Upload folder using huggingface_hub

README.md

@@ -1,12 +1,6 @@
 ---
-title: Billy Dashboard
-emoji: 🏢
-colorFrom: indigo
-colorTo: indigo
+title: billy_dashboard
+app_file: app.py
 sdk: gradio
 sdk_version: 5.29.1
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,256 @@
+# -*- coding: utf-8 -*-
+import asyncio
+import aiohttp
+import nest_asyncio
+import pandas as pd
+import requests
+import json
+import urllib.parse
+import gradio as gr
+
+wallets = [
+    "J7QAjhEGTAx71RoS1Nnuz4aKqK866xEnRXky4zhPa2WG",
+    "9huTEYifjBMJVhPWG3dX84S1Pr6pgXBH9i7274dpPeMr",
+    "CoA4vLyykjYobxEsDWHEx2Rna9WribGQMxSxVkH5NwN6",
+    "BpaD3QF9Z2YtUqPRxpTn7dgC2sBzpzxro5zrEdwhwNd9",
+    "FWmhs5vdUSMHUE5sHPQmd1hBEQVWD8wyCPe9op3KwTNg",
+    "HW1qacccywvtEmUK6mDWfaJfxcM1R64T8BmhfjmxA6Nx",
+    "3QpWHc77Vze5uiqm6WDfxYawKUj1bVbKEegS3YYBLGvL",
+    "5FyNV778E1SZ4ZBwUXCgbx43zrJFdMy4UgUysSw3yocU",
+    "4L3wPZc8smLYQbnpMSxHCiRB5cojbtuc33NkTNsrtdug",
+    "MruHB1owAkBQwtSkzTqTDpqZEDPmz4iUWz3oHCYHKWV"
+]
+
+SCRAPE_DO_TOKEN = "c3cb4da35304433483052fb6ba0c7011fef2ff42d62"
+
+common_headers = {
+    "accept": "application/json",
+    "accept-encoding": "identity",
+    "accept-language": "en-GB,en-US;q=0.9,en;q=0.8",
+    "authorization": "Bearer CGtF4EdvDbBpwUXmZSKW3HsYkajy7e",
+    "content-type": "application/json",
+    "origin": "https://portfolio.jup.ag",
+    "referer": "https://portfolio.jup.ag/",
+    "user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/136.0.0.0 Safari/537.36",
+    "x-turnstile-token": "0.replS6iD2yH0tx4VW09xgMrsCEIhseDU5PLWuo893A_Ekt3xg5uRfVTGixcTrEgV-lHCrY1MdVrBbARS4d3pvNdSjTGlyE9joXTSFSwgPi3b-vSVZ68uxq-VwgMWVBscYHQQSD8jSt_cDyl8C57Zqfc5GAeF8KLvCW6TzMDUGUkuAL824tStFs2o-16lgma5L_36AH0DxhriHsm_4bI9E8vAezX9QNyW9t7d6Io5nCyFTAcNbhXA5hvblmwprOop7Nb_j_dDjmJDUNl_SUmmbG5SshHwoXrIeJDGuSP5q47TL6xpgUlyjqg8cRboczfQVL09bn82rsb_6mOz84vm0aAH3I2lOPGeTPcaIBIyjfg4Eb2I-_YAbfVl3yPzwDCBKW8p4CCCTIVJU1pV638M_EFMOnnGk3RcXb9TVIpIkGnvatCOMzCPQHXGDpT1qbiA7YQnuZjr62oSEXwyQrsmKnXYqwC0K7iRpxjanYKfJR7B6NGRj4w5f-VJUnQd9u_zxHp2yNkz7MvQlIbUYMPaenL66HvAD0EoPzDWd8xWxivo-SME42YqXGRxw4Pe0cn48BvnfO7ZwebV1fCOGy9Mb2bQc_JxEArkmPCNu9YCJ-Cs_mSFlZ6Q5PM-VtBV2NFlV8itMea4GDEU823bGLoAgQJ_ct0Vxh5Jcn3CE94wIItk1ahZDgXCoY38Hq57U3iA3OFaCl9MvA3L8WXkWgmqezJLGl0cjj5TuxaAIlFnpSir4jdZHsD-ET7HlUeiz07Jy9rkG-Jtj1Gl5mA8lfoUloEn5eiW7P_JaediaYHZ5VmaVqVMvzLFjiZ27y9WLuRViRtlM15d-1w0isvBFI2LBXezx3UT-owLhXjeBE28y4M.AlrCxH1Qs0BV-6wumeZlZw.d75bf4fc0601ec6ac26dd1e7ab7753521759ecefbe078db1222669ae67a8fde4"  # keep this updated
+}
+
+async def fetch_portfolio(session, wallet_address, results):
+    base_url = f"https://portfolio-api-jup.sonar.watch/v1/portfolio/fetch?address={wallet_address}&addressSystem=solana"
+    encoded_url = urllib.parse.quote(base_url)
+    proxy_url = f"http://api.scrape.do/?token={SCRAPE_DO_TOKEN}&url={encoded_url}&forwardHeaders=True&super=True"
+
+    try:
+        async with session.get(proxy_url, headers=common_headers) as resp:
+            text = await resp.json()
+            if resp.status == 200:
+                print(f"✅ Wallet {wallet_address[:6]}...: success")
+                results.append({"wallet": wallet_address,"data": text})
+            else:
+                print(f"❌ Wallet {wallet_address[:6]}...: HTTP {resp.status}")
+                results.append({"wallet": wallet_address, "data": None})
+    except Exception as e:
+        print(f"⚠️ Error fetching {wallet_address}: {e}")
+        results.append({"wallet": wallet_address, "status": "error", "error": str(e)})
+
+async def get_data(wallet_addresses):
+    results = []
+    async with aiohttp.ClientSession() as session:
+        tasks = [fetch_portfolio(session, address, results) for address in wallet_addresses]
