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| import requests | |
| import asyncio | |
| import aiohttp | |
| import pandas as pd | |
| import urllib | |
| import gradio as gr | |
| import os | |
| from pymongo import MongoClient | |
| base_addresses = ["0xCaFe02A66B7fa9E63D5Ea13128cC8A82477AA418", | |
| "0x33C65AbDD4DaAc415738e05caFc38574bEfC1278", | |
| "0xC13b4E80208c24BaE4fCfC3b6DBc35b818b910D9", | |
| "0xcFcd6d25aAD11E40e4B0A158a92fD086A0CCDd69"] | |
| bsc_addresses = ["0x57AbFf97abe0c873893B71809b67826170934F27", | |
| "0x60BE905be793C2260d13945dfd4CEEAf45449332", | |
| "0x9f07773a51330eAED5DD580A30dDa7b5760C8396", | |
| "0x8dC9cF38E22229168F9400ee2427aA6b3F3bDCF8"] | |
| lp_wallets = ["0x57AbFf97abe0c873893B71809b67826170934F27", | |
| "0x9f07773a51330eAED5DD580A30dDa7b5760C8396", | |
| "0xC13b4E80208c24BaE4fCfC3b6DBc35b818b910D9", | |
| "0xcFcd6d25aAD11E40e4B0A158a92fD086A0CCDd69"] | |
| def chunk_addresses(addresses, size): | |
| return [addresses[i:i + size] for i in range(0, len(addresses), size)] | |
| API_URL = "https://api.g.alchemy.com/data/v1/yhe6L3PXmiENzS1sP9Fu4_T5E3l0QyeB/assets/tokens/by-address" | |
| async def fetch_batch(session, batch,chain): | |
| json_payload = { | |
| "addresses": [{"address": addr, "networks": [chain]} for addr in batch], | |
| "withMetadata": True, | |
| "withPrices": True, | |
| "includeNativeTokens": True | |
| } | |
| async with session.post(API_URL, json=json_payload) as response: | |
| response = await response.json() | |
| return response['data']['tokens'] | |
| async def get_wallet_base_balances(addresses): | |
| async with aiohttp.ClientSession() as session: | |
| batches = chunk_addresses(addresses, 3) | |
| tasks = [fetch_batch(session, batch,"base-mainnet") for batch in batches] | |
| responses = await asyncio.gather(*tasks) | |
| first_parts = [] | |
| rest_parts = [] | |
| for res in responses: | |
| first_parts.extend(res[:3]) | |
| rest_parts.extend(res[3:]) | |
| responses = first_parts + rest_parts | |
| df = pd.DataFrame(responses) | |
| for i in range(len(addresses)): | |
| df.at[i,'tokenMetadata'] = {} | |
| new_metadata = { | |
| 'decimals': 18, | |
| 'logo': None, | |
| 'name': "", | |
| 'symbol': "LGCT" | |
| } | |
| df.loc[df['tokenAddress'] == '0xd38b305cac06990c0887032a02c03d6839f770a8', 'tokenMetadata'] = [new_metadata] | |
| df['tokenBalance'] = df['tokenBalance'].apply(lambda x : int(x,16)) | |
| df['decimals'] = df['tokenMetadata'].apply(lambda x : x.get('decimals',0)) | |
| df['tokenBalance'] = df['tokenBalance']/(10**df['decimals']) | |
| for i in range(len(addresses)): | |
| df.at[i, 'tokenBalance'] = df.at[i, 'tokenBalance'] / (10**18) | |
| df['symbol'] = df['tokenMetadata'].apply(lambda x : x.get('symbol','')) | |
| for i in range(len(addresses)): | |
| df.at[i,'symbol'] = 'ETH' | |
| df['currentPrice'] = df['tokenPrices'].apply(lambda x : x[0].get('value',None) if len(x) > 0 else None) | |
| df.drop(columns=['tokenPrices','tokenMetadata','network','tokenAddress'], inplace=True) | |
| df = df.dropna() | |
| df['tokenBalance'] = pd.to_numeric(df['tokenBalance'], errors='coerce') | |
| df = df[df['tokenBalance'] != 0] | |
| df['tokenBalance'] = pd.to_numeric(df['tokenBalance'], errors='coerce') | |
| df['currentPrice'] = pd.to_numeric(df['currentPrice'], errors='coerce') | |
| base_price = df.loc[df['symbol']=='LGCT','currentPrice'].values[0] | |
| df['value'] = df['tokenBalance'] * df['currentPrice'] | |
| df.drop(columns=['address','decimals'],inplace=True) | |
| df = df[df['symbol'].isin(['LGCT', 'ETH', 'USDT', 'USDC'])] | |
| df= df[['symbol','tokenBalance','value']] | |
