.0.0 Safari/537.36")
+    }
+    url = "https://archeanvision.com/api/signals/available"
+    scraper = cloudscraper.create_scraper()
+    response = scraper.get(url, headers=headers)
+    response.raise_for_status()  # Raises an exception for HTTP errors
+    return response.json()  # Assuming the endpoint returns JSON
+
+def get_market_data_cloudscraper(api_key, market):
+    """Retrieves market data for the given market using cloudscraper."""
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "User-Agent": ("Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
+                       "AppleWebKit/537.36 (KHTML, like Gecko) "
+                       "Chrome/115.0.0.0 Safari/537.36")
+    }
+    # Endpoint for market data (1,440 points ~ 24h); adjust as per API docs
+    url = f"https://archeanvision.com/api/signals/{market}/data"
+    scraper = cloudscraper.create_scraper()
+    response = scraper.get(url, headers=headers)
+    response.raise_for_status()
+    return response.json()
+
+def get_market_signals_cloudscraper(api_key, market):
+    """Retrieves market signals for the given market using cloudscraper."""
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "User-Agent": ("Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
+                       "AppleWebKit/537.36 (KHTML, like Gecko) "
+                       "Chrome/115.0.0.0 Safari/537.36")
+    }
+    url = f"https://archeanvision.com/api/signals/{market}/signals"
+    scraper = cloudscraper.create_scraper()
+    response = scraper.get(url, headers=headers)
+    response.raise_for_status()
+    return response.json()
+
+# --- End Helper Functions ---
 
 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.
+    Also updates the query parameter if the user picks a different market from the dropdown.
     
-    Using experimental_* methods for compatibility with older Streamlit versions (<1.25).
+    (Uses experimental methods for compatibility with older Streamlit versions.)
     """
-    # 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)
-
+        st.experimental_set_query_params(market=selected)
     return selected
 
 def main():
@@ -99,39 +143,38 @@ def main():
 
     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()
+    # Retrieve active markets using cloudscraper
+    try:
+        active_markets = get_active_markets_cloudscraper(API_KEY)
+    except Exception as e:
+        st.error(f"Error fetching active markets: {e}")
+        return
+
     if not active_markets:
         st.error("No active markets found through the API.")
         return
 
-    # 3. Build a list of markets
+    # Expecting active_markets to be a list of market names, e.g. ["BTC", "ETH", ...]
     market_list = []
     if isinstance(active_markets, list):
         for item in active_markets:
+            # Depending on the response structure, adjust accordingly.
             if isinstance(item, dict) and "market" in item:
                 market_list.append(item["market"])
             elif isinstance(item, str):
@@ -141,83 +184,82 @@ def main():
     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'
-            }
+    if not selected_market:
+        st.error("No market selected.")
+        return
+
+    st.subheader(f"Selected Market: {selected_market}")
+    st.write(f"Fetching data for **{selected_market}** ...")
+
+    # Retrieve market data using cloudscraper
+    try:
+        market_data = get_market_data_cloudscraper(API_KEY, selected_market)
+    except Exception as e:
+        st.error(f"Error fetching market data for {selected_market}: {e}")
+        return
+
+    if not market_data:
+        st.error(f"No data found for market {selected_market}.")
+        return
+
+    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())
+
+    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)}"
         )
-        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))
+        return
+
+    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)
+
+    st.write(f"### Signals for {selected_market}")
+    try:
+        signals = get_market_signals_cloudscraper(API_KEY, selected_market)
+    except Exception as e:
+        st.error(f"Error fetching signals for {selected_market}: {e}")
+        return
+
+    if not signals:
+        st.warning(f"No signals found for market {selected_market}.")
+    else:
+        df_signals = pd.DataFrame(signals)
+        if 'date' in df_signals.columns:
+            df_signals['date'] = pd.to_datetime(df_signals['date'], unit='ms', errors='coerce')
+        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)
+        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()
-