+        await asyncio.gather(*tasks)
+    return results
+#Have a button function which does all this
+
+async def process_data():
+  portfolios = await get_data(wallets)
+  df = pd.DataFrame(portfolios)
+  token_rows = []
+
+  for index, row in df.iterrows():
+      data = row['data']
+
+      # Token info mapping
+      token_info = data.get('tokenInfo', {}).get('solana', {})
+
+      # Navigate to assets list
+      elements = data.get('elements', [])
+      for element in elements:
+          if element.get('type') == 'multiple':
+              assets = element.get('data', {}).get('assets', [])
+              for asset in assets:
+                  asset_data = asset.get('data', {})
+                  address = asset_data.get('address')
+                  amount = asset_data.get('amount')
+                  value = asset.get('value')
+
+                  # Get symbol using address
+                  symbol = token_info.get(address, {}).get('symbol', 'UNKNOWN')
+
+                  token_rows.append({
+                      'original_row': index,
+                      'address': address,
+                      'symbol': symbol,
+                      'amount': amount,
+                      'value': value
+                  })
+
+  # Final DataFrame
+  tokens_df = pd.DataFrame(token_rows)
+  tokens_df['original_row'] = tokens_df['original_row'].apply(lambda x: df.loc[x, 'wallet'])
+  tokens_df = tokens_df.dropna()
+
+  # Pivot for values
+  value_pivot = tokens_df.pivot(index='original_row', columns='symbol', values='value')
+  value_pivot.columns = [f'{col}_value' for col in value_pivot.columns]
+
+  # Pivot for amounts
+  amount_pivot = tokens_df.pivot(index='original_row', columns='symbol', values='amount')
+  amount_pivot.columns = [f'{col}_amount' for col in amount_pivot.columns]
+
+  # Combine both
+  final_df = pd.concat([value_pivot, amount_pivot], axis=1).reset_index()
+
+  # Optional: Fill NaNs with 0
+  final_df = final_df.fillna(0)
+
+  rows = []
+  for idx, row in df.iterrows():
+      data = row['data']
+      original_row = row['wallet']  # or whatever uniquely identifies the row
+
+      for element in data.get("elements", []):
+          if element.get("platformId") == "jupiter-exchange":
+              input_token = element.get("data", {}).get("assets", {}).get("input", {})
+              if input_token:
+                  token_data = input_token.get("data", {})
+                  address = token_data.get("address")
+                  value = input_token.get("value")
+                  amount = token_data.get("amount")
+                  symbol = token_info.get(address, {}).get("symbol", address)
+
+                  rows.append({
+                      "original_row": original_row,
+                      "address": address,
+                      "symbol": symbol,
+                      "amount": amount,
+                      "value": value
+                  })
+
+  # Convert to DataFrame
+  jup_tokens_df = pd.DataFrame(rows)
+
+  jup_tokens_df['symbol'] = jup_tokens_df['symbol'].apply(lambda x: "SOL" if x == "So11111111111111111111111111111111111111112" else x)
+
+  # Pivot
+  # Group by original_row and symbol, sum value and amount
+  tokens_grouped = jup_tokens_df.groupby(['original_row', 'symbol']).agg({
+      'value': 'sum',
+      'amount': 'sum'
+  }).reset_index()
+
+  # Pivot
+  value_pivot = tokens_grouped.pivot(index='original_row', columns='symbol', values='value')
+  value_pivot.columns = [f'{col}_value' for col in value_pivot.columns]
+
+  amount_pivot = tokens_grouped.pivot(index='original_row', columns='symbol', values='amount')