| df = df.rename(columns={'tokenBalance':'amount','value':'usd_value'}) | |
| df = df.groupby("symbol", as_index=False).sum() | |
| return df, df['usd_value'].sum(),base_price | |
| async def get_wallet_bsc_balances(addresses): | |
| async with aiohttp.ClientSession() as session: | |
| batches = chunk_addresses(addresses, 3) | |
| tasks = [fetch_batch(session, batch,"bnb-mainnet") for batch in batches] | |
| responses = await asyncio.gather(*tasks) | |
| first_parts = [] | |
| rest_parts = [] | |
| for res in responses: | |
| first_parts.extend(res[:3]) | |
| rest_parts.extend(res[3:]) | |
| responses = first_parts + rest_parts | |
| df = pd.DataFrame(responses) | |
| for i in range(len(addresses)): | |
| df.at[i,'tokenMetadata'] = {} | |
| df['tokenBalance'] = df['tokenBalance'].apply(lambda x : int(x,16)) | |
| df['decimals'] = df['tokenMetadata'].apply(lambda x : x.get('decimals',0)) | |
| df['tokenBalance'] = df['tokenBalance']/(10**df['decimals']) | |
| for i in range(len(addresses)): | |
| df.at[i, 'tokenBalance'] = df.at[i, 'tokenBalance'] / (10**18) | |
| df['symbol'] = df['tokenMetadata'].apply(lambda x : x.get('symbol','')) | |
| for i in range(len(addresses)): | |
| df.at[i,'symbol'] = 'BNB' | |
| df['currentPrice'] = df['tokenPrices'].apply(lambda x : x[0].get('value',None) if len(x) > 0 else None) | |
| df.drop(columns=['tokenPrices','tokenMetadata','network','tokenAddress'], inplace=True) | |
| df = df.dropna() | |
| df['tokenBalance'] = pd.to_numeric(df['tokenBalance'], errors='coerce') | |
| df = df[df['tokenBalance'] != 0] | |
| df['tokenBalance'] = pd.to_numeric(df['tokenBalance'], errors='coerce') | |
| df['currentPrice'] = pd.to_numeric(df['currentPrice'], errors='coerce') | |
| df['value'] = df['tokenBalance'] * df['currentPrice'] | |
| df.drop(columns=['address','decimals'],inplace=True) | |
| df = df[df['symbol'].isin(['LGCT', 'BNB', 'USDT', 'USDC'])] | |
| df= df[['symbol','tokenBalance','value']] | |
| df = df.rename(columns={'tokenBalance':'amount','value':'usd_value'}) | |
| df = df.groupby("symbol", as_index=False).sum() | |
| return df, df['usd_value'].sum() | |
| def agg_balances(lp): | |
| lps = [] | |
| for i in range(len(lp)): | |
| two = lp['node.tokens'].iloc[i] | |
| more_lp = pd.json_normalize(two) | |
| more_lp['token.balance'] = more_lp['token.balance'].apply(pd.to_numeric,errors='coerce') | |
| more_lp['token.balanceUSD'] = more_lp['token.balanceUSD'].apply(pd.to_numeric,errors='coerce') | |
| x = more_lp.groupby('token.symbol')[['token.balance','token.balanceUSD']].sum().reset_index() | |
| #x.columns = x.iloc[0] # Set first row as column headers | |
| #x = x.drop(x.index[0]).reset_index(drop=True) # Drop the row that became header | |
| #x = x.apply(pd.to_numeric, errors='coerce') # Convert all to numeric | |
| lps.append(x) | |
| bals_df = pd.concat(lps, ignore_index=True) | |
| bals_df['token.balance'] = bals_df['token.balance'].apply(pd.to_numeric,errors='coerce') | |
| bals_df['token.balanceUSD'] = bals_df['token.balanceUSD'].apply(pd.to_numeric,errors='coerce') | |
| bals_df.fillna(0, inplace=True) | |
| bals_df = bals_df.groupby('token.symbol').sum().reset_index() | |
| return bals_df | |
| def get_dex_balances(df): | |
| balances = [] | |
| for i in range(len(pd.json_normalize(df['node.positionBalances.edges']))): | |
| balances.append((df['node.app.displayName'].iloc[i],agg_balances(pd.json_normalize(df['node.positionBalances.edges'].iloc[i])))) | |
| final_balances = [] | |
| for i in range(len(balances)): | |
| df = balances[i] | |
| dex_name = df[0] # or extract from your grouped index | |
| df_pivot = df[1].pivot_table(index=None, columns='token.symbol', values='token.balance') | |
