Update app.py
Browse files
app.py
CHANGED
@@ -1,274 +1,274 @@
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import streamlit as st
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import pandas as pd
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import joblib
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import plotly.express as px
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import numpy as np
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# Page configuration
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st.set_page_config(
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page_title="API Status Code Predictor",
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page_icon="๐ก",
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layout="wide"
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)
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# Custom CSS for better styling
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st.markdown("""
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<style>
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.main-header {
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font-size: 2.5rem;
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color: #1E88E5;
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margin-bottom: 0;
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}
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.sub-header {
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font-size: 1.1rem;
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color: #666;
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margin-top: 0;
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margin-bottom: 2rem;
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}
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.card {
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padding: 1.5rem;
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border-radius: 0.5rem;
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background-color: #f8f9fa;
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box-shadow: 0 0.25rem 0.75rem rgba(0, 0, 0, 0.1);
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margin-bottom: 1rem;
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}
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.highlight-number {
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font-size: 3rem;
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font-weight: bold;
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}
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.status-200 { color: #4CAF50; }
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.status-400 { color: #FF9800; }
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.status-500 { color: #F44336; }
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</style>
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""", unsafe_allow_html=True)
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# Load model
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@st.cache_resource
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def load_model():
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return joblib.load("
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try:
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model = load_model()
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model_loaded = True
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except:
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st.error("โ ๏ธ Model file not found. Using a placeholder for demonstration purposes.")
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model_loaded = False
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# Create a dummy model for UI demonstration
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class DummyModel:
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def __init__(self):
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self.classes_ = np.array([200, 400, 500])
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def predict(self, X):
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return np.array([200])
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def predict_proba(self, X):
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return np.array([[0.75, 0.15, 0.10]])
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model = DummyModel()
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# Header section
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st.markdown("<h1 class='main-header'>๐ก API Status Code Predictor</h1>", unsafe_allow_html=True)
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st.markdown(
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"<p class='sub-header'>Analyze API behaviors and predict response status codes based on request parameters</p>",
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unsafe_allow_html=True)
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# Create two columns for layout
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col1, col2 = st.columns([3, 5])
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# Sidebar with inputs - now moved to a card in the left column
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with col1:
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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st.subheader("๐ Request Parameters")
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# API and Environment selection with more informative labels
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api_options = {
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"OrderProcessor": "Order Processing API",
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"AuthService": "Authentication Service",
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"ProductCatalog": "Product Catalog API",
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"PaymentGateway": "Payment Gateway"
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}
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api_id = st.selectbox("API Service", list(api_options.keys()), format_func=lambda x: api_options[x])
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env = st.selectbox(
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"Environment",
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["production-useast1", "staging"],
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format_func=lambda x: f"{'Production (US East)' if x == 'production-useast1' else 'Staging'}"
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)
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# More organized parameter inputs with tooltips
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st.subheader("โ๏ธ Performance Metrics")
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latency_ms = st.slider(
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"Latency (ms)",
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min_value=0.0,
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max_value=100.0,
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value=10.0,
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help="Response time in milliseconds"
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)
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bytes_transferred = st.slider(
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"Bytes Transferred",
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min_value=0,
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max_value=15000,
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value=500,
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help="Size of data transferred in bytes"
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)
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st.subheader("๐ Request Context")
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hour_of_day = st.select_slider(
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"Hour of Day",
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options=list(range(24)),
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value=12,
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format_func=lambda x: f"{x:02d}:00"
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)
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cpu_cost = st.slider(
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"CPU Cost",
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min_value=0.0,
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max_value=50.0,
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value=10.0,
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help="Computational resources used"
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)
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memory_mb = st.slider(
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"Memory Usage (MB)",
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min_value=0.0,
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max_value=100.0,
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value=25.0,
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help="Memory consumption in megabytes"
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)
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# Add a predict button to make prediction more intentional
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predict_button = st.button("๐ฎ Predict Status Code", use_container_width=True)
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st.markdown("</div>", unsafe_allow_html=True)
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# Mapping to codes - moved after selection
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api_id_code = {"OrderProcessor": 2, "AuthService": 0, "ProductCatalog": 1, "PaymentGateway": 3}[api_id]
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env_code = {"production-useast1": 1, "staging": 0}[env]
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# Input for prediction
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input_data = pd.DataFrame([[api_id_code, env_code, latency_ms, bytes_transferred, hour_of_day, cpu_cost, memory_mb]],
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columns=['api_id', 'env', 'latency_ms', 'bytes_transferred', 'hour_of_day',
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'simulated_cpu_cost', 'simulated_memory_mb'])
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# Results section on the right
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with col2:
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if predict_button or not model_loaded:
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# Predict
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prediction = model.predict(input_data)[0]
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probabilities = model.predict_proba(input_data)
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# Format prediction results