+  amount_pivot.columns = [f'{col}_amount' for col in amount_pivot.columns]
+
+  jup_df = pd.concat([value_pivot, amount_pivot], axis=1).reset_index().fillna(0)
+
+  final_df1 = final_df.set_index('original_row')
+  final_df2 = jup_df.set_index('original_row')
+
+  # Combine the two DataFrames, adding values where they overlap
+  combined_df = final_df1.add(final_df2, fill_value=0)
+
+  # Reset index if you want original_row back as a column
+  combined_df = combined_df.reset_index()
+
+  # Optional: fill NaNs (if any) with 0 just in case
+  combined_df = combined_df.fillna(0)
+
+  # Sum all numeric columns except the original_row which is non-numeric
+  totals = combined_df.select_dtypes(include='number').sum()
+
+  # Add a row with these totals at the bottom of the dataframe
+  totals_row = pd.DataFrame(totals).T
+  totals_row['original_row'] = 'Total'
+
+  # Append the totals row to the original df
+  df_with_totals = pd.concat([combined_df, totals_row], ignore_index=True)
+
+  totals = final_df.select_dtypes(include='number').sum()
+
+  # Add a row with these totals at the bottom of the dataframe
+  totals_row = pd.DataFrame(totals).T
+  totals_row['original_row'] = 'Total'
+
+  # Append the totals row to the original df
+  final_df = pd.concat([final_df, totals_row], ignore_index=True)
+
+  totals = jup_df.select_dtypes(include='number').sum()
+
+  # Add a row with these totals at the bottom of the dataframe
+  totals_row = pd.DataFrame(totals).T
+  totals_row['original_row'] = 'Total'
+
+  # Append the totals row to the original df
+  jup_df = pd.concat([jup_df, totals_row], ignore_index=True)
+
+  final_df = final_df.rename(columns={'original_row': ' '})
+  jup_df = jup_df.rename(columns={'original_row': ' '})
+  df_with_totals = df_with_totals.rename(columns={'original_row': ' '})
+
+  sol_price = 170
+  billy_price = 0.001845
+
+  # Starting balances
+  starting_balances = {
+      " ": ["Total"],
+      "SOL_amount": [344],
+      "SOL_value": [344 * sol_price],
+      "BILLY_amount": [75_000_000],
+      "BILLY_value": [75_000_000 * billy_price]
+  }
+
+  # Create the DataFrame
+  starting_df = pd.DataFrame(starting_balances)
+
+  return(starting_df,final_df.tail(1),jup_df.tail(1),df_with_totals.tail(1))
+
+async def display_data():
+    df0, df1, df2, df3 = await process_data()
+
+    def summarize(df):
+        value_cols = [col for col in df.columns if "value" in col.lower()]
+        total_value = df[value_cols].sum(axis=1).values[0] if value_cols else 0
+        return df, total_value
+
+    df0, total0 = summarize(df0)
+    df3, total3 = summarize(df3)
+
+    pnl = float(total3) - float(total0)
+
+    return summarize(df0) + summarize(df1) + summarize(df2) + summarize(df3) + (f"PNL : {pnl:,.2f}"," ")
+
+with gr.Blocks() as demo:
+    gr.Markdown("## BILLY Balances and PnL")
+    btn = gr.Button("Fetch & Display Balances")
+
+    df0_out = gr.Dataframe(label="Starting Balances")
+    txt0_out = gr.Textbox(label="Starting Value Total")
+
+    df1_out = gr.Dataframe(label="Wallet Balances")
+    txt1_out = gr.Textbox(label="Wallet Value Total")
+
+    df2_out = gr.Dataframe(label="JUP Limit Balances")
+    txt2_out = gr.Textbox(label="JUP Limit Value Total")
+
+    df3_out = gr.Dataframe(label="Total Balances")
+    txt3_out = gr.Textbox(label="Current Value Total")
+
+    pnl_out = gr.Textbox(label="PNL (AUM Model)")
+
+    btn.click(display_data, outputs=[df0_out, txt0_out, df1_out, txt1_out, df2_out, txt2_out, df3_out,txt3_out,pnl_out])
+
+demo.launch(debug=True)

requirements.txt

@@ -0,0 +1,8 @@
+asyncio
+aiohttp
+nest_asyncio
+pandas
+requests
+json
+urllib.parse
+gradio