feat: add demo app

app.py

@@ -0,0 +1,153 @@
+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.")