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status_codes = {
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200: "Success (200)",
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400: "Client Error (400)",
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500: "Server Error (500)"
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}
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status_class = {
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200: "status-200",
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400: "status-400",
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500: "status-500"
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}
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# Display the prediction in a card
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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st.subheader("๐ฏ Prediction Result")
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st.markdown(
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f"<p>Most likely status code:</p><h1 class='highlight-number {status_class[prediction]}'>{prediction}</h1><p>{status_codes.get(prediction, 'Unknown')}</p>",
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unsafe_allow_html=True)
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# Show prediction confidence
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prob_dict = {int(model.classes_[i]): float(probabilities[0][i]) for i in range(len(model.classes_))}
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confidence = prob_dict[prediction] * 100
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st.write(f"Confidence: {confidence:.1f}%")
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st.markdown("</div>", unsafe_allow_html=True)
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# Show probability distribution with a horizontal bar chart
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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st.subheader("๐ Probability Distribution")
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# Create dataframe for visualization
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prob_df = pd.DataFrame({
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'Status Code': [f"{int(code)} - {status_codes.get(int(code), 'Unknown')}" for code in model.classes_],
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'Probability': probabilities[0]
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})
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# Create a bar chart using Plotly
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fig = px.bar(
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prob_df,
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x='Probability',
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y='Status Code',
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orientation='h',
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color='Status Code',
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color_discrete_map={
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f"200 - {status_codes.get(200)}": '#4CAF50',
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f"400 - {status_codes.get(400)}": '#FF9800',
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f"500 - {status_codes.get(500)}": '#F44336'
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}
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)
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fig.update_layout(
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height=300,
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margin=dict(l=20, r=20, t=30, b=20),
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xaxis_title="Probability",
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yaxis_title="",
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xaxis=dict(tickformat=".0%")
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)
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st.plotly_chart(fig, use_container_width=True)
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st.markdown("</div>", unsafe_allow_html=True)
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# Parameters influence section
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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st.subheader("๐ Feature Importance")
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st.write("How different parameters influence the prediction:")
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# Mock feature importance for demonstration
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# In a real app, you'd use model-specific methods to calculate this
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feature_importance = {
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'API Service': 0.25,
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'Environment': 0.15,
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'Latency': 0.20,
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'Bytes Transferred': 0.10,
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'Time of Day': 0.05,
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'CPU Cost': 0.15,
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'Memory Usage': 0.10
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}
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# Create a horizontal bar chart for feature importance
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importance_df = pd.DataFrame({
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'Feature': list(feature_importance.keys()),
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'Importance': list(feature_importance.values())
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}).sort_values('Importance', ascending=False)
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fig_importance = px.bar(
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importance_df,
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x='Importance',
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y='Feature',
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orientation='h',
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color='Importance',
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color_continuous_scale='Blues'
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)
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fig_importance.update_layout(
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height=350,
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margin=dict(l=20, r=20, t=20, b=20),
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yaxis_title="",
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coloraxis_showscale=False
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)
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st.plotly_chart(fig_importance, use_container_width=True)
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st.markdown("</div>", unsafe_allow_html=True)
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# Footer with information
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st.markdown("---")
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st.markdown(
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"๐ก **About**: This tool uses machine learning to predict API response status codes based on request parameters and system metrics.")
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import streamlit as st
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import pandas as pd
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import joblib
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import plotly.express as px
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import numpy as np
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# Page configuration
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st.set_page_config(
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page_title="API Status Code Predictor",
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page_icon="๐ก",
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layout="wide"
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)
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# Custom CSS for better styling
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st.markdown("""
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<style>
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.main-header {
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font-size: 2.5rem;
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color: #1E88E5;
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margin-bottom: 0;
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}
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.sub-header {
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font-size: 1.1rem;
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color: #666;
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margin-top: 0;
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margin-bottom: 2rem;
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}
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.card {
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padding: 1.5rem;
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border-radius: 0.5rem;
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background-color: #f8f9fa;
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box-shadow: 0 0.25rem 0.75rem rgba(0, 0, 0, 0.1);
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margin-bottom: 1rem;
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}
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.highlight-number {
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font-size: 3rem;
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font-weight: bold;
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}
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.status-200 { color: #4CAF50; }
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.status-400 { color: #FF9800; }
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.status-500 { color: #F44336; }
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</style>
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""", unsafe_allow_html=True)
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# Load model
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@st.cache_resource
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def load_model():
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return joblib.load("status_code_classifier.pkl")
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try:
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model = load_model()
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model_loaded = True
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except:
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st.error("โ ๏ธ Model file not found. Using a placeholder for demonstration purposes.")
