app.py

import requests
import json
import pandas as pd
import gradio as gr

def return_df():
    response1 = requests.post(
  "https://api.g.alchemy.com/data/v1/yhe6L3PXmiENzS1sP9Fu4_T5E3l0QyeB/assets/tokens/by-address",
  headers={},
  json={
    "addresses": [
      {
        "address": "0x7743960B4305f9eAF4d412322B5fAb736DB51D4C",
        "networks": [
          "base-mainnet"
        ]
      },
      {
        "address": "0x3a6Cf81996b208f3167335f7075aB46e895382b1",
        "networks": [
          "base-mainnet"
        ]
      },
      {
        "address": "0xE040654071DC563a864Bb9EB56d592118CfCD468",
        "networks": [
          "base-mainnet"
        ]
      }
    ],
    "withMetadata": True,
    "withPrices": True,
    "includeNativeTokens": True
  },
)
    response2 = requests.post(
  "https://api.g.alchemy.com/data/v1/yhe6L3PXmiENzS1sP9Fu4_T5E3l0QyeB/assets/tokens/by-address",
  headers={},
  json={
    "addresses": [
      {
        "address": "0x6f648Cd40Abe3aCc21a9141Fb9C7074bcf939285",
        "networks": [
          "base-mainnet"
        ]
      },
      {
        "address": "0xd58Fc0D2a4Fe66FE9be63BeDDC8866fDdE50ce0e",
        "networks": [
          "base-mainnet"
        ]
      },
      {
        "address": "0x7D8A95508caedF57E853F81F49FB1714d75dE750",
        "networks": [
          "base-mainnet"
        ]
      }
    ],
    "withMetadata": True,
    "withPrices": True,
    "includeNativeTokens": True
  },
)
    response3 = requests.post(
  "https://api.g.alchemy.com/data/v1/yhe6L3PXmiENzS1sP9Fu4_T5E3l0QyeB/assets/tokens/by-address",
  headers={},
  json={
    "addresses": [
      {
        "address": "0x422473C72F19b0bdCA74d360CEDB72594C1b4Efa",
        "networks": [
          "base-mainnet"
        ]
      },
      {
        "address": "0x7743960B4305f9eAF4d412322B5fAb736DB51D4C",
        "networks": [
          "base-mainnet"
        ]
      }
    ],
    "withMetadata": True,
    "withPrices": True,
    "includeNativeTokens": True
  },
)

    response1 = response1.json()['data']['tokens']
    response2 = response2.json()['data']['tokens']
    response3 = response3.json()['data']['tokens']

    # Extract the prioritized items
    priority_items = response2[:3] + response3[:2]

    # Remove those items from their original lists to avoid duplication
    remaining_items = response2[3:] + response3[2:] + response1

    # Combine priority items with the rest
    response = priority_items + remaining_items

    df = pd.DataFrame(response)
    for i in range(8):
      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(8):
      df.at[i, 'tokenBalance'] = df.at[i, 'tokenBalance'] / (10**18)

    df['symbol'] = df['tokenMetadata'].apply(lambda x : x.get('symbol',''))
    for i in range(8):
      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')
    df['value'] = df['tokenBalance'] * df['currentPrice']
    df.drop(columns=['address','decimals'],inplace=True)

    df = df[df['symbol'].isin(['AGNT', 'ETH', 'USDT'])]
    df= df[['symbol','tokenBalance','value']]
    
    df = df.groupby("symbol", as_index=False).sum()

    return df, df['value'].sum()

with gr.Blocks() as demo:
    gr.Markdown("## AGNT Balances and PnL")

    df0_out = gr.Dataframe(label="Current Balances")
    txt0_out = gr.Textbox(label="Total Value")

    # Load from MongoDB on app load (sync function)
    demo.load(
        fn=return_df,
        inputs=[],
        outputs=[
            df0_out,txt0_out
        ]
    )

demo.launch(debug=True, share=True)