Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+st.set_page_config(page_title="LLM API Budget Dashboard", layout="wide")
+
+# Title and description
+st.title("LLM API Budget Dashboard")
+st.markdown("This dashboard helps you budget your API calls to various LLMs based on input and output tokens.")
+
+# Define LLM models and their costs
+llm_data = {
+    "GPT-4o": {"input_cost_per_m": 2.50, "output_cost_per_m": 10.00},
+    "Claude 3.7 Sonnet": {"input_cost_per_m": 3.00, "output_cost_per_m": 15.00},
+    "Gemini Flash 1.5-8b": {"input_cost_per_m": 0.038, "output_cost_per_m": 0.15},
+    "o3-mini": {"input_cost_per_m": 1.10, "output_cost_per_m": 4.40}
+}
+
+# Convert the LLM data to a DataFrame for displaying in a table
+llm_df = pd.DataFrame([
+    {
+        "Model": model, 
+        "Input Cost ($/M tokens)": data["input_cost_per_m"], 
+        "Output Cost ($/M tokens)": data["output_cost_per_m"]
+    } 
+    for model, data in llm_data.items()
+])
+
+# Display LLM cost info
+st.subheader("LLM Cost Information")
+st.dataframe(llm_df, use_container_width=True)
+
+# Create sidebar for inputs
+st.sidebar.header("Configuration")
+
+# Token input section
+st.sidebar.subheader("Token Settings")
+input_tokens = st.sidebar.number_input("Input Tokens", min_value=1, value=1000, step=100)
+output_tokens = st.sidebar.number_input("Output Tokens", min_value=1, value=500, step=100)
+
+# LLM selection
+st.sidebar.subheader("Select LLMs")
+selected_llms = st.sidebar.multiselect("Choose LLMs", options=list(llm_data.keys()), default=list(llm_data.keys()))
+
+# Run count settings
+st.sidebar.subheader("Run Count Settings")
+uniform_runs = st.sidebar.checkbox("Run all LLMs the same number of times", value=True)
+
+if uniform_runs:
+    uniform_run_count = st.sidebar.number_input("Number of runs for all LLMs", min_value=1, value=1, step=1)
+    run_counts = {llm: uniform_run_count for llm in selected_llms}
+else:
+    st.sidebar.write("Set individual run counts for each LLM:")
+    run_counts = {}
+    for llm in selected_llms:
+        run_counts[llm] = st.sidebar.number_input(f"Runs for {llm}", min_value=1, value=1, step=1)
+
+# Stability test settings
+st.sidebar.subheader("Stability Test Settings")
+stability_test = st.sidebar.checkbox("Enable stability testing", value=False)
+
+if stability_test:
+    st.sidebar.write("Set stability iterations for selected LLMs:")
+    stability_iterations = {}
+    for llm in selected_llms:
+        stability_enabled = st.sidebar.checkbox(f"Test stability for {llm}", value=False)
+        if stability_enabled:
+            iterations = st.sidebar.number_input(f"Iterations for {llm}", min_value=2, value=10, step=1)
+            stability_iterations[llm] = iterations
+else:
+    stability_iterations = {}
+
+# Calculate costs
+results = []
+
+for llm in selected_llms:
+    base_runs = run_counts[llm]
+    stability_runs = stability_iterations.get(llm, 0)
+    total_runs = base_runs * (1 if stability_runs == 0 else stability_runs)
+    
+    total_input_tokens = input_tokens * total_runs
+    total_output_tokens = output_tokens * total_runs
+    
+    input_cost = (total_input_tokens / 1_000_000) * llm_data[llm]["input_cost_per_m"]
+    output_cost = (total_output_tokens / 1_000_000) * llm_data[llm]["output_cost_per_m"]
+    total_cost = input_cost + output_cost
+    
+    results.append({
+        "Model": llm,
+        "Base Runs": base_runs,
+        "Stability Test Iterations": stability_iterations.get(llm, 0),
+        "Total Runs": total_runs,
+        "Total Input Tokens": total_input_tokens,
+        "Total Output Tokens": total_output_tokens,
+        "Input Cost ($)": input_cost,
+        "Output Cost ($)": output_cost,
+        "Total Cost ($)": total_cost
+    })
+
+# Create DataFrame from results
+results_df = pd.DataFrame(results)
+
+# Main content
+st.header("Cost Summary")
+st.dataframe(results_df, use_container_width=True)
+
+# Calculate overall totals
+total_input_cost = results_df["Input Cost ($)"].sum()
+total_output_cost = results_df["Output Cost ($)"].sum()
+total_cost = results_df["Total Cost ($)"].sum()
+
+# Display totals
+col1, col2, col3 = st.columns(3)
+col1.metric("Total Input Cost", f"${total_input_cost:.2f}")
+col2.metric("Total Output Cost", f"${total_output_cost:.2f}")
+col3.metric("Total API Cost", f"${total_cost:.2f}")
+
+# Data visualization
+st.header("Cost Visualization")
+
+# Cost breakdown by model
+fig1, ax1 = plt.subplots(figsize=(10, 6))
+models = results_df["Model"]
+input_costs = results_df["Input Cost ($)"]
+output_costs = results_df["Output Cost ($)"]
+
+x = np.arange(len(models))
+width = 0.35
+
+ax1.bar(x - width/2, input_costs, width, label='Input Cost')
+ax1.bar(x + width/2, output_costs, width, label='Output Cost')
+
+ax1.set_ylabel('Cost ($)')
+ax1.set_title('Cost Breakdown by Model')
+ax1.set_xticks(x)
+ax1.set_xticklabels(models, rotation=45, ha='right')
+ax1.legend()
+
+fig1.tight_layout()
+st.pyplot(fig1)
+
+# Percentage of total cost by model
+fig2, ax2 = plt.subplots(figsize=(8, 8))
+ax2.pie(results_df["Total Cost ($)"], labels=results_df["Model"], autopct='%1.1f%%', startangle=90)
+ax2.axis('equal')
+ax2.set_title('Percentage of Total Cost by Model')
+st.pyplot(fig2)
+
+# Export options
+st.header("Export Options")
+csv = results_df.to_csv(index=False).encode('utf-8')
+st.download_button(
+    label="Download Results as CSV",
+    data=csv,
+    file_name='llm_budget_results.csv',
+    mime='text/csv',
+)
+
+# Footer
+st.markdown("---")
+st.markdown("*Note: All costs are estimates based on the provided rates. Actual API costs may vary.*")