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model_loaded = False
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+
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+
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# Create a dummy model for UI demonstration
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class DummyModel:
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def __init__(self):
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self.classes_ = np.array([200, 400, 500])
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+
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def predict(self, X):
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return np.array([200])
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def predict_proba(self, X):
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return np.array([[0.75, 0.15, 0.10]])
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model = DummyModel()
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# Header section
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75 |
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st.markdown("<h1 class='main-header'>๐ก API Status Code Predictor</h1>", unsafe_allow_html=True)
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76 |
+
st.markdown(
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77 |
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"<p class='sub-header'>Analyze API behaviors and predict response status codes based on request parameters</p>",
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78 |
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unsafe_allow_html=True)
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79 |
+
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80 |
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# Create two columns for layout
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81 |
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col1, col2 = st.columns([3, 5])
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82 |
+
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83 |
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# Sidebar with inputs - now moved to a card in the left column
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84 |
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with col1:
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85 |
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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86 |
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st.subheader("๐ Request Parameters")
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87 |
+
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88 |
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# API and Environment selection with more informative labels
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89 |
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api_options = {
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90 |
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"OrderProcessor": "Order Processing API",
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91 |
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"AuthService": "Authentication Service",
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92 |
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"ProductCatalog": "Product Catalog API",
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93 |
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"PaymentGateway": "Payment Gateway"
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}
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api_id = st.selectbox("API Service", list(api_options.keys()), format_func=lambda x: api_options[x])
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96 |
+
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env = st.selectbox(
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"Environment",
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["production-useast1", "staging"],
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format_func=lambda x: f"{'Production (US East)' if x == 'production-useast1' else 'Staging'}"
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)
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102 |
+
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103 |
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# More organized parameter inputs with tooltips
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104 |
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st.subheader("โ๏ธ Performance Metrics")
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105 |
+
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106 |
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latency_ms = st.slider(
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107 |
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"Latency (ms)",
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108 |
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min_value=0.0,
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109 |
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max_value=100.0,
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110 |
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value=10.0,
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111 |
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help="Response time in milliseconds"
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112 |
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)
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113 |
+
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114 |
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bytes_transferred = st.slider(
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"Bytes Transferred",
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116 |
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min_value=0,
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117 |
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max_value=15000,
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118 |
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value=500,
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119 |
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help="Size of data transferred in bytes"
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)
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+
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st.subheader("๐ Request Context")
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123 |
+
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hour_of_day = st.select_slider(
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"Hour of Day",
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options=list(range(24)),
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value=12,
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format_func=lambda x: f"{x:02d}:00"
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)
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+
