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import streamlit as st
import requests
from typing import List, Dict
import os
API_URL = os.getenv('POND_API_URL')
API_TOKEN = os.getenv('POND_API_TOKEN')
st.title('🌊 Pond Model Demo')
st.sidebar.header('Model Selection')
model_info = {
1: {
'name': 'Security Model',
'description': 'Analyze whether an account is secure by detecting and examining malicious activities within complex blockchain data structures.'
},
2: {
'name': 'Sybil Model',
'description': 'An model aims to detect on-chain "Sybil Attacks"'
},
3: {
'name': 'ZORA NFT Recommendation',
'description': 'On-Chain Recommendation System: Making Discoveries & Spread Easier for Everyone'
}
}
selected_model = st.sidebar.selectbox(
'Choose a model',
list(model_info.keys()),
format_func=lambda x: model_info[x]['name']
)
# Show model description
st.markdown(f"### {model_info[selected_model]['name']}")
st.markdown(model_info[selected_model]['description'])
# Input section
st.header('Input Wallet Addresses')
wallet_input = st.text_area(
'Enter wallet addresses (one per line)',
height=100,
help='Enter wallet addresses, one per line'
)
def predict(addresses: List[str], model_id: int) -> Dict:
"""
Make prediction using the Pond API
"""
if not API_URL:
st.error('API URL is not configured. Please set POND_API_URL environment variable.')
return None
headers = {
"Content-Type": "application/json"
}
try:
payload = {
"req_type": "1",
"access_token": API_TOKEN,
"input_keys": addresses,
"model_id": model_id
}
headers = {
"Content-Type": "application/json"
}
# Make the API call with explicit method
session = requests.Session()
req = requests.Request('POST',
API_URL,
json=payload,
headers=headers)
prepped = req.prepare()
response = session.send(prepped,
allow_redirects=True)
# Check response
if response.status_code != 200:
st.error(f"API Error: {response.status_code}")
st.error(f"Response: {response.text}")
return None
return response.json()
except Exception as e:
st.error(f"Error making prediction: {str(e)}")
return None
def display_results(response: Dict, model_id: int):
"""
Display the results based on model type
"""
if not response or 'resp_items' not in response:
return
if model_id in [1, 2]: # Security and Sybil models
st.header('Results')
for item in response['resp_items']:
score = item['score']
address = item['input_key']
# Create color coding based on score
if score < 0.3:
color = 'green'
elif score < 0.7:
color = 'orange'
else:
color = 'red'
st.markdown(f"""
**Address**: `{address}`
**Score**: <span style='color: {color}'>{score:.4f}</span>
""", unsafe_allow_html=True)
st.markdown("")
elif model_id == 3: # NFT Recommendation model
st.header('NFT Recommendations')
for item in response['resp_items']:
address = item['input_key']
st.subheader(f'Recommendations for: `{address}`')
if 'candidates' in item:
for idx, candidate in enumerate(item['candidates'][:5], 1):
st.markdown(f"""
**{idx}. NFT ID**: `{candidate['item_id']}`
**Score**: {candidate['score']:.4f}
""")
st.markdown("")
# Process button
if st.button('Get Predictions'):
if wallet_input:
# Process input addresses
addresses = [addr.strip() for addr in wallet_input.split('\n') if addr.strip()]
# Validate addresses
valid_addresses = [addr for addr in addresses if addr.startswith('0x')]
if not valid_addresses:
st.error('Please enter valid Ethereum addresses (starting with 0x)')
else:
with st.spinner('Making predictions...'):
response = predict(valid_addresses, selected_model)
if response:
display_results(response, selected_model)
else:
st.warning('Please enter at least one wallet address')
# Footer
st.markdown("---")
st.markdown("ℹ️ This is a demo interface for the Pond Model API. For production use, please refer to the official documentation.") |