| # Add DEX column and reorder | |
| df_pivot.insert(0,'DEX', dex_name) | |
| # Remove the first level of row index (e.g., 'token.symbol') | |
| df_pivot = df_pivot.reset_index(drop=True) | |
| df_pivot.columns.name = None | |
| final_balances.append(df_pivot) | |
| final_balances = pd.concat(final_balances, ignore_index=True) | |
| final_balances.fillna(0, inplace=True) | |
| return final_balances | |
| async def get_lp_balances(): | |
| url = "https://public.zapper.xyz/graphql" | |
| headers = { | |
| "Content-Type": "application/json", | |
| "x-zapper-api-key": "8fe2c210-66e0-4ef9-9505-96a901c9b042" | |
| } | |
| query = """ | |
| query AppBalances($addresses: [Address!]!, $first: Int = 10) { | |
| portfolioV2(addresses: $addresses) { | |
| appBalances { | |
| totalBalanceUSD | |
| byApp(first: $first) { | |
| totalCount | |
| edges { | |
| node { | |
| balanceUSD | |
| app { | |
| displayName | |
| imgUrl | |
| description | |
| category { name } | |
| } | |
| network { | |
| name | |
| chainId | |
| } | |
| positionBalances(first: 10) { | |
| edges { | |
| node { | |
| ... on AppTokenPositionBalance { | |
| type | |
| symbol | |
| balance | |
| balanceUSD | |
| price | |
| groupLabel | |
| displayProps { | |
| label | |
| images | |
| } | |
| } | |
| ... on ContractPositionBalance { | |
| type | |
| balanceUSD | |
| groupLabel | |
| tokens { | |
| metaType | |
| token { | |
| ... on BaseTokenPositionBalance { | |
| symbol | |
| balance | |
| balanceUSD | |
| } | |
| } | |
| } | |
| displayProps { | |
| label | |
| images | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| """ | |
| variables = { | |
| "addresses": lp_wallets, | |
| "first": 5 | |
| } | |
| payload = { | |
| "query": query, | |
| "variables": variables | |
| } | |
| response = requests.post(url, json=payload, headers=headers) | |
| response = response.json() | |
| df = pd.json_normalize(response['data']['portfolioV2']['appBalances']['byApp']['edges']) | |
| lp_df = get_dex_balances(df) | |
| return lp_df | |
| async def get_all_dfs(): | |
| bsc_df,bsc_total = await get_wallet_bsc_balances(bsc_addresses) | |
| base_df,base_total,base_price = await get_wallet_base_balances(base_addresses) | |
| lp_df = await get_lp_balances() | |
| placeholder_df = lp_df.drop(columns=['DEX']).sum() | |
| placeholder_df = pd.DataFrame(placeholder_df) | |
| placeholder_df = placeholder_df.rename(columns={0: 'amount'}).reset_index().rename(columns={'index': 'symbol'}) | |
| prices = { | |
| 'USDT': 1.0, | |
| 'USDC': 1.0, | |
| 'LGCT': base_price | |
| } | |
| placeholder_df['usd_value'] = placeholder_df['symbol'].map(prices) * placeholder_df['amount'] | |
| final_df = pd.concat([bsc_df,base_df,placeholder_df]) | |
| final_df = final_df.groupby("symbol", as_index=False).sum() | |
| return bsc_df, bsc_total, base_df, base_total, lp_df, placeholder_df['usd_value'].sum(), final_df, final_df['usd_value'].sum() | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## LGCT DEX Balances") | |
| df0_out = gr.Dataframe(label="BSC Wallet Balances") | |
| txt0_out = gr.Textbox(label="Total Value") | |
| df1_out = gr.Dataframe(label="Base Wallet Balances") | |
| txt1_out = gr.Textbox(label="Total Value") | |
| df2_out = gr.Dataframe(label="LP Balances") | |
| txt2_out = gr.Textbox(label="Total Value") | |
| df3_out = gr.Dataframe(label="Total Balances") | |
| txt3_out = gr.Textbox(label="Total Value") | |
| # Load from MongoDB on app load (sync function) | |
| demo.load( | |
| fn= get_all_dfs, | |
| inputs = [], | |
| outputs=[df0_out, txt0_out, df1_out, txt1_out, df2_out, txt2_out,df3_out,txt3_out] | |
| ) | |
| demo.launch(debug=True, share=True) |