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cpu_cost = st.slider(
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"CPU Cost",
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133 |
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min_value=0.0,
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134 |
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max_value=50.0,
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135 |
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value=10.0,
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help="Computational resources used"
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)
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+
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memory_mb = st.slider(
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"Memory Usage (MB)",
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141 |
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min_value=0.0,
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142 |
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max_value=100.0,
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143 |
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value=25.0,
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144 |
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help="Memory consumption in megabytes"
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145 |
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)
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146 |
+
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147 |
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# Add a predict button to make prediction more intentional
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148 |
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predict_button = st.button("๐ฎ Predict Status Code", use_container_width=True)
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149 |
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st.markdown("</div>", unsafe_allow_html=True)
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150 |
+
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151 |
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# Mapping to codes - moved after selection
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152 |
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api_id_code = {"OrderProcessor": 2, "AuthService": 0, "ProductCatalog": 1, "PaymentGateway": 3}[api_id]
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153 |
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env_code = {"production-useast1": 1, "staging": 0}[env]
|
154 |
+
|
155 |
+
# Input for prediction
|
156 |
+
input_data = pd.DataFrame([[api_id_code, env_code, latency_ms, bytes_transferred, hour_of_day, cpu_cost, memory_mb]],
|
157 |
+
columns=['api_id', 'env', 'latency_ms', 'bytes_transferred', 'hour_of_day',
|
158 |
+
'simulated_cpu_cost', 'simulated_memory_mb'])
|
159 |
+
|
160 |
+
# Results section on the right
|
161 |
+
with col2:
|
162 |
+
if predict_button or not model_loaded:
|
163 |
+
# Predict
|
164 |
+
prediction = model.predict(input_data)[0]
|
165 |
+
probabilities = model.predict_proba(input_data)
|
166 |
+
|
167 |
+
# Format prediction results
|
168 |
+
status_codes = {
|
169 |
+
200: "Success (200)",
|
170 |
+
400: "Client Error (400)",
|
171 |
+
500: "Server Error (500)"
|
172 |
+
}
|
173 |
+
|
174 |
+
status_class = {
|
175 |
+
200: "status-200",
|
176 |
+
400: "status-400",
|
177 |
+
500: "status-500"
|
178 |
+
}
|
179 |
+
|
180 |
+
# Display the prediction in a card
|
181 |
+
st.markdown("<div class='card'>", unsafe_allow_html=True)
|
182 |
+
st.subheader("๐ฏ Prediction Result")
|
183 |
+
|
184 |
+
st.markdown(
|
185 |
+
f"<p>Most likely status code:</p><h1 class='highlight-number {status_class[prediction]}'>{prediction}</h1><p>{status_codes.get(prediction, 'Unknown')}</p>",
|
186 |
+
unsafe_allow_html=True)
|
187 |
+
|
188 |
+
# Show prediction confidence
|
189 |
+
prob_dict = {int(model.classes_[i]): float(probabilities[0][i]) for i in range(len(model.classes_))}
|
190 |
+
confidence = prob_dict[prediction] * 100
|
191 |
+
st.write(f"Confidence: {confidence:.1f}%")
|
192 |
+
st.markdown("</div>", unsafe_allow_html=True)
|
193 |
+
|
194 |
+
# Show probability distribution with a horizontal bar chart
|
195 |
+
st.markdown("<div class='card'>", unsafe_allow_html=True)
|
196 |
+
st.subheader("๐ Probability Distribution")
|
197 |
+
|
198 |
+
# Create dataframe for visualization
|
199 |
+
prob_df = pd.DataFrame({
|
200 |
+
'Status Code': [f"{int(code)} - {status_codes.get(int(code), 'Unknown')}" for code in model.classes_],
|
201 |
+
'Probability': probabilities[0]
|
202 |
+
})
|
203 |
+
|
204 |
+
# Create a bar chart using Plotly
|
205 |
+
fig = px.bar(
|
206 |
+
prob_df,
|
207 |
+
x='Probability',
|
208 |
+
y='Status Code',
|
209 |
+
orientation='h',
|
210 |
+
color='Status Code',
|
211 |
+
color_discrete_map={
|
212 |
+
f"200 - {status_codes.get(200)}": '#4CAF50',
|
213 |
+
f"400 - {status_codes.get(400)}": '#FF9800',
|
214 |
+
f"500 - {status_codes.get(500)}": '#F44336'
|
215 |
+
}
|
216 |
+
)
|
217 |
+
|
218 |
+
fig.update_layout(
|
219 |
+
height=300,
|
220 |
+
margin=dict(l=20, r=20, t=30, b=20),
|
221 |
+
xaxis_title="Probability",
|
222 |
+
yaxis_title="",
|
223 |
+
xaxis=dict(tickformat=".0%")
|
224 |
+
)
|
225 |
+
|
226 |
+
st.plotly_chart(fig, use_container_width=True)
|
227 |
+
st.markdown("</div>", unsafe_allow_html=True)
|
228 |
+
|
229 |
+
# Parameters influence section
|
230 |
+
st.markdown("<div class='card'>", unsafe_allow_html=True)
|
231 |
+
st.subheader("๐ Feature Importance")
|
232 |
+
st.write("How different parameters influence the prediction:")
|
233 |
+
|
234 |
+
# Mock feature importance for demonstration
|
235 |
+
# In a real app, you'd use model-specific methods to calculate this
|
236 |
+
feature_importance = {
|
237 |
+
'API Service': 0.25,
|
238 |
+
'Environment': 0.15,
|
239 |
+
'Latency': 0.20,
|
240 |
+
'Bytes Transferred': 0.10,
|
241 |
+
'Time of Day': 0.05,
|
242 |
+
'CPU Cost': 0.15,
|
243 |
+
'Memory Usage': 0.10
|
244 |
+
}
|
245 |
+
|
246 |
+
# Create a horizontal bar chart for feature importance
|
247 |
+
importance_df = pd.DataFrame({
|
248 |
+
'Feature': list(feature_importance.keys()),
|
249 |
+
'Importance': list(feature_importance.values())
|
250 |
+
}).sort_values('Importance', ascending=False)
|
251 |
+
|
252 |
+
fig_importance = px.bar(
|
253 |
+
importance_df,
|
254 |
+
x='Importance',
|
255 |
+
y='Feature',
|
256 |
+
orientation='h',
|
257 |
+
color='Importance',
|
258 |
+
color_continuous_scale='Blues'
|
259 |
+
)
|
260 |
+
|
261 |
+
fig_importance.update_layout(
|
262 |
+
height=350,
|
263 |
+
margin=dict(l=20, r=20, t=20, b=20),
|
264 |
+
yaxis_title="",
|
265 |
+
coloraxis_showscale=False
|
266 |
+
)
|
267 |
+
|
268 |
+
st.plotly_chart(fig_importance, use_container_width=True)
|
269 |
+
st.markdown("</div>", unsafe_allow_html=True)
|
270 |
+
|
271 |
+
# Footer with information
|
272 |
+
st.markdown("---")
|
273 |
+
st.markdown(
|
274 |
"๐ก **About**: This tool uses machine learning to predict API response status codes based on request parameters and system metrics.")
|